What technology do you find most beneficial to use in your work or school setting? Least beneficial? Why do you find this tool useful or not? Then, using your imagination, look to the future and think about how this tool could be enhanced even further. Describe your dream technology, with consideration for patient care and safety.
Don't use plagiarized sources. Get Your Custom Essay on
NURSING INFORMATICS
AND THE FOUNDATION OF KNOWLEDGE
THIRD EDITION
The Pedagogy
Nursing Informatics and the Foundation of Knowledge, Third Edition drives comprehension through a variety of strategies geared
toward meeting the learning needs of students, while also generating enthusiasm about the topic. This interactive approach addresses
diverse learning styles, making this the ideal text to ensure mastery of key concepts. The pedagogical aids that appear in most chapters
include the following:
NURSING INFORMATICS
AND THE FOUNDATION OF KNOWLEDGE
THIRD EDITION
Dee McGonigle, PhD, RN, CNE, FAAN, ANEF
Chair, Virtual Learning Environments and Professor, Graduate Programs
Chamberlain College of Nursing
Member, Informatics and Technology Expert Panel (ITEP)
American Academy of Nursing
Member, Serious Gaming and Virtual Environments Special Interest Group for the Society for Simulation in Healthcare (SSH)
Kathleen Mastrian, PhD, RN
Associate Professor and Program Coordinator for Nursing
Pennsylvania State University, Shenango
Sr. Managing Editor, Online Journal of Nursing Informatics (OJNI)
Special Acknowledgments
We want to express our sincere appreciation to the staff at Jones & Bartlett Learning, especially Amanda, Becky, and Keith, for their
continued encouragement, assistance, and support during the writing process and publication of our book.
Contents
Preface
Acknowledgments
Authors’ Note
Contributors
SECTION I: BUILDING BLOCKS OF NURSING INFORMATICS
1 Nursing Science and the Foundation of Knowledge
Kathleen Mastrian and Dee McGonigle
Introduction
Quality and Safety Education for Nurses
Summary
References
2 Introduction to Information, Information Science, and Information Systems
Dee McGonigle and Kathleen Mastrian
Introduction
Information
Information Science
Information Processing
Information Science and the Foundation of Knowledge
Introduction to Information Systems
Information Systems
Summary
References
3 Computer Science and the Foundation of Knowledge Model
June Kaminski
Introduction
The Computer as a Tool for Managing Information and Generating Knowledge
Components
What Is the Relationship of Computer Science to Knowledge?
How Does the Computer Support Collaboration and Information Exchange?
What Is the Human–Technology Interface?
Looking to the Future
Summary
Working Wisdom
Application Scenario
Internet and Software Resources
References
4 Introduction to Cognitive Science and Cognitive Informatics
Dee McGonigle and Kathleen Mastrian
Introduction
Cognitive Science
Sources of Knowledge
Nature of Knowledge
How Knowledge and Wisdom Are Used in Decision Making
Cognitive Informatics
CI and Nursing Practice
What Is AI?
Summary
References
5 Ethical Applications of Informatics
Kathleen Mastrian, Dee McGonigle, and Nedra Farcus
Introduction
Ethics
Bioethics
Ethical Issues and Social Media
Ethical Dilemmas and Morals
Ethical Decision Making
Theoretical Approaches to Healthcare Ethics
Applying Ethics to Informatics
Case Analysis Demonstration
New Frontiers in Ethical Issues
Summary
References
SECTION II: PERSPECTIVES ON NURSING INFORMATICS
6 Overview of Nursing Informatics
Ramona Nelson and Nancy Staggers
Introduction
Metastructures, Concepts, and Tools of NI
The Future of NI
Summary
References
7 Informatics Roles and the Knowledge Work of Nursing
Julie A. Kenney and Ida Androwich
Introduction
The Nurse as a Knowledge Worker
The Knowledge Needs and Competencies of Nurses
What Is Nursing Informatics Specialty Practice?
The Future of Nursing Informatics
Summary
References
8 Information and Knowledge Needs of Nurses in the 21st Century
Lynn M. Nagle, Nicholas Hardiker, Kathleen Mastrian, and Dee McGonigle
Introduction
Definition and Goal of Informatics
Health Information Technologies Impacting Nursing
Nurses Creating and Deriving New Knowledge
Generating Nursing Knowledge
Challenges in Getting There
The Future
Summary
References
9 Legislative Aspects of Nursing Informatics: HITECH and HIPAA
Kathleen M. Gialanella, Kathleen Mastrian, and Dee McGonigle
Introduction
Overview of the HITECH Act
How a National HIT Infrastructure Is Being Developed
How the HITECH Act Changed HIPAA
Implications for Nursing Practice
Summary
References
SECTION III: NURSING INFORMATICS ADMINISTRATIVE APPLICATIONS: PRECARE AND CARE SUPPORT
10 Systems Development Life Cycle: Nursing Informatics and Organizational Decision Making
Dee McGonigle and Kathleen Mastrian
Introduction
Waterfall Model
Rapid Prototyping or Rapid Application Development
Object-Oriented Systems Development
Dynamic System Development Method
Computer-Aided Software Engineering Tools
Open Source Software and Free/Open Source Software
Interoperability
Summary
References
11 Administrative Information Systems
Marianela Zytkowski, Susan Paschke, Dee McGonigle, and Kathleen Mastrian
Introduction
Types of Healthcare Organization Information Systems
Communication Systems
Core Business Systems
Order Entry Systems
Patient Care Support Systems
Department Collaboration and Exchange of Knowledge and Information
Summary
References
12 The Human–Technology Interface
Judith A. Effken, Dee McGonigle, and Kathleen Mastrian
Introduction
The Human–Technology Interface
The Human–Technology Interface Problem
Improving the Human–Technology Interface
A Framework for Evaluation
Future of the Human–Technology Interface
Summary
References
13 Electronic Security
Lisa Reeves Bertin, Dee McGonigle, and Kathleen Mastrian
Introduction
Securing Network Information
Authentication of Users
Threats to Security
Security Tools
Off-Site Use of Portable Devices
Summary
References
14 Nursing Informatics: Improving Workflow and Meaningful Use
Denise Hammel-Jones, Dee McGonigle, and Kathleen Mastrian
Introduction
Workflow Analysis Purpose
Workflow and Technology
Workflow Analysis and Informatics Practice
Informatics as a Change Agent
Measuring the Results
Future Directions
Summary
References
SECTION IV: NURSING INFORMATICS PRACTICE APPLICATIONS: CARE DELIVERY
15 The Electronic Health Record and Clinical Informatics
Emily B. Barey, Kathleen Mastrian, and Dee McGonigle
Introduction
Setting the Stage
Components of Electronic Health Records
Advantages of Electronic Health Records
Ownership of Electronic Health Records
Flexibility and Expandability
The Future
Summary
References
16 Informatics Tools to Promote Patient Safety and Clinical Outcomes
Kathleen Mastrian and Dee McGonigle
Introduction
What Is a Culture of Safety?
Strategies for Developing a Safety Culture
Informatics Technologies for Patient Safety
Role of the Nurse Informaticist
Summary
References
17 Supporting Consumer Information and Education Needs
Kathleen Mastrian and Dee McGonigle
Introduction
Consumer Demand for Information
Health Literacy and Health Initiatives
Healthcare Organization Approaches to Education
Promoting Health Literacy in School-Aged Children
Supporting Use of the Internet for Health Education
Future Directions
Summary
References
18 Using Informatics to Promote Community/Population Health
Margaret Ross Kraft, Ida Androwich, Kathleen Mastrian, and Dee McGonigle
Introduction
Core Public Health Functions
Community Health Risk Assessment: Tools for Acquiring Knowledge
Processing Knowledge and Information to Support Epidemiology and Monitoring Disease Outbreaks
Applying Knowledge to Health Disaster Planning and Preparation
Informatics Tools to Support Communication and Dissemination
Using Feedback to Improve Responses and Promote Readiness
Summary
References
19 Telenursing and Remote Access Telehealth
Original contribution by Audrey Kinsella, Kathleen Albright, Sheldon Prial, and Schuyler F. Hoss; revised by Kathleen Mastrian
and Dee McGonigle
Introduction
History of Telehealth
Nursing Aspects of Telehealth
Driving Forces for Telehealth
Telehealth Care
Telenursing
Telehealth Patient Populations
Tools of Home Telehealth
Home Telehealth Software
Home Telehealth Practice and Protocols
Legal, Ethical, and Regulatory Issues
A Day in the Life of a Home Telenurse
The Patient’s Role in Telehealth
Telehealth Research
The Foundation of Knowledge Model and Home Telehealth
Parting Thoughts for the Future and a View Toward What the Future Holds
Summary
References
SECTION V: EDUCATION APPLICATIONS OF NURSING INFORMATICS
20 Nursing Informatics and Nursing Education
Heather E. McKinney, Sylvia DeSantis, Dee McGonigle, and Kathleen Mastrian
Introduction: Nursing Education and the Foundation of Knowledge Model
Knowledge Acquisition and Sharing
Hardware and Software Considerations
Delivery Modalities
Technology Tools
Internet Tools: Webcasts, Searching, Instant Messaging, Chats and Online Discussions, Electronic Mailing Lists, and Portals
Promoting Active and Collaborative Learning
Knowledge Assessment Methods
Knowledge Dissemination and Sharing
The Future
Exploring Information Fair Use and Copyright Restrictions
Summary
References
21 Simulation in Nursing Informatics Education
Nickolaus Miehl
Introduction
Nursing Informatics Competencies in Nursing Education
A Case for Simulation
Incorporating EHRs into the Learning Environment
Challenges and Opportunities
What Does the Future Hold?
Summary
References
22 Games, Simulations, and Virtual Worlds for Educators
Brett Bixler
Introduction
Case Scenario
Educational Games
Educational Simulations
Virtual Worlds
Choosing Among Educational Games, Simulations, and Virtual Worlds
The Future of Games, Virtual Worlds, and Simulations
Summary
References
SECTION VI: NURSING INFORMATICS: RESEARCH APPLICATIONS
23 Research: Data Collection, Processing, and Analysis
Heather E. McKinney, Sylvia DeSantis, Kathleen Mastrian, and Dee McGonigle
Introduction: Nursing Research and the Foundation of Knowledge Model
Knowledge Generation Through Nursing Research
Acquiring Previously Gained Knowledge Through Internet and Library Holdings
Fair Use of Information and Sharing
Informatics Tools for Collecting Data and Storage of Information
Tools for Processing Data and Data Analysis
The Future
Summary
References
24 Data Mining as a Research Tool
Dee McGonigle and Kathleen Mastrian
Introduction: Big Data, Data Mining, and Knowledge Discovery
KDD and Research
Data Mining Concepts
Data Mining Techniques
Data Mining Models
Benefits of KDD
Ethics of Data Mining
Summary
References
25 Translational Research: Generating Evidence for Practice
Jennifer Bredemeyer and Ida Androwich
Introduction
Clarification of Terms
History of Evidence-Based Practice
Evidence
Bridging the Gap Between Research and Practice
Barriers to and Facilitators of Evidence-Based Practice
The Role of Informatics
Developing EBP Guidelines
Meta-Analysis and Generation of Knowledge
The Future
Summary
References
26 Bioinformatics, Biomedical Informatics, and Computational Biology
Dee McGonigle and Kathleen Mastrian
Introduction
Bioinformatics, Biomedical Informatics, and Computational Biology Defined
Why Are Bioinformatics and Biomedical Informatics So Important?
What Does the Future Hold?
Summary
References
SECTION VII: IMAGINING THE FUTURE OF NURSING INFORMATICS
27 The Art of Caring in Technology-Laden Environments
Kathleen Mastrian and Dee McGonigle
Introduction
Caring Theories
Presence
Strategies for Enhancing Caring Presence
Reflective Practice
Summary
References
28 Emerging Technologies and the Generation of Knowledge
Peter J. Murray, W. Scott Erdley, Dee McGonigle, and Kathleen Mastrian
Introduction
Looking Back from the Future
Historical Overview
Some Technologies of Today
Some Views of What Will Affect the Future
Some Emerging Technologies and Other Issues That Will Impact Nursing and Health Care 491 Summary
References
29 Nursing Informatics and the Foundation of Knowledge
Dee McGonigle and Kathleen Mastrian
Introduction
Foundation of Knowledge Revisited
Knowledge Use in Practice
Summary
References
Abbreviations
Glossary
Index
Preface
The idea for this text originated with the development of nursing informatics (NI) classes, the publication of articles related to
technology-based education, and the creation of the Online Journal of Nursing Informatics (OJNI), which Dee McGonigle cofounded.
Like most nurse informaticists, we fell into the specialty; our love affair with technology and gadgets and our willingness to be the first
to try new things helped to hook us into the specialty of informatics. The rapid evolution of technology and its transformation of the
ways of nursing prompted us to try to capture the essence of NI in a text.
As we were developing the first edition, we realized that we could not possibly know all there is to know about informatics and the
way in which it supports nursing practice, education, administration, and research. We also knew that our faculty roles constrained our
opportunities for exposure to changes in this rapidly evolving field. Therefore, we developed a tentative outline and a working model
of the theoretical framework for the text and invited participation from informatics experts and specialists around the world. We were
pleased with the enthusiastic responses we received from some of those invited contributors and a few volunteers who heard about the
text and asked to participate in their particular area of expertise.
In the second edition, we invited the original contributors to revise and update their chapters. Not everyone chose to participate in
the second edition, so we revised several of the chapters using the original work as a springboard. The revisions to the text were guided
by the contributors’ growing informatics expertise and the reviews provided by textbook adopters. In the revisions, we sought to do the
following:
• Expand the audience focus to include nursing students from BS through DNP programs as well as nurses thrust into
informatics roles in clinical agencies.
• Include, whenever possible, an attention-grabbing case scenario as an introduction or an illustrative case scenario
demonstrating why the topic is important.
• Include important research findings related to the topic. Many chapters have research briefs presented in text boxes to
encourage the reader to access current research.
• Focus on cutting-edge innovations, meaningful use, and patient safety as appropriate to each topic.
• Include a paragraph describing what the future holds for each topic.
New chapters that were added to the second edition included those focusing on technology and patient safety, system development life
cycle, workflow analysis, gaming, simulation, and bioinformatics.
In this, the third edition, we reviewed and updated all of the chapters, reordered some chapters for better content flow, eliminated
duplicated content, split the education and research content into two sections, integrated social media content, and added two new
chapters: Data Mining as a Research Tool and The Art of Caring in Technology-Laden Environments.
We believe that this text provides a comprehensive elucidation of this exciting field. Its theoretical underpinning is the Foundation
of Knowledge model. This model is introduced in its entirety in the first chapter (Nursing Science and the Foundation of Knowledge),
which discusses nursing science and its relationship to NI. We believe that humans are organic information systems that are constantly
acquiring, processing, and generating information or knowledge in both their professional and personal lives. It is their high degree of
knowledge that characterizes humans as extremely intelligent, organic machines. Individuals have the ability to manage knowledge—
an ability that is learned and honed from birth. We make our way through life interacting with our environment and being inundated
with information and knowledge. We experience our environment and learn by acquiring, processing, generating, and disseminating
knowledge. As we interact in our environment, we acquire knowledge that we must process. This processing effort causes us to
redefine and restructure our knowledge base and generate new knowledge. We then share (disseminate) this new knowledge and
receive feedback from others. The dissemination and feedback initiate this cycle of knowledge over again, as we acquire, process,
generate, and disseminate the knowledge gained from sharing and reexploring our own knowledge base. As others respond to our
knowledge dissemination and we acquire new knowledge, we engage in rethinking and reflecting on our knowledge, processing,
generating, and then disseminating anew.
The purpose of this text is to provide a set of practical and powerful tools to ensure that the reader gains an understanding of NI
and moves from information through knowledge to wisdom. Defining the demands of nurses and providing tools to help them survive
and succeed in the Knowledge Era remains a major challenge. Exposing nursing students and nurses to the principles and tools used in
NI helps to prepare them to meet the challenge of practicing nursing in the Knowledge Era while striving to improve patient care at all
levels.
The text provides a comprehensive framework that embraces knowledge so that readers can develop their knowledge repositories
and the wisdom necessary to act on and apply that knowledge. The text is divided into seven sections.
• The Building Blocks of Nursing Informatics section covers the building blocks of NI: nursing science, information science,
computer science, cognitive science, and the ethical management of information.
• The Perspectives on Nursing Informatics section provides readers with a look at various viewpoints on NI and NI practice as
described by experts in the field.
• The Nursing Informatics Administrative Applications: Precare and Care Support section covers important functions of
administrative applications of NI.
• The Nursing Informatics Practice Applications: Care Delivery section covers healthcare delivery applications including
electronic health records (EHRs), clinical information systems, telehealth, patient safety, patient and community education,
and care management.
• The Education Applications of Nursing Informatics section presents subject matter on how informatics supports nursing
education.
• The Nursing Informatics: Research Applications section covers informatics tools to support nursing research, including data
mining and bioinformatics.
• The Imagining the Future of Nursing Informatics section focuses on the future of NI, emphasizes the need to preserve caring
functions in technology-laden environments, and summarizes the relationship of informatics to the Foundation of Knowledge
model and organizational knowledge management.
The introduction to each section explains the relationship between the content of that section and the Foundation of Knowledge
model. This text places the material within the context of knowledge acquisition, processing, generation, and dissemination. It serves
both nursing students (BS to DNP/PhD) and professionals who need to understand, use, and evaluate NI knowledge. As nursing
professors, our major responsibility is to prepare the practitioners and leaders in the field. Because NI permeates the entire scope of
nursing (practice, administration, education, and research), nursing education curricula must include NI. Our primary objective is to
develop the most comprehensive and user-friendly NI text on the market to prepare nurses for current and future practice challenges.
In particular, this text provides a solid groundwork from which to integrate NI into practice, education, administration, and research.
Goals of this text are as follows:
• Impart core NI principles that should be familiar to every nurse and nursing student
• Help the reader understand knowledge and how it is acquired, processed, generated, and disseminated
• Explore the changing role of NI professionals
• Demonstrate the value of the NI discipline as an attractive field of specialization
Meeting these goals will help nurses and nursing students understand and use fundamental NI principles so that they efficiently and
effectively function as current and future nursing professionals. The overall vision, framework, and pedagogy of this text offer benefits
to readers by highlighting established principles while drawing out new ones that continue to emerge as nursing and technology
evolve.
Acknowledgments
We are deeply grateful to the contributors who provided this text with a richness and diversity of content that we could not have
captured alone. Joan Humphrey provided social media content integrated throughout the text. We especially wish to acknowledge the
superior work of Alicia Mastrian, graphic designer of the Foundation of Knowledge model, which serves as the theoretical framework
on which this text is anchored. We could never have completed this project without the dedicated and patient efforts of the Jones &
Bartlett Learning staff, especially Amanda Martin and Becky Myrick. Both fielded our questions and concerns in a very professional
and respectful manner.
Dee acknowledges the undying love, support, patience, and continued encouragement of her best friend and husband, Craig, and
her son, Craig, who has also made her so very proud. She sincerely thanks her cousins Camille, Glenn, Mary Jane, and Sonny, and her
dear friends for their support and encouragement, especially Renee.
Kathy acknowledges the loving support of her family: husband Chip; children Ben and Alicia; sisters Carol and Sue; and parents
Bob and Rosalie Garver. Kathy also acknowledges those friends who understand the importance of validation, especially Katie,
Bobbie, Kathy, Anne, and Barbara.
Authors’ Note
This text provides an overview of nursing informatics from the perspective of diverse experts in the field, with a focus on nursing
informatics and the Foundation of Knowledge model. We want our readers and students to focus on the relationship of knowledge to
informatics and to embrace and maintain the caring functions of nursing—messages all too often lost in the romance with technology.
We hope you enjoy the text!
Contributors
Ida Androwich, PhD, RN, BC, FAAN
Loyola University Chicago
School of Nursing
Maywood, IL
Emily Barey, MSN, RN
Director of Nursing Informatics
Epic Systems Corporation
Madison, WI
Lisa Reeves Bertin, BS, EMBA
Pennsylvania State University
Sharon, PA
Brett Bixler, PhD
Pennsylvania State University
University Park, PA
Jennifer Bredemeyer, RN
Loyola University Chicago
School of Nursing
Skokie, IL
Steven Brewer, PhD
Assistant Professor, Administration of Justice
Pennsylvania State University
Sharon, PA
Sylvia M. DeSantis, MA
Pennsylvania State University
University Park, PA
Eric R. Doerfler, PhD, NP
Pennsylvania State University
School of Nursing
Middletown, PA
Judith Effken, PhD, RN, FACMI
University of Arizona
College of Nursing
Tucson, AZ
William Scott Erdley, DNS, RN
Niagara University
Niagara University, NY
Nedra Farcus, MSN, RN
Pennsylvania State University, Altoona
Altoona, PA
Kathleen M. Gialanella, JD, RN, LLM
Law Offices
Westfield, NJ
Associate Adjunct Professor
Teachers College, Columbia University
New York, NY
Adjunct Professor
Seton Hall University, College of Nursing & School of Law
South Orange & Newark, NJ
Denise Hammel-Jones, MSN, RN-BC, CLSSBB
Greencastle Associates Consulting
Malvern, PA
Nicholas Hardiker, PhD, RN
Senior Research Fellow
University of Salford
School of Nursing & Midwifery
Salford, UK
Glenn Johnson, MLS
Pennsylvania State University
University Park, PA
June Kaminski, MSN, RN
Kwantlen University College
Surrey, British Columbia, Canada
Julie Kenney, MSN, RNC-OB
Clinical Analyst Advocate Health Care
Oak Brook, IL
Margaret Ross Kraft, PhD, RN
Loyola University Chicago
School of Nursing
Maywood, IL
Wendy L. Mahan, PhD, CRC, LPC
Pennsylvania State University
University Park, PA
Heather McKinney, PhD
Pennsylvania State University
University Park, PA
Nickolaus Miehl, MSN, RN
Pennsylvania State University
Erie, PA
Peter J. Murray, PhD, RN, FBCS
Coachman’s Cottage
Nocton, Lincoln, UK
Lynn M. Nagle, PhD, RN
Assistant Professor
University of Toronto
Toronto, Ontario, Canada
Ramona Nelson, PhD, RN-BC, FAAN, ANEF
Professor Emerita, Slippery Rock University
President, Ramona Nelson Consulting
Pittsburgh, PA
Nancy Staggers, PhD, RN, FAAN
Professor, Informatics
University of Maryland
Baltimore, MD
Jeff Swain
Instructional Designer
Pennsylvania State University
University Park, PA
Denise D. Tyler, MSN/MBA, RN-BC
Implementation Specialist
Healthcare Provider, Consulting
ACS, a Xerox Company
Dearborn, MI
The Editors also acknowledge the work of the following first edition contributors (original contributions edited by McGonigle and
Mastrian for second edition):
Kathleen Albright, BA, RN
Strategic Account Manager at GE Healthcare
Philadelphia, PA
Schuyler F. Hoss, BA
Northwest Healthcare Management
Vancouver, WA
Audrey Kinsella, MA, MS
Information for Tomorrow
Telehealth Planning Services
Asheville, NC
Susan M. Paschke, MSN, RN
The Cleveland
Clinic Cleveland, OH
Sheldon Prial, RPH, BS Pharmacy
Sheldon Prial Consultance
Melbourne, FL
Jackie Ritzko
Pennsylvania State University
Hazelton, PA
Marianela Zytkowsi, MSN, RN
The Cleveland Clinic
Cleveland, OH
Section I
Building Blocks of Nursing Informatics
Chapter 1 Nursing Science and the Foundation of Knowledge
Chapter 2 Introduction to Information, Information Science, and Information Systems
Chapter 3 Computer Science and the Foundation of Knowledge Model
Chapter 4 Introduction to Cognitive Science and Cognitive Informatics
Chapter 5 Ethical Applications of Informatics
Nursing professionals are information-dependent knowledge workers. As health care continues to evolve in an increasingly
competitive information marketplace, professionals—that is, the knowledge workers—must be well prepared to make significant
contributions by harnessing appropriate and timely information. Nursing informatics (NI), a product of the scientific synthesis of
information in nursing, encompasses concepts from computer science, cognitive science, information science, and nursing science. NI
continues to evolve as more and more professionals access, use, and develop the information, computer, and cognitive sciences
necessary to advance nursing science for the betterment of patients and the profession. Regardless of their future roles in the healthcare
milieu, it is clear that nurses need to understand the ethical application of computer, information, and cognitive sciences to advance
nursing science.
To implement NI, one must view it from the perspective of both the current healthcare delivery system and specific, individual
organizational needs, while anticipating and creating future applications in both the healthcare system and the nursing profession.
Nursing professionals should be expected to discover opportunities to use NI, participate in the design of solutions, and be challenged
to identify, develop, evaluate, modify, and enhance applications to improve patient care. This text is designed to provide the reader
with the information and knowledge needed to meet this expectation.
Section I presents an overview of the building blocks of NI: nursing, information, computer, and cognitive sciences. Also included
in this section is a chapter on ethical applications of healthcare informatics. This section lays the foundation for the remainder of the
book.
The Nursing Science and the Foundation of Knowledge chapter describes nursing science and introduces the Foundation of
Knowledge model as the conceptual framework for the book. In this chapter, a clinical case scenario is used to illustrate the concepts
central to nursing science. A definition of nursing science is also derived from the American Nurses Association’s definition of
nursing. Nursing science is the ethical application of knowledge acquired through education, research, and practice to provide services
and interventions to patients to maintain, enhance, or restore their health, and to acquire, process, generate, and disseminate nursing
knowledge to advance the nursing profession. Information is a central concept and health care’s most valuable resource. Information
science and systems, together with computers, are constantly changing the way healthcare organizations conduct their business. This
will continue to evolve.
To prepare for these innovations, the reader must understand fundamental information and computer concepts, covered in the
Introduction to Information, Information Science, and Information Systems and Computer Science and the Foundation of Knowledge
Model chapters, respectively. Information science deals with the interchange (or flow) and scaffolding (or structure) of information
and involves the application of information tools for solutions to patient care and business problems in health care. To be able to use
and synthesize information effectively, an individual must be able to obtain, perceive, process, synthesize, comprehend, convey, and
manage the information. Computer science deals with understanding the development, design, structure, and relationship of computer
hardware and software. This science offers extremely valuable tools that, if used skillfully, can facilitate the acquisition and
manipulation of data and information by nurses, who can then synthesize these resources into an ever-evolving knowledge and wisdom
base. This not only facilitates professional development and the ability to apply evidence-based practice decisions within nursing care,
but, if the results are disseminated and shared, can also advance the profession’s knowledge base. The development of knowledge
tools, such as the automation of decision making and strides in artificial intelligence, has altered the understanding of knowledge and
its representation. The ability to structure knowledge electronically facilitates the ability to share knowledge structures and enhance
collective knowledge.
As discussed in the Introduction to Cognitive Science and Cognitive Informatics chapter, cognitive science deals with how the
human mind functions. This science encompasses how people think, understand, remember, synthesize, and access stored information
and knowledge. The nature of knowledge, including how it is developed, used, modified, and shared, provides the basis for continued
learning and intellectual growth.
The Ethical Applications of Informatics chapter focuses on ethical issues associated with managing private information with
technology and provides a framework for analyzing ethical issues and supporting ethical decision making.
The material within this book is placed within the context of the Foundation of Knowledge model (shown in Figure I-1 and
periodically throughout the book, but more fully introduced and explained in the Nursing Science and the Foundation of Knowledge
chapter). The Foundation of Knowledge model is used throughout the text to illustrate how knowledge is used to meet the needs of
healthcare delivery systems, organizations, patients, and nurses. It is through interaction with these building blocks—the theories,
architecture, and tools—that one acquires the bits and pieces of data necessary, processes these into information, and generates and
disseminates the resulting knowledge. Through this dynamic exchange, which includes feedback, individuals continue the interaction
and use of these sciences to input or acquire, process, and output or disseminate generated knowledge. Humans experience their
environment and learn by acquiring, processing, generating, and disseminating knowledge. When they then share (disseminate) this
new knowledge and receive feedback on the knowledge they have shared, the feedback initiates the cycle of knowledge all over again.
As individuals acquire, process, generate, and disseminate knowledge, they are motivated to share, rethink, and explore their own
knowledge base. This complex process is captured in the Foundation of Knowledge model. Throughout the chapters in the Building
Blocks of Nursing Informatics section, readers are challenged to think about how the model can help them to understand the ways in
which they acquire, process, generate, disseminate, and then receive feedback on their new knowledge of the building blocks of NI.
Figure I-1 Foundation of Knowledge Model
Source: Designed by Alicia Mastrian.
Chapter 1
Nursing Science and the Foundation of Knowledge
Kathleen Mastrian and Dee McGonigle
OBJECTIVES
1. Define nursing science and its relationship to various nursing roles and nursing informatics.
2. Introduce the Foundation of Knowledge model as the organizing conceptual framework for the text.
3. Explain the relationships among knowledge acquisition, knowledge processing, knowledge generation, knowledge dissemination, and wisdom.
Key Terms
Borrowed theory
Building blocks
Clinical databases
Clinical practice guidelines
Conceptual framework
Data
Data mining
Evidence
Feedback
Foundation of Knowledge model
Information
Knowledge
Knowledge acquisition
Knowledge dissemination
Knowledge generation
Knowledge processing
Knowledge worker
Nursing informatics
Nursing science
Nursing theory
Relational database
Transparent wisdom
Introduction
Nursing informatics is defined as the combination of nursing science, information science, and computer science. This chapter
focuses on nursing science as one of the building blocks of nursing informatics, although in this text the traditional definition of
nursing informatics is extended to include cognitive science as one of the building blocks. The Foundation of Knowledge model is
also introduced as the organizing conceptual framework of this text, and the model is tied to nursing science and the practice of
nursing informatics. To lay the groundwork for this discussion, consider the following patient scenario:
Tom H. is a registered nurse who works in a very busy metropolitan hospital emergency room. He has just admitted a 79-year-
old man whose wife brought him to the hospital because he is having trouble breathing. Tom immediately clips a pulse
oximeter to the patient’s finger and performs a very quick assessment of the patient’s other vital signs. He discovers a rapid
pulse rate and a decreased oxygen saturation level in addition to the rapid and labored breathing. Tom determines that the
patient is not in immediate danger and that he does not require intubation. Tom focuses his initial attention on easing the
patient’s labored breathing by elevating the head of the bed and initiating oxygen treatment; he then hooks the patient up to a
heart monitor. Tom continues to assess the patient’s breathing status as he performs a head-to-toe assessment of the patient
that leads to the nursing diagnoses and additional interventions necessary to provide comprehensive care to this patient.
Consider Tom’s actions and how and why he intervened as he did. Tom relied on the immediate data and information that he
acquired during his initial rapid assessment to deliver appropriate care to his patient. Tom also used technology (a pulse oximeter and a
heart monitor) to assist with and support the delivery of care. What is not immediately apparent, and some would argue is transparent
(done without conscious thought), is the fact that during the rapid assessment, Tom reached into his knowledge base of previous
learning and experiences to direct his care, so that he could act with transparent wisdom. He used both nursing theory and
borrowed theory to inform his practice. Tom certainly used nursing process theory, and he may have also used one of several other
nursing theories, such as Rogers’s science of unitary human beings, Orem’s theory of self-care deficit, or Roy’s adaptation theory. In
addition, Tom may have applied his knowledge from some of the basic sciences, such as anatomy, physiology, psychology, and
chemistry, as he determined the patient’s immediate needs. Information from Maslow’s hierarchy of needs, Lazarus’s transaction
model of stress and coping, and the health belief model may have also helped Tom practice professional nursing. He gathered data, and
then analyzed and interpreted those data to form a conclusion—the essence of science. Tom has illustrated the practical aspects of
nursing science.
The American Nurses Association (2003) defines nursing in this way: “Nursing is the protection, promotion, and optimization of
health and abilities, prevention of illness and injury, alleviation of suffering through the diagnosis and treatment of human response,
and advocacy in the care of individuals, families, communities, and populations” (p. 6). Thus the focus of nursing is on human
responses to actual or potential health problems and advocacy for various clients. These human responses are varied and may change
over time in a single case. Nurses must possess the technical skills to manage equipment and perform procedures, the interpersonal
skills to interact appropriately with people, and the cognitive skills to observe, recognize, and collect data; analyze and interpret data;
and reach a reasonable conclusion that forms the basis of a decision. At the heart of all of these skills lies the management of data and
information. This definition of nursing science focuses on the ethical application of knowledge acquired through education, research,
and practice to provide services and interventions to patients to maintain, enhance, or restore their health and to acquire, process,
generate, and disseminate nursing knowledge to advance the nursing profession.
Nursing is an information-intensive profession. The steps of using information, applying knowledge to a problem, and acting with
wisdom form the basis of nursing practice science. Information is composed of data that were processed using knowledge. For
information to be valuable, it must be accessible, accurate, timely, complete, cost-effective, flexible, reliable, relevant, simple,
verifiable, and secure. Knowledge is the awareness and understanding of a set of information and ways that information can be made
useful to support a specific task or arrive at a decision. In the case scenario, Tom used accessible, accurate, timely, relevant, and
verifiable data and information. He compared that data and information to his knowledge base and previous experiences to determine
which data and information were relevant to the current case. By applying his previous knowledge to data, he converted those data into
information, and information into new knowledge—that is, an understanding of which nursing interventions were appropriate in this
case. Thus information is data made functional through the application of knowledge.
Humans acquire data and information in bits and pieces and then transform the information into knowledge. The information-
processing functions of the brain are frequently compared to those of a computer, and vice versa (an idea discussed further in the
Introduction to Cognitive Science and Cognitive Informatics chapter). Humans can be thought of as organic information systems that
are constantly acquiring, processing, and generating information or knowledge in their professional and personal lives. They have an
amazing ability to manage knowledge. This ability is learned and honed from birth as individuals make their way through life
interacting with the environment and being inundated with data and information. Each person experiences the environment and learns
by acquiring, processing, generating, and disseminating knowledge.
Tom, for example, acquired knowledge in his basic nursing education program and continues to build his foundation of knowledge
by engaging in such activities as reading nursing research and theory articles, attending continuing education programs, consulting
with expert colleagues, and using clinical databases and clinical practice guidelines. As he interacts in the environment, he acquires
knowledge that must be processed. This processing effort causes him to redefine and restructure his knowledge base and generate new
knowledge. Tom can then share (disseminate) this new knowledge with colleagues, and he may receive feedback on the knowledge
that he shares. This dissemination and feedback builds the knowledge foundation anew as Tom acquires, processes, generates, and
disseminates new knowledge as a result of his interactions. As others respond to his knowledge dissemination and he acquires yet
more knowledge, he is engaged to rethink, reflect on, and reexplore his knowledge acquisition, leading to further processing,
generating, and then disseminating knowledge. This ongoing process is captured in the Foundation of Knowledge model, which is
used as an organizing framework for this text.
At its base, the model contains bits, bytes (computer terms for chunks of information), data, and information in a random
representation (Figure 1-1). Growing out of the base are separate cones of light that expand as they reflect upward; these cones
represent knowledge acquisition, knowledge generation, and knowledge dissemination. At the intersection of the cones and forming a
new cone is knowledge processing. Encircling and cutting through the knowledge cones is feedback that acts on and may transform
any or all aspects of knowledge represented by the cones. One should imagine the model as a dynamic figure in which the cones of
light and the feedback rotate and interact rather than remain static. Knowledge acquisition, knowledge generation, knowledge
dissemination, knowledge processing, and feedback are constantly evolving for nurse scientists. The transparent effect of the cones is
deliberate and is intended to suggest that as knowledge grows and expands its use becomes more transparent—a person uses this
knowledge during practice without even being consciously aware of which aspect of knowledge is being used at any given moment.
Experienced nurses, thinking back to their novice years, may recall feeling like their head was filled with bits of data and
information that did not form any type of cohesive whole. As the model depicts, the processing of knowledge begins a bit later
(imagine a timeline applied vertically) with early experiences on the bottom and expertise growing as the processing of knowledge
ensues. Early on in nurses’ education, conscious attention is focused mainly on knowledge acquisition, and they depend on their
instructors and others to process, generate, and disseminate knowledge. As nurses become more comfortable with the science of
nursing, they begin to take over some of the other Foundation of Knowledge functions. However, to keep up with the explosion of
information in nursing and health care, they must continue to rely on the knowledge generation of nursing theorists and researchers
and the dissemination of their work. In this sense, nurses are committed to lifelong learning and the use of knowledge in the practice of
nursing science.
Figure 1-1 Foundation of Knowledge Model
Source: Designed by Alicia Mastrian.
The Foundation of Knowledge model permeates this text, reflecting the understanding that knowledge is a powerful tool and that
nurses focus on information as a key building block of knowledge. The application of the model is described in each section of the text
to help the reader understand and appreciate the foundation of knowledge in nursing science and see how it applies to nursing
informatics. All of the various nursing roles (practice, administration, education, research, and informatics) involve the science of
nursing. Nurses are knowledge workers, working with information and generating information and knowledge as a product. They are
knowledge acquirers, providing convenient and efficient means of capturing and storing knowledge. They are knowledge users,
meaning individuals or groups who benefit from valuable, viable knowledge. Nurses are knowledge engineers, designing, developing,
implementing, and maintaining knowledge. They are knowledge managers, capturing and processing collective expertise and
distributing it where it can create the largest benefit. Finally, they are knowledge developers and generators, changing and evolving
knowledge based on the tasks at hand and the information available.
In the case scenario, at first glance one might label Tom as a knowledge worker, a knowledge acquirer, and a knowledge user.
However, stopping here might sell Tom short in his practice of nursing science. Although he acquired and used knowledge to help him
achieve his work, he also processed the data and information he collected to develop a nursing diagnosis and a plan of care. The
knowledge stores Tom used to develop and glean knowledge from valuable information are generative (having the ability to originate
and produce or generate) in nature. For example, Tom may have learned something new about his patient’s culture from the patient or
his wife that he will file away in the knowledge repository of his mind to be used in another similar situation. As he compares this new
cultural information to what he already knows, he may gain insight into the effect of culture on a patient’s response to illness. In this
sense, Tom is a knowledge generator. If he shares this newly acquired knowledge with another practitioner, and as he records his
observations and his conclusions, he is then disseminating knowledge. Tom also uses feedback from the various technologies he has
applied to monitor his patient’s status. In addition, he may rely on feedback from laboratory reports or even other practitioners to help
him rethink, revise, and apply the knowledge about this patient that he is generating.
To have ongoing value, knowledge must be viable. Knowledge viability refers to applications (most technology based) that offer
easily accessible, accurate, and timely information obtained from a variety of resources and methods and presented in a manner so as
to provide the necessary elements to generate new knowledge. In the case scenario, Tom may have felt the need to consult an
electronic database or a clinical guidelines repository that he has downloaded on his personal digital assistant (PDA) or that reside in
the emergency room’s networked computer system to assist him in the development of a comprehensive care plan for his patient. In
this way, Tom uses technology and evidence to support and inform his practice. It is also possible in this scenario that an alert might
appear in the patient’s electronic health record or the clinical information system (CIS) reminding Tom to ask about influenza and
pneumonia vaccines. Clinical information technologies that support and inform nursing practice and nursing administration are an
important part of nursing informatics and are covered in detail in the Nursing Informatics Administrative Applications: Precare and
Care Support and Nursing Informatics Practice Applications: Care Delivery sections of this text. Technologies that support and
inform nursing education and nursing research are covered in the Education Applications and Research Applications of Nursing
Informatics sections respectively.
This text provides a framework that embraces knowledge so that readers can develop the wisdom necessary to apply what they
have learned. Wisdom is the application of knowledge to an appropriate situation. In the practice of nursing science, one expects
actions to be directed by wisdom. Wisdom uses knowledge and experience to heighten common sense and insight to exercise sound
judgment in practical matters. It is developed through knowledge, experience, insight, and reflection. Wisdom is sometimes thought of
as the highest form of common sense, resulting from accumulated knowledge or erudition (deep, thorough learning) or enlightenment
(education that results in understanding and the dissemination of knowledge). It is the ability to apply valuable and viable knowledge,
experience, understanding, and insight while being prudent and sensible. Knowledge and wisdom are not synonymous: Knowledge
abounds with others’ thoughts and information, whereas wisdom is focused on one’s own mind and the synthesis of experience,
insight, understanding, and knowledge. Wisdom has been called the foundation of the art of nursing.
Some nursing roles might be viewed as more focused on some aspects rather than other aspects of the foundation of knowledge.
For example, some might argue that nurse educators are primarily knowledge disseminators and that nurse researchers are knowledge
generators. Although the more frequent output of their efforts can certainly be viewed in this way, it is important to realize that nurses
use all of the aspects of the Foundation of Knowledge model regardless of their area of practice. For nurse educators to be effective,
they must be in the habit of constantly building and rebuilding their foundation of knowledge about nursing science. In addition, as
they develop and implement curricular innovations, they must evaluate the effectiveness of those changes. In some cases, they use
formal research techniques to achieve this goal and, therefore, generate knowledge about the best and most effective teaching
strategies. Similarly, nurse researchers must acquire and process new knowledge as they design and conduct their research studies. All
nurses have the opportunity to be involved in the formal dissemination of knowledge via their participation in professional
conferences, either as presenters or as attendees. In addition, some nurses disseminate knowledge by formal publication of their ideas.
In the cases of conference presentation and publication, nurses may receive feedback that stimulates rethinking about the knowledge
they have generated and disseminated, in turn prompting them to acquire and process data and information anew.
All nurses, regardless of their practice arena, must use informatics and technology to inform and support that practice. The case
scenario discussed Tom’s use of various monitoring devices that provide feedback on the physiologic status of the patient. It was also
suggested that Tom might consult a clinical database or nursing practice guidelines residing on a PDA or a clinical agency network as
he develops an appropriate plan of action for his nursing interventions. Perhaps the CIS in the agency supports the collection of data
about patients in a relational database, providing an opportunity for data mining by nursing administrators or nurse researchers. In
this way, administrators and researchers can glean information about best practices and determine which improvements are necessary
to deliver the best and most effective nursing care (Swan, Lang, & McGinley, 2004).
The future of nursing science and nursing informatics is closely associated with nursing education and nursing research. Skiba
(2007) suggests that techno-savvy and well-informed faculty who can demonstrate the appropriate use of technologies to enhance the
delivery of nursing care are needed. Along those lines, Greenfield (2007) conducted research among nursing students to determine the
effectiveness of PDA technology applied to medication administration. Her study makes a good case for incorporating such technology
into nursing curricula. Girard (2007) discussed cutting-edge operating room technologies, such as nanosurgery using nanorobots, smart
fabrics that aid in patient assessment during surgery, biopharmacy techniques for the safe and effective delivery of anesthesia, and
virtual reality training. She makes an extremely provocative point about nursing education: “Educators will need to expand their
knowledge and teach for the future and not the past. They must take heed that the old tried-and-true nursing education methods and
curriculum that has lasted 100 years will have to change, and that change will be mandated for all areas of nursing” (p. 353).
Bassendowski (2007) specifically addresses the potential for the generation of knowledge in educational endeavors as faculty apply
new technologies to teaching and the focus shifts away from individual to group instruction that promotes sharing and processing of
knowledge.
Several key national groups are promoting the inclusion of informatics content in nursing education programs. These initiatives
include a proposal by the National League for Nursing (NLN, 2008); recommendations in the Quality and Safety Education for Nurses
(Cronenwett et al., 2007) report; the Technology Informatics Guiding Education Reform (TIGER) Initiative (2007); and a plan by the
American Association of Colleges of Nursing (AACN, 2008).
The NLN’s (2008) position statement, Preparing the Next Generation of Nurses to Practice in a Technology-Rich Environment:
An Informatics Agenda, challenges nurse educators to prepare informatics-competent nurses who can practice safely in a technology-
rich healthcare environment.
In the Quality and Safety Education for Nurses (2007) report, Cronenwett and colleagues identified several core competencies for
nursing education. One competency specifically addressed nursing informatics: “Use information and technology to communicate,
manage knowledge, mitigate error, and support decision-making” (p. 129). Another addressed the appropriate use of data and
information in nursing practice to promote quality improvement: “Use data to monitor the outcomes and processes and use
improvement methods to design and test changes to continuously improve the quality and safety of health care systems” (p. 127).
The TIGER (2007) initiative identifies a key purpose: “to create a vision for the future of nursing that bridges the quality chasm
with information technology, enabling nurses to use informatics in practice and education to provide safer, higher-quality patient care”
(p. 4). The pillars of the TIGER vision include the following:
Management and Leadership: Revolutionary leadership that drives, empowers, and executes the transformation of health care.
Education: Collaborative learning communities that maximize the possibilities of technology toward knowledge development
and dissemination, driving rapid deployment and implementation of best practices.
Communication and Collaboration: Standardized, person-centered, technology-enabled processes to facilitate teamwork and
relationships across the continuum of care.
Informatics Design: Evidence-based, interoperable intelligence systems that support education and practice to foster quality
care and safety.
Information Technology: Smart, people-centered, affordable technologies that are universal, useable, useful, and standards
based.
Policy: Consistent, incentives-based initiatives (organizational and governmental) that support advocacy and coalition-building,
achieving and resourcing an ethical culture of safety.
Culture: A respectful, open system that leverages technology and informatics across multiple disciplines in an environment
where all stakeholders trust each other to work together toward the goal of high quality and safety (p. 4).
The Essentials of Baccalaureate Education for Professional Nursing Practice (AACN, 2008, pp. 18–19) includes the following
technology-related outcomes for baccalaureate nursing graduates:
1. Demonstrate skills in using patient care technologies, information systems, and communication devices that support safe
nursing practice.
2. Use telecommunication technologies to assist in effective communication in a variety of healthcare settings.
3. Apply safeguards and decision-making support tools embedded in patient care technologies and information systems to support
a safe practice environment for both patients and healthcare workers.
4. Understand the use of CIS to document interventions related to achieving nurse-sensitive outcomes.
5. Use standardized terminology in a care environment that reflects nursing’s unique contribution to patient outcomes.
6. Evaluate data from all relevant sources, including technology, to inform the delivery of care.
7. Recognize the role of information technology in improving patient care outcomes and creating a safe care environment.
8. Uphold ethical standards related to data security, regulatory requirements, confidentiality, and clients’ right to privacy.
9. Apply patient care technologies as appropriate to address the needs of a diverse patient population.
10. Advocate for the use of new patient care technologies for safe, quality care.
11. Recognize that redesign of workflow and care processes should precede implementation of care technology to facilitate
nursing practice.
12. Participate in the evaluation of information systems in practice settings through policy and procedure development.
The report suggests the following sample content for achieving these student outcomes (AACN, 2008, pp. 19–20):
Use of patient care technologies (e.g., monitors, pumps, computer-assisted devices)
Use of technology and information systems for clinical decision making
Computer skills that may include basic software, spreadsheet, and healthcare databases
Information management for patient safety
Regulatory requirements through electronic data-monitoring systems
Ethical and legal issues related to the use of information technology, including copyright, privacy, and confidentiality issues
Retrieval information systems, including access, evaluation of data, and application of relevant data to patient care
Online literature searches
Technological resources for evidence-based practice
Web-based learning and online literature searches for self and patient use
Technology and information systems safeguards (e.g., patient monitoring, equipment, patient identification systems, drug alerts
and IV systems, and bar coding)
Interstate practice regulations (e.g., licensure, telehealth)
Technology for virtual care delivery and monitoring
Principles related to nursing workload measurement and resources and information systems
Information literacy
Electronic health record and physician order entry
Decision support tools
Role of the nurse informaticist in the context of health informatics and information systems
The Informatics and Healthcare Technologies Essentials of Master’s Education in Nursing includes the following elements:
Essential V: Informatics and Healthcare Technologies
Rationale
Informatics and healthcare technologies encompass five broad areas:
Use of patient care and other technologies to deliver and enhance care
Communication technologies to integrate and coordinate care
Data management to analyze and improve outcomes of care
Health information management for evidence-based care and health education
Facilitation and use of electronic health records to improve patient care (AACN, 2011, pp. 17–18).
Quality and Safety Education for Nurses
As nursing science evolves, it is critical that patient care improves. Sometimes, unfortunately, patient care is less than adequate and is
unsafe. Therefore, quality and safety have become paramount. The Quality and Safety Education for Nurses (QSEN) Institute project
seeks to prepare future nurses who will have the knowledge, skills, and attitudes (KSAs) necessary to continuously improve the quality
and safety of the healthcare systems within which they work.
Prelicensure informatics KSAs include the following (QSEN Institute, n.d.b):
INFORMATICS
Knowledge Skills Attitudes
Explain why information and technology skills
are essential for safe patient care
Seek education about how
information is managed in
care settings before
providing care
Apply technology and
information management
tools to support safe
Appreciate the necessity for all health
professionals to seek lifelong, continuous
learning of information technology skills
processes of care
Identify essential information that must be
available in a common database to support patient
care
Contrast benefits and limitations of different
communication technologies and their impact on
safety and quality
Navigate the electronic
health record
Document and plan patient
care in an electronic health
record
Employ communication
technologies to coordinate
care for patients
Value technologies that support clinical
decision making, error prevention, and care
coordination
Protect the confidentiality of protected
health information in electronic health
records
Describe examples of how technology and
information management are related to the quality
and safety of patient care
Recognize the time, effort, and skill required for
computers, databases, and other technologies to
become reliable and effective tools for patient
care
Respond appropriately to
clinical decision-making
supports and alerts
Use information
management tools to
monitor the outcomes of
care processes
Value nurses’ involvement in design,
selection, implementation, and evaluation
of information technologies to support
patient care
Use high-quality electronic sources of
health-care information
Definition: Use information and technology to communicate, manage knowledge, mitigate error, and support decision making.
Reprinted from Nursing Outlook, 55(3), Cronenwett, L., Sherwood, G., Barnsteiner J., Disch, J., Johnson, J., Mitchell, P., Sullivan, D., Warren, J.,
Quality and safety education for nurses, pages 122–131. copyright 2007, with permission from Elsevier.
Graduate-level informatics KSAs include the following (QSEN Institute, n.d.a):
INFORMATICS
Knowledge Skills Attitudes
Contrast benefits and limitations of common
information technology strategies used in the
delivery of patient care
Evaluate the strengths and weaknesses of
information systems used in patient care
Participate in the selection, design,
implementation, and evaluation of
information systems
Communicate the integral role of
information technology in nurses’
work
Model behaviors that support the
implementation and appropriate use of
electronic health records
Assist team members to adopt
information technology by piloting
and evaluating proposed technologies
Value the use of information and
communication technologies in patient
care
Formulate essential information that must be
available in a common database to support
patient care in the practice specialty
Evaluate benefits and limitations of different
communication technologies and their
impact on safety and quality
Promote access to patient care
information for all professionals who
provide care to patients
Serve as a resource for how to
document nursing care at basic and
advanced levels
Develop safeguards for protected
health information
Champion communication
technologies that support clinical
decision making, error prevention,
care coordination, and protection of
patient privacy
Appreciate the need for consensus and
collaboration in developing systems to
manage information for patient care
Value the confidentiality and security
of all patient records
Describe and critique taxonomic and
terminology systems used in national efforts
to enhance interoperability of information
systems and knowledge management
systems
Access and evaluate high-quality
electronic sources of healthcare
information
Participate in the design of clinical
decision-making supports and alerts
Search, retrieve, and manage data to
make decisions using information and
knowledge management systems
Anticipate unintended consequences
of new technology
Value the importance of standardized
terminologies in conducting searches
for patient information
Appreciate the contribution of
technological alert systems
Appreciate the time, effort, and skill
required for computers, databases, and
other technologies to become reliable
and effective tools for patient care
Definition: Use information and technology to communicate, manage knowledge, mitigate error, and support decision making.
Reprinted from Nursing Outlook, 57(6), Cronenwett, L., Sherwood, G., Pohl, J., Barnsteiner, J., Moore, S., Sullivan, D., Ward, D., Warren, J.,
Quality and safety education for advanced nursing practice, pages 338–348, copyright 2009, with permission from Elsevier.
This text is designed to include the necessary content to prepare nurses for practice in the ever-changing and technology-laden
healthcare environments.
Goossen (2000) believes that the focus of nursing informatics research should be on the structuring and processing of patient
information and the ways that these endeavors inform nursing decision making in clinical practice. The increased use of technology to
enhance nursing practice, nursing education, and nursing research will open new avenues for acquiring, processing, generating, and
disseminating knowledge.
In the future, nursing research will make significant contributions to the development of nursing science. Technologies and
translational research will abound, and clinical practices will be evidence based, thereby improving patient outcomes and decreasing
safety concerns. Schools of nursing will embrace nursing science as they strive to meet the needs of changing student populations and
the increasing complexity of healthcare environments.
Summary
Nursing science influences all areas of nursing practice. This chapter provided an overview of nursing science and considered how
nursing science relates to typical nursing practice roles, nursing education, and nursing research. The Foundation of Knowledge model
was introduced as the organizing conceptual framework for this text. Finally, the relationship of nursing science to nursing informatics
was discussed. In subsequent chapters the reader will learn more about how nursing informatics supports nurses in their many and
varied roles. In an ideal world, nurses would embrace nursing science as knowledge users, knowledge managers, knowledge
developers, knowledge engineers, and knowledge workers.
THOUGHT-PROVOKING
QUESTIONS
1. Imagine you are in a social situation and someone asks you, “What does a nurse do?” Think about how you will capture and convey the richness
that is nursing science in your answer.
2. Choose a clinical scenario from your recent experience and analyze it using the Foundation of Knowledge model. How did you acquire
knowledge? How did you process knowledge? How did you generate knowledge? How did you disseminate knowledge? How did you use
feedback, and what was the effect of the feedback on the foundation of your knowledge?
References
American Association of Colleges of Nursing (AACN). (2008, October 20). The essentials of baccalaureate education for professional nursing practice.
http://www.aacn.nche.edu/education-resources/baccessentials08
American Association of Colleges of Nursing (AACN). (2011, March 21). The essentials of master’s education in nursing.
http://www.aacn.nche.edu/education-resources/MastersEssentials11
American Nurses Association. (2003). Nursing’s social policy statement (2nd ed.). Silver Spring, MD: Author.
Bassendowski, S. (2007). NursingQuest: Supporting an analysis of nursing issues. Journal of Nursing Education, 46(2), 92–95. Retrieved from
Education Module database [document ID: 1210832211].
Cronenwett, L., Sherwood, G., Barnsteiner J., Disch, J., Johnson, J., Mitchell, P., …Warren, J. (2007). Quality and safety education for nurses. Nursing
Outlook, 55(3), 122–131.
Girard, N. (2007). Science fiction comes to the OR. Association of Operating Room Nurses.
AORN Journal, 86(3), 351–353. Retrieved from Health Module database [document ID: 1333149261].
Goossen, W. (2000). Nursing informatics research. Nurse Researcher, 8(2), 42. Retrieved from ProQuest Nursing & Allied Health Source database
[document ID: 67258628].
Greenfield, S. (2007). Medication error reduction and the use of PDA technology. Journal of Nursing Education, 46(3), 127–131. Retrieved from
Education Module database [document ID: 1227347171].
National League for Nursing (NLN). (2008). Preparing the next generation of nurses to practice in a technology rich environment: An informatics
agenda [Position statement]. http://www.nln.org/aboutnln/PositionStatements/informatics_052808
QSEN Institute. (n.d.a). Graduate KSAs. http://qsen.org/competencies/graduate-ksas/
QSEN Institute. (n.d.b). Pre-licensure KSAs. http://qsen.org/competencies/Pre-licensure-ksas/Skiba, D. (2007). Faculty 2.0: Flipping the novice to expert
continuum. Nursing Education Per-
spectives, 28(6), 342–344. Retrieved from ProQuest Nursing & Allied Health Source database [document ID: 1401240241].
Swan, B., Lang, N., & McGinley, A. (2004). Access to quality health care: Links between evidence, nursing language, and informatics. Nursing
Economics, 22(6), 325–332. Retrieved from Health Module database [document ID: 768191851].
Technology Informatics Guiding Education Reform. (2007). Evidence and informatics transforming nursing: 3-year action steps toward a 10-year vision.
http://www.tigersummit.com/uploads/TIGERInitiative_Report2007_Color
Chapter 2
Introduction to Information, Information Science, and
Information Systems
Dee McGonigle and Kathleen Mastrian
OBJECTIVES
1. Reflect on the progression from data to information to knowledge.
2. Describe the term information.
3. Assess how information is acquired.
4. Explore the characteristics of quality information.
5. Describe an information system.
6. Explore data acquisition or input and processing or retrieval, analysis, and synthesis of data.
7. Assess output or reports, documents, summaries alerts, and outcomes.
8. Describe information dissemination and feedback.
9. Define information science.
10. Assess how information is processed.
11. Explore how knowledge is generated in information science.
Key Terms
Acquisition
Alert
Analysis
Chief information officer
Chief technical officer
Chief technology officer
Cloud computing
Cognitive science
Communication science
Computer-based information system
Computer science
Consolidated Health Informatics
Data
Dissemination
Document
Electronic health record
Federal Health Information Exchange
Feedback
Health information exchange
Health Level Seven
Indiana Health Information Exchange
Information
Information science
Information system
Information technology
Input
Interface
Internet2 Knowledge
Knowledge worker
Library science
Massachusetts Health
Data Consortium
National Health Information Infrastructure
National Health Information Network
New England Health EDI Network
Next-Generation Internet
Outcome
Output
Processing
Rapid Syndromic Validation Project
Report
Social sciences
Stakeholder
Summaries
Synthesis
Telecommunications
Introduction
This chapter explores information, information systems (IS), and information science. The key word here, of course, is information.
Healthcare professionals are knowledge workers, and they deal with information on a daily basis. Many concerns and issues arise
with healthcare information, such as ownership, access, disclosure, exchange, security, privacy, disposal, and dissemination. With the
gauntlet of developing electronic health records having been laid down, publicand private-sector stakeholders have been
collaborating on a wide-ranging variety of healthcare information solutions. These initiatives include Health Level Seven (HL7),
Consolidated Health Informatics’ (CHI’s) eGov initiative, the National Health Information Infrastructure (NHII), the National
Health Information Network (NHIN), Next-Generation Internet (NGI), Internet2, and iHealth record. There are also health
information exchange (HIE) systems, such as Connecting for Health, the eHealth initiative, the Federal Health Information
Exchange (FHIE), the Indiana Health Information Exchange (IHIE), the Massachusetts Health Data Consortium (MHDC), the
New England Health EDI Network (NEHEN), the State of New Mexico Rapid Syndromic Validation Project (RSVP), the
Southeast Michigan e-Prescribing Initiative, and the Tennessee Volunteer eHealth Initiative (Goldstein, Groen, Ponkshe, & Wine,
2007). The most recent federal government initiative, the HITECH Act, has set 2014 as the deadline for implementing electronic
health records (see the Legislative Aspects of Nursing Informatics: HITECH and HIPAA chapter).
It is quite evident from the previous brief listing that there is a need to remedy healthcare information technology concerns,
challenges, and issues faced today. One of the main issues deals with how healthcare information is managed to make it meaningful. It
is important to understand how people obtain, manipulate, use, share, and dispose of information. This chapter deals with the
information piece of this complex puzzle.
Information
Suppose someone states the number 99.5. What does that mean? It could be a radio station or a score on a test. Now suppose someone
says that Ms. Howsunny’s temperature is 99.5°F—what does that convey? It is then known that 99.5 is a person’s temperature. The
data (99.5) were processed to the information that 99.5° is a specific person’s temperature. Data are raw facts. Information is
processed data that has meaning. Healthcare professionals constantly process data and information to provide the best care possible for
their patients.
Many types of data exist, such as alphabetic, numeric, audio, image, and video data. Alphabetic data refer to letters, numeric data
refer to numbers, and alphanumeric data include both letters and numbers. This includes all text and the numeric outputs of digital
monitors. Some of the alphanumeric data encountered by healthcare professionals are in the form of patients’ names, identification
numbers, or medical record numbers. Audio data refer to sounds, noises, or tones—for example, monitor alerts or alarms, taped or
recorded messages, and other sounds. Image data include graphics and pictures, such as graphic monitor displays or recorded
electrocardiograms, radiographs, magnetic resonance imaging
(MRI) outputs, and computed tomography (CT) scans. Video data refer to animations, moving pictures, or moving graphics. Using
these data, one may review the ultrasound of a pregnant patient, examine a patient’s echocardiogram, watch an animated video for
professional development, or learn how to operate a new technology tool, such as a pump or monitoring system.
The integrity and quality of the data, rather than the form, are what matter. Integrity refers to whole, complete, correct, and
consistent data. Data integrity can be compromised through human error; viruses, worms, or other computer bugs; hardware failures or
crashes; transmission errors; or hackers entering the system. Information technologies help to decrease these errors by putting into
place safeguards, such as backing up files on a routine basis, error detection for transmissions, and user interfaces that help people
enter the data correctly. High-quality data are relevant and accurately represent their corresponding concepts. Data are dirty when a
database contains errors, such as duplicate, incomplete, or outdated records. One author (D.M.) found 50 cases of tongue cancer in a
database she examined for data quality. When the records were tracked down and analyzed, and the dirty data were removed, only one
case of tongue cancer remained. In this situation, the data for the same person had been entered erroneously 49 times. The major
problem was with the patient’s identification number and name: The number was changed or his name was misspelled repeatedly. If
researchers had just taken the number of cases in that defined population as 50, they would have concluded that tongue cancer was an
epidemic, resulting in flawed information that is not meaningful. As this example demonstrates, it is imperative that data be clean if the
goal is quality information. The data that are processed into information must be of high quality and integrity to create meaning to
inform assessments and decision making.
To be valuable and meaningful, information must be of good quality. Its value relates directly to how the information informs
decision making. Characteristics of valuable, quality information include accessibility, security, timeliness, accuracy, relevancy,
completeness, flexibility, reliability, objectivity, utility, transparency, verifiability, and reproducibility.
Accessibility is a must; the right user must be able to obtain the right information at the right time and in the right format to meet
his or her needs. Getting meaningful information to the right user at the right time is as vital as generating the information in the first
place. The right user refers to an authorized user who has the right to obtain the data and information he or she is seeking. Security is a
major challenge because unauthorized users must be blocked while the right user is provided with open, easy access (see the
Electronic Security chapter).
Timely information means that the information is available when it is needed for the right purpose and at the right time. Knowing
who won the lottery last week does not help one to know if the person won it today. Accurate information means that there are no
errors in the data and information. Relevant information is a subjective descriptor, in that the user must have information that is
relevant or applicable to his or her needs. If a healthcare provider is trying to decide whether a patient needs insulin and only the
patient’s CT scan information is available, this information is not relevant for that current need. However, if one needed information
about the CT scan, then the information is relevant.
Complete information contains all of the necessary essential data. If the healthcare provider needs to contact the only relative listed
for the patient and his or her contact information is listed but the approval for that person to be a contact is missing, this information is
considered incomplete. Flexible information means that the information can be used for a variety of purposes. Information concerning
the inventory of supplies on a nursing unit, for example, can be used by nurses who need to know if an item is available for use for a
patient. The nurse manager accesses this information to help decide which supplies need to be ordered, to determine which items are
used most frequently, and to do an economic assessment of any waste.
Reliable information comes from reliable or clean data and authoritative and credible sources. Objective information is as close to
the truth as one can get; it is not subjective or biased, but rather is factual and impartial. If someone states something, it must be
determined whether that person is reliable and whether what he or she is stating is objective or tainted by his or her own perspective.
Utility refers to the ability to provide the right information at the right time to the right person for the right purpose. Transparency
allows users to apply their intellect to accomplish their tasks while the tools housing the information disappear. Verifiable information
means that one can check to verify or prove that the information is correct. Reproducibility refers to the ability to produce the same
information again.
Information is acquired either by actively looking for it or by having it conveyed by the environment. All of the senses (vision,
hearing, touch, smell, and taste) are used to gather input from the surrounding world, and as technologies mature, more and more
input will be obtained through the senses. Currently, people receive information from computers (output), through vision, hearing, or
touch (input); and the response (output) to the computer (input) is the interface with technology. Gesture recognition is increasing, and
interfaces that incorporate it will change the way people become informed. Many people access the Internet on a daily basis seeking
information or imparting information. Individuals are constantly becoming informed, discovering, or learning; becoming reinformed,
rediscovering, or relearning; and purging what has been acquired. The information acquired through these processes is added to the
knowledge base. Knowledge is the awareness and understanding of a set of information and ways that information can be made useful
to support a specific task or arrive at a decision. This knowledge building is an ongoing process engaged in while a person is conscious
and going about his or her normal daily activities.
Information Science
Information science has evolved over the last 50 some years as a field of scientific inquiry and professional practice. It can be thought
of as the science of information, studying the application and usage of information and knowledge in organizations and the interface or
interaction between people, organizations, and information systems (IS). This extensive, interdisciplinary science integrates features
from cognitive science, communication science, computer science, library science, and social sciences. Information science is
primarily concerned with the input, processing, output, and feedback of data and information through technology integration with a
focus on comprehending the perspective of the stakeholders involved and then applying information technology as needed. It is
systemically based, dealing with the big picture rather than individual pieces of technology.
Information science can also be related to determinism. Specifically, it is a response to technologic determinism—the belief that
technology develops by its own laws, that it realizes its own potential, limited only by the material resources available, and must
therefore be regarded as an autonomous system controlling and ultimately permeating all other subsystems of society (Web Dictionary
of Cybernetics and Systems, 2007, para. 1).
This approach sets the tone for the study of information as it applies to itself, the people, the technology, and the varied sciences
that are contextually related depending on the needs of the setting or organization; what is important is the interface between the
stakeholders and their systems, and the ways they generate, use, and locate information. According to Cornell University (2010),
“Information Science brings together faculty, students and researchers who share an interest in combining computer science with the
social sciences of how people and society interact with information” (para. 1). Information science is an interdisciplinary, people-
oriented field that explores and enhances the interchange of information to transform society, communication science, computer
science, cognitive science, library science, and the social sciences. Society is dominated by the need for information, and knowledge
and information science focuses on systems and individual users by fostering user-centered approaches that enhance society’s
information capabilities, effectively and efficiently linking people, information, and technology. This impacts the configuration and
mix of organizations and influences the nature of work—namely, how knowledge workers interact with and produce meaningful
information and knowledge.
Information Processing
Claude E. Shannon, who is considered the father of information theory (Horgan, 1990), thought of information processing as the
conversion of latent information into manifest information. Latent information is that which is not yet realized or apparent, whereas
manifest information is obvious or clearly apparent. According to O’Connor and Robertson (2005), “Shannon believed that
information was no different than any other quantity and therefore could be manipulated by a machine” (para. 13).
Information science enables the processing of information. This processing links people and technology. Humans are organic ISs,
constantly acquiring, processing, and generating information or knowledge in their professional and personal lives. This high degree of
knowledge, in fact, characterizes humans as extremely intelligent organic machines. The premise of this text revolves around this
concept, and the text is organized on the basis of the Foundation of Knowledge model: knowledge acquisition, knowledge processing,
knowledge generation, and knowledge dissemination.
Information is data that are processed using knowledge. For information to be valuable or meaningful, it must be accessible,
accurate, timely, complete, cost-effective, flexible, reliable, relevant, simple, verifiable, and secure. Knowledge is the awareness and
understanding of an information set and ways that information can be made useful to support a specific task or arrive at a decision. As
an example, if an architect were going to design a building, part of the knowledge necessary for developing a new building is
understanding how the building will be used, what size of building is needed compared to the available building space, and how many
people will have or need access to this building. Therefore, the work of choosing or rejecting facts based on their significance or
relevance to a particular task, such as designing a building, is also based on a type of knowledge used in the process of converting data
into information. Information can then be considered data made functional through the application of knowledge. The knowledge used
to develop and glean knowledge from valuable information is generative (having the ability to originate and produce or generate) in
nature. Knowledge must also be viable. Knowledge viability refers to applications that offer easily accessible, accurate, and timely
information obtained from a variety of resources and methods and presented in a manner so as to provide the necessary elements to
generate knowledge.
Information science and computational tools are extremely important in enabling the processing of data, information, and
knowledge in health care. In this environment, the hardware, software, networking, algorithms, and human organic ISs work together
to create meaningful information and generate knowledge. The links between information processing and scientific discovery are
paramount. However, without the ability to generate practical results that can be disseminated, the processing of data, information, and
knowledge is for naught. It is the ability of machines (inorganic ISs) to support and facilitate the functioning of people (human organic
ISs) that refines, enhances, and evolves nursing practice by generating knowledge. This knowledge represents five rights: the right
information, accessible by the right people in the right settings, applied the right way at the right time.
An important and ongoing process is the struggle to integrate new knowledge and old knowledge so as to enhance wisdom.
Wisdom is the ability to act appropriately; it assumes actions directed by one’s own wisdom. Wisdom uses knowledge and experience
to heighten common sense, and insight to exercise sound judgment in practical matters. It is developed through knowledge, experience,
insight, and reflection. Wisdom is sometimes thought of as the highest form of common sense, resulting from accumulated knowledge
or erudition (deep, thorough learning) or enlightenment (education that results in understanding and the dissemination of knowledge).
It is the ability to apply valuable and viable knowledge, experience, understanding, and insight while being prudent and sensible.
Knowledge and wisdom are not synonymous, because knowledge abounds with others’ thoughts and information, whereas wisdom is
focused on one’s own mind and the synthesis of one’s own experience, insight, understanding, and knowledge.
If clinicians are inundated with data without the ability to process it, the situation results in too much data and too little wisdom.
Consequently, it is crucial that clinicians have viable ISs at their fingertips to facilitate the acquisition, sharing, and use of knowledge
while maturing wisdom; this process leads to empowerment.
Information Science and the Foundation of Knowledge
Information science is a multidisciplinary science that encompasses aspects of computer science, cognitive science, social science,
communication science, and library science to deal with obtaining, gathering, organizing, manipulating, managing, storing, retrieving,
recapturing, disposing of, distributing, and broadcasting information. Information science studies everything that deals with
information and can be defined as the study of ISs. This science originated as a subdiscipline of computer science, as practitioners
sought to understand and rationalize the management of technology within organizations. It has since matured into a major field of
management and is now an important area of research in management studies. Moreover, information science has expanded its scope
to examine the human–computer interaction, interfacing, and interaction of people, ISs, and corporations. It is taught at all major
universities and business schools worldwide.
Modern-day organizations have become intensely aware of the fact that information and knowledge are potent resources that must
be cultivated and honed to meet their needs. Thus information science or the study of ISs—that is, the application and usage of
knowledge—focuses on why and how technology can be put to best use to serve the information flow within an organization.
Information science impacts information interfaces, influencing how people interact with information and subsequently develop
and use knowledge. The information a person acquires is added to his or her knowledge base. Knowledge is the awareness and
understanding of an information set and ways that information can be made useful to support a specific task or arrive at a decision.
Healthcare organizations are affected by and rely on the evolution of information science to enhance the recording and processing
of routine and intimate information while facilitating human-to-human and human-to-systems communications, delivery of healthcare
products, dissemination of information, and enhancement of the organization’s business transactions. Unfortunately, the benefits and
enhancements of information science technologies have also brought to light new risks, such as glitches and loss of information and
hackers who can steal identities and information. Solid leadership, guidance, and vision are vital to the maintenance of cost-effective
business performance and cuttingedge, safe information technologies for the organization. This field studies all facets of the building
and use of information. The emergence of information science and its impact on information have also influenced how people acquire
and use knowledge.
Information science has already had a tremendous impact on society and will undoubtedly expand its sphere of influence further as
it continues to evolve and innovate human activities at all levels. What visionaries only dreamed of is now possible and part of reality.
The future has yet to fully unfold in this important arena.
Introduction to Information Systems
Consider the following scenario: You have just been hired by a large healthcare facility. You enter the personnel office and are told
that you must learn a new language to work on the unit where you have been assigned. This language is used just on this unit. If you
had been assigned to a different unit, you would have to learn another language that is specific to that unit, and so on. Because of the
differences in various units’ languages, interdepartmental sharing and information exchange (known as interoperability) are severely
hindered.
This scenario might seem far-fetched, but it is actually how workers once operated in health care—in silos. There was a system for
the laboratory, one for finance, one for clinical departments, and so on. As healthcare organizations have come to appreciate the
importance of communication, tracking, and research, however, they have developed integrated information systems that can handle
the needs of the entire organization.
Information and information technology have become major resources for all types of organizations, and health care is no
exception (see Box 2-1). Information technologies help to shape a healthcare organization, in conjunction with personnel, money,
materials, and equipment. Many healthcare facilities have hired chief information officers (CIOs) or chief technical officers (CTOs),
also known as chief technology officers. The CIO is involved with the information technology infrastructure, and this role is
sometimes expanded to include the position of chief knowledge officer. The CTO is focused on organizationally based scientific and
technical issues and is responsible for technological research and development as part of the organization’s products and services. The
CTO and CIO must be visionary leaders for the organization, because so much of the business of health care relies on solid
infrastructures that generate potent and timely information and knowledge. The CTO and CIO are sometimes interchangeable
positions, but in some organizations the CTO reports to the CIO. These positions will become critical roles as companies continue to
shift from being product oriented to knowledge oriented, and as they begin emphasizing the production process itself rather than the
product. In health care, ISs must be able to handle the volume of data and information necessary to generate the needed information
and knowledge for best practices, because the goal is to provide the highest quality of patient care.
Information Systems
ISs can be manually based, but for the purposes of this text, the term refers to computer-based information systems (CBISs).
According to Jessup and Valacich (2008), computer-based ISs “are combinations of hardware, software and telecommunications
networks that people build and use to collect, create, and distribute useful data, typically in organizational settings” (p. 10). Along the
same lines, ISs are also defined as “a set of interrelated components that collect, manipulate, store and disseminate data and
information and provide a feedback mechanism to meet an objective” (Stair & Reynolds, 2008, p. 4). ISs are designed for specific
purposes within organizations. They are only as functional as the decision-making capabilities, problem-solving skills, and
programming potency built in and the quality of the data and information input into them (see the Systems Development Life Cycle:
Nursing Informatics and Organizational Decision Making chapter). The capability of the IS to disseminate, provide feedback, and
adjust the data and information based on these dynamic processes is what sets them apart. The IS should be a user-friendly entity that
provides the right information at the right time and in the right place.
BOX 2-1 EXAMPLES OF INFORMATION SYSTEMS
Information System How It Is Used
Clinical Information System (CIS)
Comprehensive and integrative system that manages the administrative,
financial, and clinical aspects of a clinical facility; a CIS should help to link
financial and clinical outcomes. An example is the electronic health record
(EHR).
Decision Support System (DSS)
Organizes and analyzes information to help decision makers formulate
decisions when they are unsure of their decision’s possible outcomes. After
gathering relevant and useful information, develops “what if” models to
analyze the options or choices and alternatives.
Executive Support System
Collects, organizes, analyzes, and summarizes vital information to help
executives or senior management with strategic decision making. Provides a
quick view of all strategic business activities.
Geographic Information System (GIS)
Collects, manipulates, analyzes, and generates information related to
geographic locations or the surface of the earth; provides output in the form
of virtual models, maps, or lists.
Management Information Systems (MIS)
Provides summaries of internal sources of information, such as information
from the transaction processing system, and develops a series of routine
reports for decision making.
Office Systems Facilitates communication and enhances the productivity of users needing toprocess data and information.
Transaction Processing System (TPS)
Processes and records routine business transactions, such as billing systems
that create and send invoices to customers, and payroll systems that generate
employees’ pay stubs and wage checks and calculate tax payments.
Hospital Information System (HIS) Manages the administrative, financial, and clinical aspects of a hospitalenterprise. It should help to link financial and clinical outcomes.
An IS acquires data or inputs; processes data through the retrieval, analysis, or synthesis of those data; disseminates or outputs
information in the form of reports, documents, summaries, alerts, prompts, or outcomes; and provides for responses or feedback. Input
or data acquisition is the activity of collecting and acquiring raw data. Input devices include combinations of hardware, software, and
telecommunications and include keyboards, light pens, touch screens, mice or other pointing devices, automatic scanners, and
machines that can read magnetic ink characters or lettering. To watch a pay-per-view movie, for example, the viewer must first input
the chosen movie, verify the purchase, and have a payment method approved by the vendor. The IS must acquire this information
before the viewer can receive the movie.
Processing—the retrieval, analysis, or synthesis of data—refers to the alteration and transformation of the data into helpful or
useful information and outputs. The processing of data can range from storing it for future use, to comparing the data, making
calculations, or applying formulas, to taking selective actions. Processing devices consist of combinations of hardware, software, and
telecommunications and include processing chips where the central processing unit (CPU) and main memory are housed. Some of
these chips are quite ingenious. According to Schupak (2005), the bunny chip could save the pharmaceutical industry money while
sparing “millions of furry creatures, with a chip that mimics a living organism” (para. 1). The HµREL Corporation has developed
environments or biologic ISs that reside on chips and actually mimic the functioning of the human body. Researchers can use these
environments to test for both the harmful and beneficial effects of drugs, including those that are considered experimental and that
could be harmful if used in human and animal testing. Such chips also allow researchers to monitor a drug’s toxicity in the liver and
other organs.
One patented HµREL microfluidic “biochip” comprises an arrangement of separate but fluidically interconnected “organ” or
“tissue” compartments. Each compartment contains a culture of living cells drawn from, or engineered to mimic the primary functions
of the respective organ or tissue of a living animal. Microfluidic channels permit a culture medium that serves as a “blood surrogate”
to recirculate just as in a living system, driven by a microfluidic pump. The geometry and fluidics of the device are fashioned to
simulate the values of certain related physiologic parameters found in the living creature. Drug candidates or other substrates of
interest are added to the culture medium and allowed to recirculate through the device. The effects of drug compounds and their
metabolites on the cells within each respective organ compartment are then detected by measuring or monitoring key physiologic
events. The cell types used may be derived from either standard cell culture lines or primary tissues (HµREL Corporation, 2010, para.
2–3). As new technologies such as the HµREL chips continue to evolve, more and more robust ISs that can handle a variety of
biological and clinical applications will be seen.
Returning to the movie rental example, the IS must verify the data entered by the viewer and then process the request by following
the steps necessary to provide access to the movie that was ordered. This processing must be instantaneous in today’s world, where
everyone wants everything now. After the data are processed, they are stored. In this case, the rental must also be processed so the
vendor receives payment for the movie, whether electronically, via a credit card or checking account withdrawal, or by generating a
bill for payment.
Output or dissemination produces helpful or useful information that can be in the form of reports, documents, summaries, alerts,
or outcomes. Reports are designed to inform and are generally tailored to the context of a given situation or user or user group.
Reports may include charts, figures, tables, graphics, pictures, hyperlinks, references, or other documentation necessary to meet the
needs of the user. Documents represent information that can be printed, saved, e-mailed or otherwise shared, or displayed.
Summaries are condensed versions of the original designed to highlight the major points. Alerts are warnings, feedback, or additional
information necessary to assist the user in interacting with the system. Outcomes are the expected results of input and processing.
Output devices are combinations of hardware, software, and telecommunications and include sound and speech synthesis outputs,
printers, and monitors.
Continuing with the movie rental example, the IS must be able to provide the consumer with the movie ordered when it is wanted
and somehow notify the purchaser that he or she has, indeed, purchased the movie and is granted access. The IS must also be able to
generate payment either electronically or by generating a bill, while storing the transactional record for future use.
Feedback or responses are reactions to the inputting, processing, and outputs. In ISs, feedback refers to information from the
system that is used to make modifications in the input, processing actions, or outputs. In the movie rental example, what if the
consumer accidentally entered the same movie order three times, but really wanted to order the movie only once? The IS would
determine that more than one movie order is out of range for the same movie order at the same time and provide feedback. Such
feedback is used to verify and correct the input. If undetected, the viewer’s error would result in an erroneous bill and decreased
customer satisfaction while creating more work for the vendor, which would have to engage in additional transactions with the
customer to resolve this problem. The Nursing Informatics Practice Applications: Care Delivery section of this text provides detailed
descriptions of clinical ISs that operate on these same principles to support healthcare delivery.
Summary
Information systems deal with the development, use, and management of an organization’s information technology (IT) infrastructure.
An IS acquires data or inputs; processes data through the retrieval, analysis, or synthesis of those data; disseminates or outputs in the
form of reports, documents, summaries, alerts, or outcomes; and provides for responses or feedback. Quality decision-making and
problem-solving skills are vital to the development of effective, valuable ISs. Today’s organizations now recognize that their most
precious asset is their information, as represented by their employees, experience, competence or know-how, and innovative or novel
approaches, all of which are dependent on a robust information network that encompasses the information technology infrastructure.
In an ideal world, all ISs would be fluid in their ability to adapt to any and all users’ needs. They would be Internet oriented and
global, where resources are available to everyone. Think of cloud computing—it is just the beginning point from which ISs will
expand and grow in their ability to provide meaningful information to their users. As technologies advance, so will the skills and
capabilities to comprehend and realize what ISs can become.
It is important to continue to develop and refine functional, robust, visionary ISs that meet the current meaningful information
needs while evolving systems that are even better prepared to handle future information and knowledge needs of the healthcare
industry.
THOUGHT-PROVOKING
QUESTIONS
1. How do you acquire information? Choose 2 hours out of your busy day and try to notice all of the information that you receive from your
environment. Keep diaries indicating where the information came from and how you knew it was information and not data.
2. Reflect on an IS with which you are familiar, such as the automatic banking machine. How does this IS function? What are the advantages of
using this system (i.e., why not use a bank teller instead)? What are the disadvantages? Are there enhancements that you would add to this
system?
3. In health care, think about a typical day of practice and describe the setting. How many times does the nurse interact with ISs? What are the ISs
that we interact with, and how do we access them? Are they at the bedside, hand-held, or station based? How do their location and ease of access
impact nursing care?
4. Briefly describe an organization and discuss how our need for information and knowledge impacts the configuration and interaction of that
organization with other organizations. Also discuss how the need for information and knowledge influences the nature of work or how
knowledge workers interact with and produce information and knowledge in this organization.
5. If you could meet only four of the rights discussed in this chapter, which one would you omit and why? Also, provide your rationale for each
right you chose to meet.
References
Cornell University. (2010). Information science. http://www.infosci.cornell.edu/
Goldstein, D., Groen, P., Ponkshe, S., & Wine, M. (2007). Medical informatics 20/20. Sudbury, MA: Jones and Bartlett.
Horgan, J. (1990). Claude E. Shannon: Unicyclist, juggler and father of information theory. Retrieved March 2008 from
http://www.ecs.umass.edu/ece/hill/ece221.dir/shannon.html
HµREL Corporation. (2010). Human-relevant: HµREL. Technology overview. http://www.hurelcorp.com/overview.php
Jessup, L., & Valacich, J. (2008). Information systems today (3rd ed.). Upper Saddle River, NJ: Pearson Prentice Hall.
O’Connor, J., & Robertson, E. (2005). Claude Elwood Shannon. http://www.thocp.net/biographies/shannon_claude.htm
Schupak, A. (2005). Technology: The bunny chip. http://members.forbes.com/forbes/2005/0815/053.html
Stair, R., & Reynolds, G. (2008). Principles of information systems (8th ed.). Boston, MA: Thomson Course Technology.
Web Dictionary of Cybernetics and Systems. (2007). Technological determinism. http://pespmc1.vub.ac.be/ASC/TECHNO_DETER.html
Chapter 3
Computer Science and the Foundation of Knowledge Model
June Kaminski
OBJECTIVES
1. Describe the essential components of computer systems, including both hardware and software.
2. Recognize the rapid evolution of computer systems and the benefit of keeping up-to-date with current trends and developments.
3. Analyze how computer systems function as tools for managing information and generating knowledge.
4. Define the concept of human–technology interfaces.
5. Articulate how computers can support collaboration, networking, and information exchange.
Key Terms
Acquisition
Applications
Arithmetic logic unit (ALU)
Basic input/output system (BIOS)
Binary system
Bit
Bus
Byte
Cache memory
Central processing unit (CPU)
Communication software
Compact disk read-only memory (CD-ROM)
Compact disk-recordable (CD-R)
Compact disk-rewritable (CD-RW)
Compatibility
Computer
Computer science
Conferencing software
Creativity software
Databases
Degradation
Desktop
Digital video disk (DVD)
Digital video disk-recordable (DVD-R)
Digital video disk-rewritable (DVD-RW)
Dissemination
Dynamic random access memory (DRAM)
E-mail
E-mail client
Electronically erasable programmable read-only memory (EEPROM)
Exabyte (EB)
Execute
Extensibility
FireWire
Firmware
Flash memory
Gigabyte
Gigahertz
Graphical user interface
Graphics card
Hard disk
Hard drive
Hardware
Information
Information Age
Instant message (IM)
Integrated drive electronics (IDE)
Internet browser
Keyboard
Knowledge
Laptop
Main memory
Mainframes
Megabyte (MB)
Megahertz (MHz)
Memory
Microprocessor
Microsoft Surface
Modem
Monitor
Motherboard
Mouse
MPEG-1 Audio Layer-3 (MP3)
Networks
Nonsynchronous
Office suite
Open source
Operating system (OS)
Palm computers
Parallel port
Peripheral component interconnection (PCI)
Personal computer (PC)
Personal digital
assistant (PDA)
Plug and play
Port
Portability
Portable operating system interface for UNIX (POSIX)
Power supply
Presentation
Processing
Productivity software
Professional development
Programmable read-only memory (PROM)
Publishing
QWERTY
Random-access memory (RAM)
Read-only memory (ROM)
Security
Serial port
Small Computer System Interface (SCSI)
Software
Sound card
Spreadsheet
Supercomputers
Synchronous
Synchronous dynamic random-access memory (SDRAM)
Technology
Terabyte (TB) Throughput
Touch screen
Universal serial bus (USB)
User friendly
User interface
Video adapter card
Virtual memory
Wearable technology
Wisdom
Word processing
World Wide Web (WWW)
Yottabyte (YB)
Zettabyte (ZB)
Introduction
In this chapter, the discipline of computer science is introduced through a focus on computers and the hardware and software that make
up these evolving systems. Computer science offers extremely valuable tools that, if used skillfully, can facilitate the acquisition and
manipulation of data and information by nurses, who can then synthesize these into an evolving knowledge and wisdom base. This
process can facilitate professional development and the ability to apply evidence-based practice decisions within nursing care, and if
the results are disseminated and shared, can also advance the professional knowledge base.
This chapter begins with a look at common computer hardware, followed by a brief overview of operating, productivity, creativity, and
communication software. It concludes with a glimpse at how computer systems help to shape knowledge and collaboration and an
introduction to human–technology interface dynamics.
The Computer as a Tool for Managing Information and Generating Knowledge
Throughout history, various milestones have signaled discoveries, inventions, or philosophic shifts that spurred a surge in knowledge
and understanding within the human race. The advent of the computer is one such milestone, which has sparked an intellectual
metamorphosis whose boundaries have yet to be fully understood. Computer technology has ushered in what has been called the
Information Age, an age when data, information, and knowledge are both accessible and able to be manipulated by more people than
ever before in history. How can a mere machine lead to such a revolutionary state of knowledge potential? To begin to answer this
question, it is best to examine the basic structure and components of computer systems.
Essentially, a computer is an electronic information-processing machine that serves as a tool with which to manipulate data and
information. The easiest way to begin to understand computers is to realize they are input–output systems. These unique machines
accept data input via a variety of devices, process data through logical and arithmetic rendering, store the data in memory components,
and output data and information to the user.
Since the advent of the first electronic computer in the mid-1940s, computers have evolved to become essential tools in every walk
of life, including the profession of nursing. The complexity of computers has increased dramatically over the years, and will continue
to do so. “Computing has changed the world more than any other invention of the past hundred years, and has come to pervade nearly
all human endeavors. Yet, we are just at the beginning of the computing revolution; today’s computing offers just a glimpse of the
potential impact of computers” (Evans, 2010, p. 3). Major computer manufacturers and researchers, such as Intel, have identified the
need to design computers to mask this growing complexity. The sophistication of computers is evolving at amazing speed, yet ease of
use or user-friendly aspects are also increasing accordingly. This is achieved by honing hardware and software capabilities until they
work seamlessly together to ensure user-friendly, intuitive tools for users of all levels of expertise. Box 3-1 provides information about
computing surfaces, an evolving technology.
According to Intel Corporation’s technology research team, the goal is “technology that just works.” “To conceal complexity, Intel
Research is looking at a number of solutions by:
Relating user mental models with complex systems and technology to improve the use and adaptation of systems across devices
and contexts.
Enabling devices to explore their environment to discover other devices and capabilities, and then form integrated ‘teams’ that
self-organize for higher functionality and performance.
Better control of failure modes, graceful degradation, and self-healing across ensembles of devices.
Zero-knowledge applications and interoperation.” (Intel Corporation, 2008, para. 2)
BOX 3-1 MICROSOFT SURFACE TENSION? iTABLE
Dee McGonigle
Do not get too attached to your mouse and keyboard, because they will be outdated soon if Microsoft and PQ Labs have their way. Microsoft has
introduced the Microsoft Surface and PQ Labs is building custom iTables, according to Kumparak (2009). Have you ever thought of digital
information you can touch and grab? Microsoft and PQ Labs are leading us into the next generation of computing, known as surface or table
computing.
Surface or table computing consists of a multitouch, multiuser interface that allows one to “grab” digital information and then collaborate, share,
and store that information, without using a mouse or keyboard—just the hands and fingers, and such devices as a digital camera and personal digital
assistant (PDA). This interface generally rests on top of a table and is so advanced that it can actually sense objects, touch, and gestures from many
users (Microsoft, 2008).
Imagine entering a restaurant and interacting with the menu through the surface of the table where you sit. Once you have completed your order,
you can begin computing by using the capabilities built into the surface or using your own device, such as a PDA. You can set the PDA on the
surface and download images, graphics, and text to the surface. You can even communicate with others using full audio and video while waiting for
your order. When you have finished eating, you simply set your credit card on the surface and it is automatically charged; you pick up your credit
card and leave. This is certainly a different kind of eating experience—but one that will become commonplace for the next generation of users.
You might be wondering when this new age of computing will be touched by typical users. In fact, it is already used in Las Vegas, as well as in
selected casinos, banks, restaurants, and hotels throughout the United States and Canada.
You should seek to explore this new interface, which will forever change how we interact and compute. Think of the
ramifications for health care …
REFERENCES
Kumparak, G. (2009). Look out, Microsoft Surface: The iTable might just trump you in every way. http://www.crunchgear.com/2009/01/10/look-
out-microsoft-surface-the-itable-mightjust-trump-you-in-every-way/
Microsoft. (2008). Microsoft Surface: General questions. Retrieved May 2010 from http://www.microsoft.com/SURFACE/about_faqs/faqs.aspx
One example of this type of complexity masked in simplicity is the evolution of “plug and play” computer add-ons, where a
peripheral, such as an iPod or game console, can be simply plugged into a serial or other port and instantly used.
Computers are universal machines, because they are general-purpose, symbol-manipulating devices that can perform any task
represented in specific programs. For instance, they can be used to draw an image, calculate statistics, write an essay, or record nursing
care data. In a nutshell, computers can be used for data and information storage, retrieval, analysis, generation, and transformation.
Most computers are based on scientist John Von Neumann’s model of a processor–memory–input–output architecture. In this
model, the logic unit and control unit are parts of the processor, the memory is the storage region, and the input and output segments
are provided by the various computer devices, such as the keyboard, mouse, monitor, and printer. Recent developments have provided
alternative configurations to the Von Neumann model—for example, the parallel computing model, where multiple processors are set
up to work together. Nevertheless, today’s computer systems share the same basic configurations and components inherent in the
earliest computers.
Components
Hardware
Computer hardware refers to the actual physical body of the computer and its components. Several key components in the average
computer work together to shape a complex yet highly usable machine that serves as a tool for knowledge management,
communication, and creativity.
Protection: The Casing
The most noticeable component of any computer is the outer case. Desktop personal computers have either a desktop case, which lies
flat, horizontally on a desk, often with the computer monitor positioned on top of it; or a tower case, which stands vertically, and
usually sits beside the monitor or on a lower shelf or the floor. Most cases come equipped with a case fan, which is extremely critical
for keeping the computer components cool when in use. Laptop computers combine the casing in a flat rectangular casing that is
attached to the hinged or foldable monitor. Palm computers and personal digital assistants also have a protective outer plastic and
metal case with an embedded liquid crystal display screen.
Central Processing Unit
Sometimes conceptualized as the “brain” of the computer, the central processing unit (CPU) is the computer component that actually
executes, calculates, and processes the binary computer code (which consists of various configurations of 0s and 1s), instigated by the
operating system (OS) and other applications on the computer. The CPU serves as the command center that directs the actions of all
other computer components, and it manages both incoming and outgoing data that are processed across components. Common CPUs
include the Pentium, K6, PowerPC, and Sparc models.
The CPU contains specific mechanical units, including registers, arithmetic logic units, a floating point unit, control circuitry, and
cache memory. Together, these inner components form the computer’s central processor. Registers consist of data-storing circuits
whose contents are processed by the adjacent arithmetic and logic units or the floating point unit. Cache memory is extremely quick
memory that holds whatever data and code are being used at any one time. The CPU uses the cache to store inprocess data so that it
can be quickly retrieved as needed. The CPU is protected by a heat sink, a copper or aluminum metal block that cools the processor
(often with the help of a fan) to prevent overheating.
In the past, the speed and power of a CPU were measured in units of megahertz and was written as a value in MHz (e.g., 400
MHz, meaning the microprocessor ran at 400 MHz, executing 400 million cycles per second). Today, it is more common to see the
speed measured in gigahertz (1 GHz is equal to 1,000 MHz); thus a CPU that operates at 4 GHz is 1,000 times faster than an older one
that operates at 4 MHz. The more cycles a processor can complete per second, the faster computer programs can run.
In recent years, processor manufacturers, such as Intel, have moved to multicore microprocessors, which are chips that combine
two or more processors. In fact, multiple microprocessors have become a standard in both personal and professional-level computers.
“Minicomputers, which were traditionally made from off-the-shelf logic or from gate arrays, have been replaced by servers made using
microprocessors. Mainframes have been almost replaced with multiprocessors consisting of small numbers of off-the-shelf
microprocessors. Even high-end supercomputers are being built with collections of microprocessors” (Hennessy & Patterson, 2006,
p. 3).
Motherboard
The motherboard has been called the “central nervous system” of the computer. It is a key foundational component because all other
components are connected to it in some way (either directly via local sockets, attached directly to it, or connected via cables). This
includes universal serial bus (USB) controllers, Ethernet network controllers, integrated graphics controllers, and so forth. The
essential structures of the motherboard include the major chipset, Super Input/Output chip, BIOS read-only memory (ROM), bus
communications pathways, and a variety of sockets that allow components to plug into the board. The chipset (often a pair of chips)
determines the computer’s CPU type and memory. It also houses the north bridge and south bridge controllers that allow the buses to
transfer data from one to another.
Power Supply
The power supply is a critical component of any computer, because it provides the essential electrical energy needed to allow a
computer to operate. The power supply unit converts the 240-V AC main power (provided via the power cable from the wall socket
into which the computer is plugged) into low-voltage DC power. Computers depend on a reliable, steady supply of DC power to
function properly. The more devices and programs used on a computer, the larger the power supply should be to avoid damage and
malfunctioning. Power supplies normally range from 160 to 700 W, with an average of 300 to 400 W. Most contemporary power
supply units come equipped with at least one fan to cool the unit under heavy use. The power supply is controlled by pressing the on
and off switch, as well as the reset switch (which restarts the system) of a computer.
Laptop and other portable computing machines, such as electronic readers and tablet computers, are equipped with a rechargeable
battery power supply and the standard plug-in variety.
Hard Disk
This component is so named because of the rigid hard disks that reside in it, which are mounted to a spindle that is spun by a motor
when in use. Drive heads (most computers have two or more heads) produce a magnetic field through their transducers that magnetizes
the disk surface as a voltage is applied to the disk. The hard disk acts as a permanent data storage area that holds the gigabytes or even
terabytes worth of data, information, documents, and programs saved on the computer, even when the computer is shut off. Disk drives
are not infallible, however, so backing up important data is imperative.
The computer writes binary data to the hard drive by magnetizing small areas of its surface. Each drive head is connected to an
actuator that moves along the disk to hover over any point on the disk surface as it spins. The parts of the hard disk are encased in a
sealed unit. The hard drive is managed by a disk controller, which is a circuit board that controls the motor and actuator arm assembly.
The hard drive produces the voltage waveform that contacts the heads to write and read data, and handles communications with the
motherboard. It is usually located within the computer’s hard outer casing. Some people also attach a second hard drive externally, to
increase available memory or to back up data.
Main Memory or Random-Access Memory
Random-access memory (RAM) is considered to be volatile memory because it is a temporary storage system that allows the
processor to access program codes and data while working on a task. The contents of RAM are lost once the system is rebooted, shut
off, or loses power.
The memory is actually situated on small chip boards, which sport rows of pins along the bottom edge and are plugged into the
motherboard of the computer. These memory chips contain complex arrays of tiny memory circuits that can be either set by the CPU
during write operations (puts them into storage) or read by the CPU during data retrieval. The circuits store the data in binary form as
either a low (on) voltage stage, expressed as a 0, or a high (off) voltage stage, expressed as a 1. All of the work being done on a
computer resides in RAM until it is saved onto the hard drive or other storage drive. Computers generally come with 2 GB of RAM or
more, and some offer more RAM via graphics cards and other expansion cards.
A certain portion of the RAM, called the main memory, serves the hard disk and facilitates interactions between the hard disk and
central processor. Main memory is provided by dynamic random access memory (DRAM) and is attached to the processor using
specific addresses and data buses.
Synchronous dynamic random-access memory (SDRAM) (also known as static dynamic RAM) is “much faster than
conventional (nonsynchronous) memory because it can synchronize itself with a microprocessor’s bus” (Null & Lobor, 2006, p. 8).
Read-Only Memory
Read-only memory (ROM) is essential permanent or semipermanent nonvolatile memory that stores saved data and is critical in the
working of the computer’s OS and other activities. ROM is primarily stored in the motherboard, but it may also be available through
the graphics card, other expansion cards, and peripherals. In recent years, rewritable ROM chips that may include other forms of
ROM, such as programmable read-only memory (PROM), erasable ROM, electronically erasable programmable read-only
memory (EEPROM), and a flash memory (a variation of electronically erasable programmable ROM) have become available.
Basic Input/Output System
The basic input/output system (BIOS) is a specific type of ROM used by the computer when it first boots up, to establish basic
communication between the processor, motherboard, and other components. Often called boot firmware, it controls the computer from
the time the machine is switched on until the primary OS (e.g., Windows, OS X, or Linux) takes over. The firmware initializes the
hardware and boots (loads and executes) the primary OS.
Virtual Memory
Virtual memory is a special type of memory is stored on the hard disk to provide temporary data storage so data can be swapped in
and out of the RAM as needed. This capability is particularly handy when working with large data-intensive programs, such as games
and multimedia.
Integrated Drive Electronics Controller
The integrated drive electronics (IDE) controller component is the primary interface for the hard drive, compact disk read-only
memory (CD-ROM), or digital video disk (DVD) drive, and the floppy disk drive.
Peripheral Component Interconnection Bus
This component is important for connecting additional plug-in components to the computer. It uses a series of slots on the motherboard
to allow peripheral component interconnection (PCI) card plug-in.
Small Computer System Interface
The Small Computer System Interface (SCSI) component provides the means to attach additional devices, such as scanners and
extra hard drives, to the computer.
DVD/CD Drive
The CD-ROM drive reads and records data to portable CDs, using a laser diode to emit an infrared light beam that reflects onto a track
on the CD using a mirror positioned by a motor. The light reflected on the disk is directed by a system of lenses to a photodetector that
converts the light pulses into an electrical signal; this signal is then decoded by the drive electronics to the motherboard. Both compact
disk-recordable (CD-R) and compact disk-rewritable (CD-RW) drives are common. The same principle applies to digital video
disk-recordable (DVD-R) and digital video disk-rewritable (DVD-RW) drives. A DVD drive can do everything a CD drive can do,
plus it can play the content of disks and, if it is a recordable unit, can record data on blank DVDs.
Flash or USB Drive
This portable memory device uses electronically erasable programmable ROM to provide fast permanent memory.
Modem
A modem is a component that can be situated either externally (external modem) or internally (internal modem) relative to the
computer and enables Internet connectivity via a cable connection through network adaptors situated within the computer apparatus.
Connection Ports
All computers have connection ports made to fit different types of plug-in devices. These ports include a monitor cable port, keyboard
and mouse ports, a network cable port, microphone/speaker/auxiliary input ports, USB ports, and printer ports (SCSI or parallel).
These ports allow data to move to and from the computer via peripheral or storage devices. Specific ports include the following:
Parallel: connects to a printer
Serial: connects to an external modem
USB: connects to a myriad of plug-in devices, such as portable flash drives, digital cameras, MPEG-1 Audio Layer-3 (MP3)
players, graphics tablets, and light pens, using a plug-and-play connection (the ability to add devices automatically)
FireWire (IEEE 1394): often used to connect digital-video devices to the computer
Ethernet: connects networking apparatus, such as Internet and modem cables
Graphics Card
Most computers come equipped with a graphics accelerator card slotted in the microprocessor of a computer to process image data and
output those data to the monitor. These in situ graphic cards provide satisfactory graphics quality for two-dimensional art and general
text and numerical data. However, if a user intends to create or view three-dimensional images or is an active game user, one or more
graphics enhancement cards are often installed.
Video Adapter Cards
Video adapter cards provide video memory, a video processor, and a digital-to-analog converter that works with the CPU to output
higher quality video images to the monitor.
Sound Card
The sound card converts digital data into an analog signal that is then output to the computer’s speakers or headphones. The reverse is
also accomplished by inputting a signal from a microphone or other audio recording equipment, which then converts the analog signal
to a digital signal.
Bit
A bit is the smallest possible chunk of data memory used in computer processing and is depicted as either a 1 or a 0. Bits make up the
binary system of the computer.
Byte
A byte is a chunk of memory that consists of 8 bits; it is considered to be the best way to indicate computer memory or storage
capacity. In modern computers, bytes are described in units of megabytes (MB); gigabytes (GB), where 1 GB equals 1,000 MB; or
terabytes (TB), where 1 TB equals 1 trillion bytes or 1,000 GB. Box 3-2 discusses storage capacities.
Software
Software comprises the application programs developed to facilitate various user functions, such as writing, artwork, organizing
meetings, surfing the Internet, communicating with others, and so forth. For the purposes of this overview, the various types of
software have been divided into four categories: (1) OS software, (2) productivity software, (3) creativity software, and (4)
communication software.
User friendliness is a critical condition for effective software adoption. “End user performance is likely to be facilitated by user
friendliness of software packages” (Mahmood, 2003, p. 71). The easier and more intuitive a software package seems to be to a user
influences that user’s perception of how clear the package is to understand and to use. The rapid evolution of hardware mentioned
previously has been equally matched by the phenomenal development in software over the past three or four decades.
Commercial Software
Several large commercial software companies, such as Apple, Microsoft, IBM, and Adobe, dominate the market for software, and have
done so since the advent of the personal computer. Licensed software has evolved over time; hence, most products have a long
version history. Many software packages, such as office suites, are expensive to purchase; in turn, there is a “digital divide” as far as
access and affordability go across societal spheres, especially when viewed from a global perspective.
BOX 3-2 STORAGE CAPACITIES
Dee McGonigle and Kathleen Mastrian
Storage and memory capacities are evolving. In the past few decades, there have been great leaps in data storage. It all begins with the bit, the basic
unit of data storage, composed of 0s and 1s, also known as binary digits (bit). A byte is generally considered to be equal to 8 bits. The files on a
computer are stored as binary files. The software that is used translates these binary files into words, numbers, pictures, images, or video. Using this
binary code in the binary numbering system, measurement is counted by factors of 2, such as 1, 2, 4, 8, 16, 32, 64, and 128. These multiples of the
binary system in computer usage are also prefixed based on the metric system. Therefore, a kilobyte (KB) is actually 2 to the 10th power (210) or
1,024 bytes, but is typically considered to be 1,000 bytes. This is why one sees 1,024 or multiples of that number instead of an even 1,000 mentioned
at times in relation to kilobytes.
In the early 1980s, kilobytes were the norm as far as computer capacity went, and 128 KB machines were launched for personal use. Subsequent
decades, however, have seen advanced computing power and storage capacity. As capabilities soared, so did the ability to save and store what was
used and created. Megabytes (MB) emerged as a common unit of measure; a megabyte is 1,048,576 bytes but is considered to be roughly equivalent
to 1 million bytes. The next leap in computer capacity was one that some people could not even imagine: gigabytes (GB). A gigabyte is
1,073,741,824 bytes but is generally rounded to 1 billion bytes. Some computing experts are very concerned that valuable bytes are lost when these
measurements are rounded, whereas hard drive manufacturers use the decimal system so their capacity is expressed as an even 1 billion bytes per
gigabyte.
The next advancements in computer capacity are moving into the range of terabytes (TB), petabytes (PB), exabytes (EB), zettabytes (ZB), and
yottabytes (YB). These terms storage capacity are defined as follows:
TB 1,000 GB
PB 1,000,000 GB
EB 1,000 PB
ZB 1,000 EB
YB 1,000 ZB
To put all of this in perspective, Williams (n.d., para. 5) writes about the data powers of 10: 2 kilobytes: A typewritten page
2 megabytes: A high-resolution photograph
10 megabytes: A minute of high-fidelity sound or a digital chest X-ray
50 megabytes: A digital mammogram
1 gigabyte: A symphony in high-fidelity sound or a movie at TV quality
1 terabyte: All the X-ray films in a large technologically advanced hospital
2 petabytes: The contents of all U.S. academic research libraries
5 exabytes: All words ever spoken by human beings
We have not even addressed ZB and YB. Stay tuned …
REFERENCE
Williams, R. (n.d.). Data powers of ten. http://ict.stmargaretsacademy.org.uk/computing/hardware/dataquan/d_p_ten2.html
Open Source Software
The open source movement began several years ago, but recently has become a powerful movement that is changing the software
production and consumer market. In addition to commercially available software, a growing number of open source software packages
are being developed in all four of the categories addressed in this chapter. The open source movement was begun by developers who
wished to offer their creations to others for the good of the community and encouraged them to do the same. Users who modify or
contribute to the evolution of open source software are obligated to share their new code, but essentially the software is free to all.
Open Office and KOffice are both examples of open source productivity software.
OS Software
The OS is the most important software on any computer. It is the very first program to load on computer start-up and is fundamental
for the operation of all other software and the computer hardware. Examples of commonly used operating systems include the
Microsoft Windows family, Linux, Mac OS X, and UNIX. The OS manages both the hardware and the software and provides a
reliable, consistent interface for the software applications to work with the computer’s hardware. An OS must be both powerful and
flexible to adapt to the myriad of types of software available, which are made by a variety of development companies. New versions of
the major OSs are equipped to deal with multiple users and handle multitasking with ease. For instance, a user can work on a word
processing document while listening for an “e-mail received” signal, have a Web browser window open to look for references on the
Internet as needed, listen to music in the CD drive, and download a file—all at the same time.
OS tasks can be described in terms of six basic processes:
Memory management
Device management
Processor management
Storage management
Application interface
User interface (usually a graphical user interface [GUI])
OSs should be convenient to use, easy to learn, reliable, safe, and fast. They should also be easy to design, implement, and
maintain and should be flexible, reliable, error free, and efficient. For example, Silbershatz, Baer Galvin, and Gagne (2004) described
how the Windows OS has been designed in keeping with the following goals established by Microsoft:
Portability: The OS can be moved from one hardware architecture to another with few changes needed.
Security: The OS incorporates hardware protection for virtual memory and software protection mechanisms for OS resources.
Portable operating system interface for UNIX (POSIX) compliance: Applications designed to follow the POSIX (IEEE
1003.1) standard can be compiled to run on Windows without changing the source code.
Multiprocessor support: The OS is designed for symmetrical multiprocessing.
Extensibility: This capability is provided by using a layered architecture with a protected executive layer for basic system
services, several server subsystems that operate in user mode, and a modular structure that allows additional environmental
subsystems to be added without affecting the executive layer.
International support: The Windows OS supports different locales via the national language support application programming
interface (API).
Compatibility with MS-DOS and MS-Windows applications.
Productivity Software
Productivity software, such as office suites, is the type of software most commonly used both in the workplace and on personal
computers. Several software companies produce these multiple-program software, which usually bundles together word processing,
spreadsheets, databases, presentation, Web development, and e-mail programs.
The intent of office suites is generally to provide all of the basic programs that office or knowledge workers need to do their work.
The bundled programs within the suite are organized to be compatible with one another, are designed to look similar to one another for
ease of use, and provide a powerful array of tools for data manipulation, information gathering, and knowledge generation. Some
office suites add other programs, such as database creation software, mathematical editors, drawing, and desktop publishing programs.
Table 3-1 summarizes the programs included in five of the most popular office suites: Microsoft Office, Open Office, KOffice, Corel
WordPerfect Suite, and Apple iWork (for Macintosh computers). Of these five, Open Office (for Windows, Linux, Solaris, Mac OS X,
FreeBSD, and HP-UX OSs) and KOffice (for Linux environments but also being developed for Windows and Mac OS X platforms)
are open source, free software.
Creative Software
Creative software includes programs that allow users to draw, paint, render, record music and sound, and incorporate digital video and
other multimedia in professional aesthetic ways to share and convey information and knowledge (Table 3-2).
Communication Software
Networking and communication software enable users to dialogue, share, and network with other users via the exchange of e-mail or
instant messages, by accessing the World Wide Web, or by engaging in virtual meetings using conferencing software (Table 3-3).
TABLE 3-1 OFFICE SUITE SOFTWARE FEATURES AND EXAMPLES
OFFICE SUITE SOFTWARE
Program Application Examples
Word processing Composition, editing, formatting,and producing text documents
Microsoft Word, Open Office Writer, KOffice
KWord, Corel WordPerfect or Corel Write, Apple
Pages
Spreadsheets
Grid-based documents in ledger
format; organizes numbers and text;
calculates statistical formulae
Microsoft Excel, Open Office Calc, KOffice
Kspread, Corel Quattro Pro, Apple Numbers
Presentations
Slideshow software, usually used
for business or classroom
presentations using text, images,
Microsoft Power Point, Open Office Impress,
KOffice KPresenter, Corel Show, Apple Keynote
graphs, media
Databases Database creation for text andnumbers
Microsoft Access (in elite packages), Open Office
Base, KOffice Kexi, Corel Calculate, Corel
Paradox
E-mail Integrated e-mail program to sendand receive electronic mail
Microsoft Outlook, Corel WordPerfect Mail,
Mozilla Thunderbird
Drawing Graphics and diagram drawing Open Office Draw, Corel Presentation Graphics,KOffice Kivio, Karbon, Krita
Math formulas Inserts math equations in wordprocessing and presentation work Open Office Math, KOffice KFormula
Desktop publishing Page layouts and publication-readydocuments
Microsoft Publisher (in elite packages), Apple
Pages
TABLE 3-2 CREATIVE SOFTWARE FEATURES AND EXAMPLES
CREATIVE SOFTWARE
Program and Application Software Examples
Raster graphics programs
Draw, paint, render, manipulate and edit images, fonts, and
photographs to create pixel-based (dot points) digital art and
graphics.
Adobe Photoshop and Fireworks, Ulead PhotoImpact, Corel
Draw, Painter, and Paint Shop Pro, GIMP (open source),
KOffice’s Krita (open source)
Vector graphics programs
Mathematically rendered, geometric modeling is applied
through shapes, curves, lines, points and manipulated for shape,
color, size. Ideal for printing and three-dimensional (3D)
modeling
Adobe Flash, Freehand, and Illustrator, CorelDraw and
Designer, Open Office Draw (open source), Mirosoft Visio,
Xara Xtreme, KOffice Karbon14 (open source)
Desktop publishing programs
Page layout and publishing preparation for printed and web
documents, such as magazines, journals, books, newsletters,
brochures
Adobe InDesign, Corel PageMaker, Microsoft Publisher,
Scribus (open source), QuarkXPress, Apple Pages (note that
many of the graphics programs can also be used for DTP)
Web design programs
Create, edit, update webpages using specific codes, such as
XML, CSS, HTML, and JAVA
Adobe Dreamweaver, Coffee Cup, Microsoft FrontPage, Nvu
(open source), W3C’s Amaya (open source)
Multimedia programs
Combines text, audio, images, animation, and video into
interactive content for electronic presentation.
Adobe Flash, Microsoft Movie Maker, Apple QuickTime and
FinalCut Studio, Corel VideoStudio, Ulead VideoStudio, Real
Studio, CamStudio (open source), Audacity (open source)
TABLE 3-3 COMMUNICATION SOFTWARE FEATURES AND EXAMPLES
COMMUNICATI ON SOFTWARE
E-mail client Resident programs
Allows user to read, edit, forward, and send email messages to
other users via an Internet connection. The software can be
resident on the computer or accessed via the World Wide Web
Microsoft Outlook and Outlook Express, Eudora, Pegasus,
Mozilla Thunderbird, Lotus Notes Web-based programs
Gmail, Yahoo Mail, Hotmail
Internet browsers
Enables user to access, browse, download, upload, and interact
with text, audio, video, and other Web-based documents
Mozilla Firefox, Microsoft Internet Explorer, Google Chrome,
Apple Safari, Opera, Microbrowser (for mobile access)
Instant messaging (IM)
Real-time text messaging between users, can attach images,
videos, and other documents via personal computer, cell phone,
hand-held devices Conferencing
MSN Instant Messenger, Microsoft Live Messenger, Yahoo
Messenger, Apple iChat
Enables user to communicate in a virtual meeting room setting
to share work, discussions, planning, using an intranet or
Internet environment; can exhibit files, video, screen shots of
content
Adobe Acrobat Connect, Microsoft Live Meeting or Meeting
Space, GotoMeeting, Meeting Bridge, Free Conference,
RainDance, WebEx
Acquisition of Data and Information: Input Components
Input devices include the keyboard; mouse; joysticks (typically used for playing computer games); game controllers or pads; Web
cameras (webcams); stylus (often used with tablets or personal digital assistants); image scanners for copying a digital image of a
document or picture; or other plug-and-play input devices, such as digital cameras, digital video recorders (camcorders), MP3 players,
electronic musical instruments, and physiologic monitors (Figure 3-1). These devices are the origin or medium used to input text,
visual, audio, or multimedia data into the computer system for viewing, listening, manipulating, creating, or editing. The two primary
input devices on a computer are the keyboard and mouse.
Figure 3-1 Computer System
Keyboard
Computer keyboards are very similar to the typewriter keyboards of earlier days and usually serve as the prime input device that
enables the user to type words, numbers, and commands into the computer’s programs. Standard computer keyboards have 101 keys
and are organized to facilitate Latin-based languages using a QWERTY layout (so named because these letters appear on the first six
keys in the first row of letters).
Certain keys are used as command keys, particularly the control (CTRL), alternate (Alt), delete (Del), and shift keys, which can all
be used to activate useful commands. The escape (ESC) key allows the user instantly to exit a process or program. The F keys,
numbered F1 through F12, are function keys. They are used in different ways by particular programs. If a program instructs users to
press the “F8” key, they would do so by pressing F8. The print screen (PrtSc) key sends a graphical picture or screen shot of a
computer screen to the clipboard. This copied screen shot can then be pasted in any graphic program that can work with bitmap files.
Mouse
The mouse is the second most commonly used input device. It is manipulated by the user’s hand to point, click, and move objects
around on the computer screen. A mouse can come in a number of different configurations, including a standard mechanical trackball
serial mouse, bus mouse, PS/2 mouse, USB connected mouse, optical lens mouse, cordless mouse, and optomechanical mouse.
Processing of Data and Information: Throughput/Processing Components
All of the hardware discussed earlier in this chapter is involved in the throughput or processing of input data and in the preparation of
output data and information. Specific software is used, depending on the application and data involved. One key hardware component,
the computer monitor, is a unique example of a visible throughput component—it is the part of the computer that users focus on the
most when they are working on a computer. Input data can be visualized and accessed by manipulating the mouse and keyboard input
devices, but it is the monitor that receives the user’s attention. The monitor is critical for the efficient rendering during this part of the
cycle, because it facilitates user access and control of the data and information.
Monitor
The monitor is the visual display that serves as the landscape for all interactions between user and machine. It typically resembles a
television screen, and comes in various sizes (usually ranging from 15 to 21 inches) and configurations. Monitors are either based on
cathode ray tubes (the conventional monitor with a large section behind the screen) or are thinner, flat-screen liquid crystal display
devices. Some computer monitors also have a touch screen that can serve as an input device when the user touches specific areas of
the screen.
Monitors vary in their refresh rate (usually measured in megahertz) and dot pitch. Both of these characteristics are important for
user comfort. The faster the refresh rate, the cleaner and clearer the image on the screen, because the monitor refreshes the screen
contents more frequently. For instance, a monitor with a 100 MHz refresh rate refreshes the screen contents 100 times per second.
Similarly, the larger the dot pitch factor, the smaller the dots that make up the screen image, which provides a more detailed display on
the monitor and also facilitates clarity and ease of viewing.
If equipped with a touch screen, a monitor can also serve as an input device when activated by a stylus or finger pressure. Some
users might also consider the monitor to be an output device, because access to input and stored documents is often performed via the
screen (e.g., reading a document that is stored on the computer or viewable from the Internet).
Dissemination: Output Components
Output devices carry data in a usable form through exit devices in or attached to a computer. Common forms of output include printed
documents, audio or video files, physiologic summaries, scan results, and saved files on portable disk drives, such as a CD, DVD,
flash drive, or external hard drive. Output devices literally put data and information at the user’s fingertips, which can then be used to
develop knowledge and even wisdom. The most commonly used output devices include printers, speakers, and portable disk drives.
Printer
Printers are external components that can be attached to a computer using a printer cord that is secured into the computer’s printer port.
Printers enable users to print a hard paper copy of documents that are housed on the computer.
The most common printer types are the inkjet and laser printers. Inkjet printers are more economical to use and offer quite good
quality; they apply ink to paper using a jet-spray mechanism. Laser printers produce publisher-ready quality printing if combined with
good-quality paper but cost more in terms of printing supplies. Both types of printers can print in black and white or in color.
Speakers
All computers have some sort of speaker setup, usually small speakers embedded in the monitor, in the case, or, if a laptop, close to the
keyboard. Often, external speakers are added to a computer system using speaker connectors; these devices provide enhanced sound
and a more enjoyable listening experience.
What Is the Relationship of Computer Science to Knowledge?
Scholars and researchers are just beginning to understand the effects that computer systems, architecture, applications, and processes
have on the potential for knowledge acquisition and development. Users who have access to contemporary computers equipped with
full Internet access have resources at their fingertips that were only dreamed of before the 21st century. Entire library collections are
accessible, with many documents available in full printable form. Users are also able to contribute to the development of knowledge
through the use of productivity, creativity, and communication software. In addition, using the World Wide Web interface, users are
able to disseminate knowledge on a grand scale with other users.
This deluge of information available via computers must be mastered and organized by the user if knowledge is to emerge.
Discernment and the ability to critique and filter this information must also be present to facilitate the further development of wisdom.
The development of an understanding of computer science principles as they apply to technology used in nursing can facilitate
optimal usage of the technology for knowledge development in the profession. The maxim that “knowledge is power” and that the
skillful use of computers lies at the heart of this power is a presumption:
The computer-literate nurse will have knowledge, and as a result, power and influence. Society has accepted computers as
standard elements, and as such, computers will continue to shape nurses’ psychological, social, economic, and political
existence in innumerable ways. Nursing, in order to interface with other spheres of society, must be computer literate. In short,
society has accepted computer technology as a means to enhance life; so must nursing. (Richards, 2001, p. 9)
Once nurses become comfortable with the various technologies, they can shape them, refine them, and apply them in new and different
ways, just as they have always adapted earlier equipment and technologies.
How Does the Computer Support Collaboration and Information Exchange?
Computers can be linked to other computers through networking software and hardware to promote communication, information
exchange, work sharing, and collaboration. Such networks can be local or organizationally based, with computers joined together into
a local area network; or organized on a wider area scope (e.g., a city or district) using a metropolitan area network; or encompassing
computers at an even greater distance (e.g., a whole country or continent, or the Internet itself) using a wide area network
configuration (Sarkar, 2006). Network interface cards are used to connect a computer and its modem to a network.
Networks within health care can manifest in several different configurations, including client-focused networks, such as in
telenursing, e-health, and client support networks; work-related networks, including virtual work and virtual social networks; and
learning and research networks, as in communities of practice. These trends are still in their infancy in most nursing work
environments (and most nurses’ personal lives), but they are predicted to grow dramatically in the future:
As the Net generation grows in influence, the trend will be toward networks, not hierarchies; toward open collaboration rather
than authority; toward consensus rather than arbitrary edict. The communication support provided by networks and
information systems will also alter patterns of social interaction within a healthcare organization. This technology provides a
medium for greater accessibility to shared information and support for rich interpersonal exchange and collaboration across
departmental boundaries. (Richards, 2001, p. 10)
Virtual social networks are another form of professional network that have expanded phenomenally since the advent of the Internet
and other computer software and hardware:
Electronic media do more than just expand access to vast bodies of information. They also serve as a convenient vehicle for
building virtual social networks for creating shared knowledge through collaborative learning and problem solving. Cross
pollination of ideas through worldwide connectivity can boost creativity synergistically in the co-construction of knowledge.
(Bandura, 2002, p. 4)
Nursing-related virtual social networks provide a cyberspace for nurses to make contacts, share information and ideas, and build a
sense of community.
Social communication software is used to provide a dynamic virtual environment, and often virtual social networks provide
communicative capabilities through posting tools, such as blogs, forums, and wikis; e-mail for sharing ideas on a smaller scale;
collaborative areas for interaction, creating, and building digital artifacts or planning projects; navigation tools for moving through the
virtual network landscape; and profiles to provide a space for each member to disclose personal information with others. Nurses who
have to engage in shift work often find that virtual social networks can provide a sense of connection with other professionals that is
available around the clock. Because time is often a factor in any social interchange, virtual communication often offers an alternative
for practicing nurses, who can access information and engage in interchanges at any time of day. With active participation, the
interchanges and shared information and ideas of the network can culminate in valuable social and cultural capital, available to all
members of that network. Often, nursing virtual social networks are created for the purpose of exchanging ideas on practice issues and
best practices; to become more knowledgeable about new trends, research, and innovations in health care; or to participate in
advocacy, activist, and educational initiatives.
Through the use of portable disk devices, such as flash drives, CDs, and DVDs, people can share information, documents, and
communications by exchanging files. Since the advent of the Internet in the mid-1980s, the World Wide Web has evolved to become a
viable and user-friendly way for people to collaborate and exchange information, projects, and other knowledge-based files, such as
websites, e-mail, social networking applications, and web conferencing logs. Box 3-3 provides information on Web 2.0, the latest
iteration of the World Wide Web.
BOX 3-3 WEB 2.0 TOOLS
Dee McGonigle, Kathleen Mastrian, and Wendy Mahan
Web 2.0—the name given to the new World Wide Web tools—enables users to collaborate, network socially, and disseminate knowledge with other
users on a scale that was once not even comprehensible. These programs promote data and information exchange, feedback, and knowledge
development and dissemination.
To facilitate a selective review of the Web 2.0 tools available, they have been categorized into three areas here: (1) tools for creating and sharing
information, (2) tools for collaborating, and (3) tools for communicating. Examples of tools for creating and sharing information include blogs,
podcasts, Flickr, YouTube, Hellodeo, Jing, Screencast-o-matic, Facebook, MySpace, and MakeBeliefsComix. Examples of tools for collaborating
with others include Google Docs, Zoho, wikis, Del.icio.us, and Gliffy. Finally, some tools for communicating with others include Adobe Connect,
Vyew, Skype, Twitter, and instant messaging.
The application of the creating and sharing information tools has led to an explosion of social networking on the Web. YouTube has promoted
the “broadcast yourself” proliferation. Anyone can launch a video onto YouTube that is shared with others over the Web. Similarly, Flickr allows
users to upload and tag personal photos to share either privately or publicly. Facebook and MySpace both promote socializing on the Web. Facebook
is a social utility and MySpace is a place for friends, according to the descriptions found on these websites. Other tools let users create and share
recorded messages, diagrams, screen captures, and even custom comic strips.
Collaborating over the Web has become easier. Indeed, it is a way of life for many people. Google Docs and Zoho allow users to create online
and share and collaborate in real time. Wikis are server-based software programs that enable users to generate and edit webpage content using any
browser. Del.icio.us is a social bookmarking manager that uses tags to identify or describe the bookmarks that can be shared with others.
Communicating with others includes audio and video conferencing in real time. Adobe Connect is a comprehensive Web communications
solution. Although a fee-based service, it does provide a free trial. Users should read all of the documentation on Adobe’s site before downloading,
installing, and using this software. Vyew is free, always-on collaboration plus live Web conferencing. Skype allows users to make calls in audio only
or with video. Users can download Skype for free but depending on the type of calls made, fees or charges could be assessed. Individuals should read
through all of the information before downloading, installing, and using this software. Twitter allows participants to answer the question “What are
you doing?” with messages containing 140 or fewer characters. Although Twitter can be used to keep the friends in a person’s network updated on
daily activities, it can also be used for other purposes, such as asking questions or expressing thoughts. In addition, Twitter can be accessed by cell
phones, so users can stay in touch on the go.
Along with all of the advantages and intellectual harvesting capabilities from the use of these tools come serious security issues. Wagner (2007)
warns the user to “bear in mind before you jump in that you’re giving information to a third-party company to store” (para. 5). He also states that
“you should talk to your company’s legal and compliance offices to be sure you’re obeying the law and regulations with regard to managing
company’s information” (para. 5). One suggestion that Wagner offers is that if you do not want to involve a third party, “Wikis provide a good
alternative for organizations looking to maintain control of their own software. Organizations can install wiki software on their own, internal servers”
(para. 6).
This new wave of Web-based tools facilitates the ability to interact, exchange, collaborate, communicate, and share in ways that have only begun
to be realized. As the tools and their innovative uses continue to expand, users need to stay vigilant to handle the associated security challenges.
These Web 2.0 tools are providing a new cyber-playground that is limited only by users’ own imaginations and intelligence. We encourage you to
explore these tools. Refer to this text’s companion website (http://go.jblearning.com/mcgonigle) for more information.
REFERENCE
Wagner, M. (2007). Nine easy Web-based collaborative tools. http://www.forbes.com/2007/02/26/google-microsoft-bluetie-enttech-
cx_mw_0226smallbizresource.html
What Is the Human–Technology Interface?
In the context of using a computer system, the human–technology interface is facilitated by the input and output devices discussed
previously in this chapter. Specifically, the keyboard, mouse, monitor, laser pen, joystick, stylus, or game pads and controls, and other
USB or plug-and-play devices, such as MP3 players, digital cameras, digital camcorders, musical instruments, and hand-held smaller
computers, such as personal digital assistants, are all viable devices for interfacing with a computer.
The GUI associated with the OS of a computer provides the on-screen environment for direct interaction between the user and the
computer. The typical GUI provided by Windows or Mac OS X utilizes a user-friendly desktop metaphor interface that is made up of
the input and output devices and icons that represent files, programs, actions, and processes. These interface icons can be activated by
clicking the mouse buttons to perform various actions, such as provide information, execute functions, open and manipulate folders
(directories), select options, and so forth.
Although these aspects of a computer system may be taken for granted, they are critical in facilitating a sense of comfort and
competency in users of the system. This environment is particularly critical in nursing, when computers are used in the context of
nursing care. One question that arises is, Do nurses control these information technology tools, or do the tools shape the activities,
decisions, and attention of the nurses as users of technology? Both possibilities can be answered in the affirmative to some extent, but
the former is the safest situation for nursing care. (See The Art of Caring in Technology-Laden Environments for an expanded
discussion of this issue). If the nurse user needs to focus on the software or hardware because of difficult-to-use programs, confusing
GUI schema, or sheer complexity in the programming, the nurse’s provision of client care will suffer. It is critical that any software
and hardware used in the nursing milieu be expertly designed to facilitate nursing care in a user-friendly, intuitive way. This is one
reason that informatics experts, called nurse informaticians, are being placed in positions of authority where they can facilitate the
adoption of computer systems within nursing care environments. It is essential that the activities of the staff nurses are reflected well
within the software that is used in the care setting. If nurses are knowledgeable about computers and related technologies, they will be
able to provide meaningful data and information about how computer systems best work within their particular care areas.
In an ideal world, nurses would be able to use and interact with computer technologies effectively to enhance patient care. They
would understand computer science and know how to harness its capabilities to benefit the profession and ultimately their patients.
Looking to the Future
The coming trends toward wearable technology, smaller and faster hand-held and portable computer systems, and high-quality voice-
activated inventions will further facilitate the use of computers in nursing practice and professional development. The field of
computer science will continue to contribute to the evolving art and science of nursing informatics. New trends promise to bring wide-
sweeping and (it is hoped) positive changes to the practice of nursing. Computers and other technologies have the potential to support
a more client-oriented healthcare system in which clients truly become active participants in their own healthcare planning and
decisions. Mobile health technology, telenursing, sophisticated electronic health records, and next-generation technology are predicted
to contribute to high-quality nursing care and consultation within healthcare settings, including patients’ homes and communities.
In the future, computers will become more powerful yet more compact, which will contribute to the development of several
technologic initiatives that are currently still in their infancy. Some of these initiatives are described here. These predicted innovations
are only some of the many computer and technologic applications being developed. As nurses gain proficiency in capitalizing on the
creative, time-saving, and interactive capabilities emerging from information technology research, the field of nursing informatics will
grow in similar proportions.
Voice-Activated Communicators
Voice-activated communicators are already being developed by a variety of companies, including Vocera Communications. These new
technologies will permit nurses and other healthcare professionals to use wireless, hands-free devices to communicate with one another
and to record data. This technology promises to become a user-friendly and cost-effective way to increase clinical productivity.
Game and Simulation Technology
Game and simulation technology promises to offer realistic, innovative ways to teach nursing content in general, including nursing
informatics concepts and skills. The same technology that powers video games can be used to create dynamic educational interfaces to
help student nurses learn about pathophysiology, care guidelines, medication usage, and a host of other topics. Such applications can
also be very valuable for client education and health promotion materials. The “serious games” industry is just beginning to develop.
Video game producers are now looking beyond mere entertainment to address public and private policy, management, and leadership
issues and topics, including those related to health care. For example, the Games for Health Project, initiated by the Robert Wood
Johnson Foundation (2006), is working on developing best practices to support innovation in healthcare training, messaging, and
illness management.
Virtual Reality
Virtual reality is another technological breakthrough that will become common in nursing education and professional development in
the future. Virtual reality is a three-dimensional, computer-generated “world” where a person (with the right equipment) can move
about and interact as if he or she were actually in the visualized location. The person’s senses are immersed in this virtual reality world
using special gadgetry, such as head-mounted displays, data gloves, joysticks, and other hand tools. The equipment and special
technology provides a sense of presence that is lacking in multimedia and other complex programs.
Mobile Devices
Mobile devices will be used more by nurses both at the point of care and in planning, documenting, interacting with the healthcare
team, and research.
There are strong indicators that nursing is ready to move quickly to adopt this new technology and utilize it to its full potential
at the point-of-care. We anticipate the rate of adoption for mobile information systems within nursing to be rapid, and it will
ultimately equal and perhaps exceed that of physicians. Mobile Nursing Informatics will be at the core of nursing in the 21st
century. Ready access to data and analytical tools will fundamentally change the way practitioners of the health sciences
conduct research, and approach and solve problems. (Suszka-Hildebrandt, 2000, p. 3)
Summary
The field of computer science is one of the fastest-growing disciplines. Astonishing innovations in computer hardware, software, and
architecture have occurred over the past few decades, and there are no indications that this trend will come to a halt anytime soon.
Computers have increased in speed, accuracy, and efficiency, yet now cost less and have reduced physical size compared to their
forebears. These trends are predicted to continue. Current computer hardware and software serve as vital and valuable tools for both
nurses and clients to engage in on-screen and online activities that provide rich access to data and information. Productivity, creativity,
and communication software tools also enable nurses to work with computers to further foster knowledge acquisition and
development. Wide access to vast stores of information and knowledge shared by others facilitates the emergence of wisdom in users,
which can then be applied to nursing in meaningful and creative ways. It is imperative that nurses become discerning, yet skillful users
of computer technology to apply the principles of nursing informatics to practice, and to contribute to the profession’s ever-growing
body of knowledge.
Working Wisdom
Since the beginning of the profession, nurses have applied their ingenuity, resourcefulness, and professional awareness of what works
to adapt technology and objects to support nursing care, usually with the intention of promoting efficiency but also in support of client
comfort and healing. This resourcefulness could also be applied effectively to the adaptation of information technology within the care
environment, to ensure that the technology truly does serve clients and nurses and the rest of the interdisciplinary team.
Consider this question: “How can you develop competency in using the various computer hardware and software not only to
promote efficient nursing care and to develop yourself professionally, but also to further the development of the profession’s body of
knowledge?”
Application Scenario
Dan P. is a first-year student in graduate studies in nursing. In the past, he has learned to use his family’s personal computer to surf the
World Wide Web, exchange e-mail with friends, and play some computer games. Now, however, Dan realizes that the computer is a
vital tool for his academic success. He has saved up enough money to purchase a laptop computer. He has decided on a Pentium CPU
system with 500 GB of storage and 4 GB of RAM. Dan also wishes to choose appropriate software for his system. He is on a limited
budget but wants to make the most of his investment.
1. Which of the four categories of software discussed in this chapter would benefit Dan the most in his studies (OS, productivity,
creativity, or communication)? Dan definitely needs an OS—this is critical. He would also directly benefit from productivity
software and at least connective e-mail and web browser software from the communication group so he can access the Internet
for research, to collaborate with peers, and to communicate with his teachers.
2. How could Dan afford to install software from all four groups on his new laptop? If Dan accessed some open source software
(e.g., Open Office for his productivity software), he could save money to put toward creativity software.
Internet and Software Resources
BBC Absolute Beginner’s Guide to Using Your Computer: A WebWise Guide. http://www.bbc.co.uk/webwise/abbeg/abbeg.shtml
BBC’s Computer Tutor: The BBC’s Guide to Using a Computer. http://www.bbc.co.uk/webwise/topics/your-computer/
THOUGHT-PROVOKING
QUESTIONS
1. How can knowledge of computer hardware and software help nurses to participate in information technology adoption decisions in the practice
area?
2. How can new computer software help nurses engage in professional development, collaboration, and knowledge dissemination activities at their
own pace and leisure?
References
Bandura, A. (2002). Growing primacy of human agency in adaptation and change in the electronic era. European Psychologist, 7(1), 2–16.
Evans, D. (2010). Introduction to computing: Explorations in language, logic, and machines. University of Virginia. http://www.computingbook.org
Hennessy, J., & Patterson, D. (2006). Computer architecture: A quantitative approach (4th ed.). San Francisco, CA: Morgan Kaufmann.
Intel Corporation. (2008). Concealing complexity. Technology and research. http://techresearch.intel.com/articles/Exploratory/1430.htm
Mahmood, M. (2003). Advanced topics in end user computing. Hershey, PA: Idea Group Inc.
Null, L., & Lobor, J. (2006). The essentials of computer organization and architecture (2nd ed.). Sudbury, MA: Jones and Bartlett.
Richards, J. A. (2001). Nursing in a digital age. Nursing Economic$, 19(1), 6–12.
Robert Wood Johnson Foundation. (2006). Games for health. http://gamesforhealth.org/about/
Sarkar, N. (2006). Tools for teaching computer networking and hardware concepts. Hershey, PA: Idea Group.
Silbershatz, A., Baer Galvin, P., & Gagne, G. (2004). Operating system concepts (7th ed.). Hoboken, NJ: John Wiley & Sons.
Suszka-Hildebrandt, S. (2000). Mobile information technology at the point-of-care. PDA Cortex. http://www.rnpalm.com/mitatpoc.htm
http://gamesforhealth.org/about/
http://www.rnpalm.com/mitatpoc.htm
Chapter 4
Introduction to Cognitive Science and Cognitive
Informatics
Dee McGonigle and Kathleen Mastrian
OBJECTIVES
1. Describe cognitive science.
2. Assess how the human mind processes and generates information and knowledge.
3. Explore cognitive informatics.
4. Examine artificial intelligence and its relationship to cognitive science and computer science.
Key Terms
Artificial intelligence
Brain
Cognitive informatics
Cognitive science
Computer science
Connectionism
Decision making
Empiricism
Epistemology
Intelligence
Intuition
Knowledge
Logic
Memory
Mind
Neuroscience
Perception
Problem solving
Psychology
Rationalism
Reasoning
Wisdom
Introduction
Cognitive science is the fourth of four basic building blocks used to understand informatics. The Building Blocks of Nursing
Informatics section began by examining nursing science, information science, and computer science and considering how each relates
to and helps one understand the concept of informatics. This chapter explores the building blocks of cognitive science, cognitive
informatics (CI), and artificial intelligence (AI).
Throughout the centuries, cognitive science has intrigued philosophers and educators alike. Beginning in Greece, the ancient
philosophers sought to comprehend how the mind works and what the nature of knowledge is. This age-old quest to unravel the
processes inherent in the working brain has been undertaken by some of the greatest minds in history. However, it was only about 50
years ago that computer operations and actions were linked to cognitive science, meaning theories of the mind, intellect, or brain. This
association led to the expansion of cognitive science to examine the complete array of cognitive processes, from lower-level
perceptions to higher-level critical thinking, logical analysis, and reasoning.
The focus of this chapter is the impact of cognitive science on nursing informatics (NI). This section provides the reader with an
introduction and overview of cognitive science, the nature of knowledge, wisdom, and AI as they apply to the Foundation of
Knowledge model and NI. The applications to NI include problem solving, decision support systems, usability issues, user-centered
interfaces and systems, and the development and use of terminologies.
Cognitive Science
The interdisciplinary field of cognitive science studies the mind, intelligence, and behavior from an information processing
perspective. According to Wikipedia (2013), “The term cognitive science was coined by Christopher Longuet-Higgins in his 1973
commentary on the Lighthill report, which concerned the then-current state of artificial intelligence research” (para. 36). The Cognitive
Science Society and the Cognitive Science Journal date back to 1980 (Cognitive Science Society, 2005). Their interdisciplinary base
arises from psychology, philosophy, neuroscience, computer science, linguistics, biology, and physics; covers memory, attention,
perception, reasoning, language, mental ability, and computational models of cognitive processes; and explores the nature of the mind,
knowledge representation, language, problem solving, decision making, and the social factors influencing the design and use of
technology. Simply put, cognitive science is the study of the mind and how information is processed in the mind. As described in the
Stanford Encyclopedia of Philosophy:
The central hypothesis of cognitive science is that thinking can best be understood in terms of representational structures in the
mind and computational procedures that operate on those structures. While there is much disagreement about the nature of the
representations and computations that constitute thinking, the central hypothesis is general enough to encompass the current
range of thinking in cognitive science, including connectionist theories which model thinking using artificial neural networks.
(2010, para. 9)
Connectionism is a component of cognitive science that uses computer modeling through artificial neural networks to explain
human intellectual abilities. Neural networks can be thought of as interconnected simple processing devices or simplified models of
the brain and nervous system that consist of a considerable number of elements or units (analogs of neurons) linked together in a
pattern of connections (analogs of synapses). A neural network that models the entire nervous system would have three types of units:
(1) input units (analogs of sensory neurons), which receive information to be processed; (2) hidden units (analogs to all of the other
neurons, not sensory or motor), which work in between input and output units; and (3) output units (analogs of motor neurons), where
the outcomes or results of the processing are found.
Connectionism is rooted in how computation occurs in the brain and nervous system or biologic neural networks. On their own,
single neurons have minimal computational capacity. When interconnected with other neurons, however, they have immense
computational power. The connectionism system or model learns by modifying the connections linking the neurons. Just as neurons
form elaborate information processing networks, so artificial neural networks are unique computer programs that model or simulate
their biologic analogs.
The mind is frequently compared to a computer, and experts in computer science strive to understand how the mind processes data
and information. In contrast, experts in cognitive science model human thinking using artificial networks provided by computers—an
endeavor sometimes referred to as AI. How does the mind process all of the inputs received? Which items and in which ways are
things stored or placed into memory, accessed, augmented, changed, reconfigured, and restored? Cognitive science provides the
scaffolding for the analysis and modeling of complicated, multifaceted human performance and has a tremendous effect on the issues
impacting informatics.
The end user is the focus of this activity because the concern is with enhancing the performance in the workplace; in nursing, the
end user could be the actual clinician in the clinical setting, and cognitive science can enhance the integration and implementation of
the technologies being designed to facilitate this knowledge worker with the ultimate goal of improving patient care. Technologies
change rapidly, and this evolution must be harnessed for the clinician at the bedside. To do this at all levels of nursing practice, one
must understand the nature of knowledge, the information and knowledge needed, and the means by which the nurse processes this
information and knowledge in the situational context.
Sources of Knowledge
Just as philosophers have questioned the nature of knowledge, so they have also strived to determine how knowledge arises, because
the origins of knowledge can help one understand its nature. How do people come to know what they know about themselves, others,
and their world? There are many viewpoints on this issue, both scientific and nonscientific.
According to Holt (2006), “There are two competing traditions concerning the ultimate source of our knowledge: empiricism and
rationalism” (para. 3). Empiricism is based on knowledge being derived from experiences or senses, whereas rationalism contends
that “some of our knowledge is derived from reason alone and that reason plays an important role in the acquisition of all of our
knowledge” (para. 5). Empiricists do not recognize innate knowledge, whereas rationalists believe that reason is more essential in the
acquisition of knowledge than the senses.
Three sources of knowledge have been identified: (1) instinct, (2) reason, and (3) intuition. Instinct is when one reacts without
reason, such as when a car is heading toward a pedestrian and he jumps out of the way without thinking. Instinct is found in both
humans and animals, whereas reason and intuition are found only in humans. Reason “[c]ollects facts, generalizes, reasons out from
cause to effect, from effect to cause, from premises to conclusions, from propositions to proofs” (Sivananda, 2004, para. 4). Intuition
is a way of acquiring knowledge that cannot be obtained by inference, deduction, observation, reason, analysis, or experience. Intuition
was described by Aristotle as “A leap of understanding, a grasping of a larger concept unreachable by other intellectual means, yet
fundamentally an intellectual process” (Shallcross & Sisk, 1999, para. 4).
Some believe that knowledge is acquired through perception and logic. Perception is the process of acquiring knowledge about the
environment or situation by obtaining, interpreting, selecting, and organizing sensory information from seeing, hearing, touching,
tasting, and smelling. Logic is “[a] science that deals with the principles and criteria of validity of inference and demonstration: the
science of the formal principles of reasoning” (Merriam-Webster Online Dictionary, 2007, para. 1). Acquiring knowledge through
logic requires reasoned action to make valid inferences.
The sources of knowledge provide a variety of inputs, throughputs, and outputs through which knowledge is processed. No matter
how one believes knowledge is acquired, it is important to be able to explain or describe those beliefs, communicate those thoughts,
enhance shared understanding, and discover the nature of knowledge.
Nature of Knowledge
Epistemology is the study of the nature and origin of knowledge—that is, what it means to know. Everyone has a conception of what
it means to know based on their own perceptions, education, and experiences; knowledge is a part of life that continues to grow with
the person. Thus a definition of knowledge is somewhat difficult to agree on because it reflects the viewpoints, beliefs, and
understandings of the person or group defining it. Some people believe that knowledge is part of a sequential learning process
resembling a pyramid, with data on the bottom, rising to information, then knowledge, and finally wisdom. Others believe that
knowledge emerges from interactions and experience with the environment, and still others think that it is religiously or culturally
bound. Knowledge acquisition is thought to be an internal process derived through thinking and cognition or an external process from
senses, observations, studies, and interactions. Descartes’s important premise “called ‘the way of ideas’ represents the attempt in
epistemology to provide a foundation for our knowledge of the external world (as well as our knowledge of the past and of other
minds) in the mental experiences of the individual” (Encyclopedia Britannica, 2007, para. 4).
For the purpose of this text, knowledge is defined as the awareness and understanding of a set of information and ways that
information can be made useful to support a specific task or arrive at a decision. It abounds with others’ thoughts and information or
consists of information that is synthesized so that relationships are identified and formalized.
How Knowledge and Wisdom Are Used in Decision Making
The reason for collecting and building data, information, and knowledge is to be able to make informed, judicious, prudent, and
intelligent decisions. When one considers the nature of knowledge and its applications, one must also examine the concept of wisdom.
Wisdom has been defined in numerous ways:
Knowledge applied in a practical way or translated into actions
The use of knowledge and experience to heighten common sense and insight to exercise sound judgment in practical matters
The highest form of common sense resulting from accumulated knowledge or erudition (deep, thorough learning) or
enlightenment (education that results in understanding and the dissemination of knowledge)
The ability to apply valuable and viable knowledge, experience, understanding, and insight while being prudent and sensible
Focused on our own minds
The synthesis of our experience, insight, understanding, and knowledge
The appropriate use of knowledge to solve human problems
In essence, wisdom entails knowing when and how to apply knowledge. The decision-making process revolves around knowledge
and wisdom. It is through efforts to understand the nature of knowledge and its evolution to wisdom that one can conceive of, build,
and implement informatics tools that enhance and mimic the mind’s processes to facilitate decision making and job performance.
Cognitive Informatics
Wang (2003) describes CI as an emerging transdisciplinary field of study that bridges the gap in understanding regarding how
information is processed in the mind and in the computer. Computing and informatics theories can be applied to help elucidate the
information processing of the brain, and cognitive and neurologic sciences can likewise be applied to build better and more efficient
computer processing systems. Wang suggests that the common issue among the human knowledge sciences is the drive to develop an
understanding of natural intelligence and human problem solving.
Pacific Northwest National Laboratory (PNNL), an organization operated on behalf of the U.S. Department of Energy; 2008,
suggests the disciplines of neuroscience, linguistics, AI, and psychology constitute this field. It defines CI as “the multidisciplinary
study of cognition and information sciences, which investigates human information processing mechanisms and processes and their
engineering applications in computing” (para. 1). CI helps to bridge this gap by systematically exploring the mechanisms of the brain
and mind and exploring specifically how information is acquired, represented, remembered, retrieved, generated, and communicated.
This dawning of understanding can then be applied and modeled in AI situations resulting in more efficient computing applications.
Wang explains further:
Cognitive informatics attempts to solve problems in two connected areas in a bidirectional and multidisciplinary approach. In
one direction, CI uses informatics and computing techniques to investigate cognitive science problems, such as memory,
learning, and reasoning; in the other direction, CI uses cognitive theories to investigate the problems in informatics,
computing, and software engineering. (p. 120)
CI and Nursing Practice
According to Mastrian (2008), the recognition of the potential application of principles of cognitive science to NI is relatively new.
The traditional and widely accepted definition of NI advanced by Graves and Corcoran (1989) is that NI is a combination of nursing
science, computer science, and information science used to describe the processes nurses use to manage data, information, and
knowledge in nursing practice. Turley (1996) proposed the addition of cognitive science to this mix, as nurse scientists are seen to
strive to capture and explain the influence of the human brain on data, information, and knowledge processing and to elucidate how
these factors in turn affect nursing decision making. The need to include cognitive sciences is imperative as researchers attempt to
model and support nursing decision making in complex computer programs.
In 2003, Wang proposed the term cognitive informatics to signify the branch of information and computer sciences that
investigates and explains information processing in the human brain. The science of CI grew out of interest in AI, as computer
scientists developed computer programs that mimic the information processing and knowledge generation functions of the human
brain. CI bridges the gap between artificial and natural intelligence and enhances the understanding of how information is acquired,
processed, stored, and retrieved so that these functions can be modeled in computer software.
What does this have to do with nursing? At its very core, nursing practice requires problem solving and decision making. Nurses
help people manage their responses to illnesses and identify ways that they can maintain or restore their health. During the nursing
process, nurses must first recognize that there is a problem to be solved, identify the nature of the problem, pull information from
knowledge stores that is relevant to the problem, decide on a plan of action, implement the plan, and evaluate the effectiveness of the
interventions. When a nurse has practiced the science of nursing for some time, he or she tends to do these processes automatically; it
is instinctively known what needs to be done to intervene in the problem. What happens, however, if the nurse faces a situation or
problem for which he or she has no experience on which to draw? The ever-increasing acuity and complexity of patient situations
coupled with the explosion of information in health care has fueled the development of decision support software for nursing. This
software models the human and natural decision-making processes of professionals in an artificial program. Such systems can help
decision makers to consider the consequences of different courses of action before implementing the action. They also provide stores
of information that the user may not be aware of and can use to choose the best course of action and ultimately make a better decision
in unfamiliar circumstances.
Decision support programs continue to evolve as research in the fields of cognitive science, AI, and CI is continuously generated
and then applied to the development of these systems. Nurses must embrace—not resist—these advances as support and enhancement
of the practice of nursing science.
What Is AI?
The field of AI deals with the conception, development, and implementation of informatics tools based on intelligent technologies.
This field captures the complex processes of human thought and intelligence.
Herbert Simon believes that the field of AI could have two functions: “One is to use the power of computers to augment human
thinking, just as we use motors to augment human or horse power. The other is to use a computer’s artificial intelligence to understand
how humans think in a humanoid way” (Association for the Advancement of Artificial Intelligence [AAAI], 2007a, para. 1).
According to the AAAI (2007b), AI is the “scientific understanding of the mechanisms underlying thought and intelligent behavior
and their embodiment in machines” (para. 2).
John McCarthy, one of the men credited with founding the field of AI in the 1950s, stated that AI “is the science and engineering
of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to
understand human intelligence, but AI does not have to confine itself to methods that are biologically observable” (AAAI, 2007b, para.
4).
Lamont (2007) interviewed Ray Kurzweil, a visionary who defined AI as “the ability to perform a task that is normally performed
by natural intelligence, particularly human natural intelligence. We have in fact artificial intelligence that can perform many tasks that
used to require—and could only be done by—human intelligence” (para. 6). The intelligence factor is extremely important in AI and
has been defined by McCarthy as “the computational part of the ability to achieve goals in the world. Varying kinds and degrees of
intelligence occur in people, many animals, and some machines” (AAAI, 2007b, para. 4).
The challenge of this field rests in capturing, mimicking, and creating the complex processes of the mind in informatics tools,
including software, hardware, and other machine technologies, with the goal that the tool be able to initiate and generate its own
mechanical thought processing. The brain’s processing is highly intricate and complicated. This complexity is reflected in Cohn’s
(2006) comment that “Artificial intelligence is 50 years old this summer, and while computers can beat the world’s best chess players,
we still can’t get them to think like a 4-year-old” (para. 1). AI uses cognitive science and computer science to replicate and generate
human intelligence. This field will continue to evolve and produce artificially intelligent tools to enhance nurses’ personal and
professional lives.
Summary
Cognitive science is the interdisciplinary field that studies the mind, intelligence, and behavior from an information processing
perspective. CI is a field of study that bridges the gap in understanding regarding how information is processed in the mind and in the
computer. Computing and informatics theories can be applied to help elucidate the information processing of the brain, and cognitive
and neurologic sciences can likewise be applied to build better and more efficient computer processing systems.
AI is the field that deals with the conception, development, and implementation of informatics tools based on intelligent
technologies. This field captures the complex processes of human thought and intelligence. AI uses cognitive science and computer
science to replicate and generate human intelligence.
The sources of knowledge, nature of knowledge, and rapidly changing technologies must be harnessed by clinicians to enhance
their bedside care. Therefore, we must understand the nature of knowledge, the information and knowledge needed, and the means by
which nurses process this information and knowledge in their own situational context. The reason for collecting and building data,
information, and knowledge is to be able to build wisdom—that is, the ability to apply valuable and viable knowledge, experience,
understanding, and insight while being prudent and sensible. Wisdom is focused on our own minds, the synthesis of our experience,
insight, understanding, and knowledge. Nurses must use their wisdom and make informed, judicious, prudent, and intelligent decisions
while providing care to patients, families, and communities. Cognitive science, CI, and AI will continue to evolve to help build
knowledge and wisdom.
THOUGHT-PROVOKING
QUESTIONS
1. How would you describe CI? Reflect on a plan of care that you have developed for a patient. How could CI be used to create tools to help with
this important work?
2. Think of a clinical setting with which you are familiar and envision which AI tools might be applied in this setting. Are there any current tools in
use? Which tools would enhance practice in this setting and why?
References
Association for the Advancement of Artificial Intelligence (AAAI). (2007a). AI overview. http://www.aaai.org/AITopics/html/overview.html
Association for the Advancement of Artificial Intelligence (AAAI). (2007b). Cognitive science.
http://www.aaai.org/aitopics/pmwiki/pmwiki.php/AITopics/CognitiveScience
Cognitive Science Society. (2005). CSJ archive. http://www.cogsci.rpi.edu/CSJarchive/1980v04/index.html
Cohn, D. (2006). AI reaches the golden years. http://www.wired.com/news/technology/0,71389–0.html
Encyclopedia Britannica. (2007). Epistemology. http://www.britannica.com/eb/article-247960/epistemology
Graves, J., & Corcoran, S. (1989). The study of nursing informatics. Image: Journal of Nursing Scholarship, 21(4), 227–230.
Holt, T. (2006). Sources of knowledge. http://www.theoryofknowledge.info/sourcesofknowledge.html
Lamont, I.(2007). The grill: Ray Kurzweil talks about “augmented reality” and the singularity. Retrieved October 2007 from
http://www.computerworld.com/action/article.do?command=viewArticleBasic&articleId=306176
Mastrian, K. (2008, February). Invited editorial: Cognitive informatics and nursing practice. Online Journal of Nursing Informatics, 12(1).
http://ojni.org/12_1/kathy.html
Merriam-Webster Online Dictionary. (2007). Logic. http://www.merriam-webster.com/dictionary/logic
Pacific Northwest National Laboratory, U.S. Department of Energy. (2008). Cognitive informatics. http://www.pnl.gov/coginformatics/
Shallcross, D. J., & Sisk, D. A. (1999). What is intuition? In T. Arnold (Ed.), Hyponoesis glossary: Intuition.
http://www.hyponoesis.org/Glossary/Definition/Intuition
Sivananda, S. (2004). Four sources of knowledge. http://www.dlshq.org/messages/knowledge.htm
Stanford Encyclopedia of Philosophy. (2010). Cognitive science. http://plato.stanford.edu/entries/cognitive-science/
Turley, J. (1996). Toward a model for nursing informatics. Image: Journal of Nursing Scholarship, 28(4), 309–313.
Wang, Y. (2003). Cognitive informatics: A new transdisciplinary research field. Brain and Mind, 4(2), 115–127.
Wikipedia. (2013). Cognitive science. http://en.wikipedia.org/wiki/Cognitive_science
Chapter 5
Ethical Applications of Informatics
Kathleen Mastrian, Dee McGonigle, and Nedra Farcus
OBJECTIVES
1. Recognize ethical dilemmas in nursing informatics.
2. Examine ethical implications of nursing informatics.
3. Evaluate professional responsibilities for the ethical use of healthcare informatics technology.
4. Explore the ethical model for ethical decision making.
5. Analyze practical ways of applying the ethical model for ethical decision making to manage ethical dilemmas in nursing informatics.
Key Terms
Alternatives
Antiprinciplism
Application (app)
Autonomy
Beneficence
Bioethics
Bioinformatics
Care ethics
Casuist approach
Confidentiality
Consequences
Courage
Decision making
Decision support
Duty
Ethical decision making
Ethical dilemma
Ethical, social, and legal
implications
Ethicist
Ethics
Eudaemonistic
Fidelity
Good
Google Glass
Harm
Justice
Liberty
Moral dilemmas
Moral rights
Morals
Negligence
Nicomachean
Nonmaleficence
Principlism
Privacy
Rights
Security
Self-control
Smartphones
Social media
Standard
Truth
Uncertainty
Values
Veracity
Virtue
Virtue ethics
Wisdom
Introduction
Those who followed the actual events of Apollo 13, or who were entertained by the movie (Howard, 1995), watched the astronauts
strive against all odds to bring their crippled spaceship back to Earth. The speed of their travel was incomprehensible to most viewers,
and the task of bringing the spaceship back to Earth seemed nearly impossible. They were experiencing a crisis never imagined by the
experts at NASA, and they made up their survival plan moment by moment. What brought them back to Earth safely? Surely, credit
must be given to the technology and the spaceship’s ability to withstand the trauma it experienced. Most amazing, however, were the
traditional nontechnological tools, skills, and supplies that were used in new and different ways to stabilize the spacecraft’s
environment and keep the astronauts safe while traveling toward their uncertain future.
This sense of constancy in the midst of change serves to stabilize experience in many different life events and contributes to the
survival of crisis and change. This rhythmic process is also vital to the healthcare system’s stability and survival in the presence of the
rapidly changing events of the Knowledge Age. No one can dispute the fact that the Knowledge Age is changing health care in ways
that will not be fully recognized and understood for years. The change is paradigmatic, and every expert who addresses this change
reminds healthcare professionals of the need to go with the flow of rapid change or be left behind.
As with any paradigm shift, a new way of viewing the world brings with it some of the enduring values of the previous worldview.
As health care journeys into the brave new world of digital communications, it brings some familiar tools and skills recognized in the
form of values, such as privacy, confidentiality, autonomy, and nonmaleficence. Although these basic values remain unchanged, the
standards for living out these values will take on new meaning as health professionals confront new and different moral dilemmas
brought on by the adoption of technological tools for information management and knowledge development. Ethical decision-making
frameworks will remain constant, but the context for examining these moral issues or ethical dilemmas will become increasingly
complex.
This chapter highlights some familiar ethical concepts to consider on the challenging journey into the increasingly complex future
of healthcare informatics. Ethics and bioethics are briefly defined, and the evolution of ethical approaches from the Hippocratic ethic
era, to principlism, to the current antiprinciplism movement of ethical decision making is examined. New and challenging ethical
dilemmas are surfacing in the venture into the unfolding era of healthcare informatics. Also presented in this chapter are findings from
some of the more recent literature related to these issues. Readers are challenged to think constantly and carefully about ethics as they
become involved in healthcare informatics and to stay abreast of new developments in ethical approaches.
Ethics
Ethics is a process of systematically examining varying viewpoints related to moral questions of right and wrong. Ethicists have
defined the term in a variety of ways, with each reflecting a basic theoretical philosophic perspective.
Beauchamp and Childress (1994) refer to ethics as a generic term for various ways of understanding and examining the moral life.
Ethical approaches to this examination may be normative, presenting standards of right or good action; descriptive, reporting what
people believe and how they act; or explorative, analyzing the concepts and methods of ethics.
Husted and Husted (1995) emphasize a practice-based ethics, stating “ethics examines the ways men and women can exercise their
power in order to bring about human benefit—the ways in which one can act in order to bring about the conditions of happiness” (p.
3).
Velasquez, Andre, Shanks, and Myer (1987) posed the question, “What is ethics?”, and answered it with the following two-part
response: “First, ethics refers to well-based standards of right and wrong that prescribe what humans ought to do, usually in terms of
rights, obligations, benefits to society, fairness, or specific virtues” (para. 10), and “Secondly, ethics refers to the study and
development of one’s ethical standards” (para. 11).
Regardless of the theoretical definition, common characteristics regarding ethics are its dialectical, goal-oriented approach to
answering questions that have the potential for multiple acceptable answers.
Bioethics
Bioethics is defined as the study and formulation of healthcare ethics. Bioethics takes on relevant ethical problems experienced by
healthcare providers in the provision of care to individuals and groups. Husted and Husted (1995) state the fundamental background of
bioethics that forms its essential nature is:
1. The nature and needs of humans as living, thinking beings
2. The purpose and function of the healthcare system in a human society
3. An increased cultural awareness of human beings’ essential moral status (p. 7)
Bioethics emerged in the 1970s as health care began to change its focus from a mechanistic approach of treating disease to a more
holistic approach of treating people with illnesses. As technology advanced, recognition and acknowledgment of the rights and the
needs of individuals and groups receiving this high-tech care also increased.
In today’s technologically savvy healthcare environment, patients are being prescribed applications (also known simply as apps)
for their smartphones instead of medications in some clinical practices. Patients’ smartphones are being used to interact with them in
new ways and to monitor and assess their health in some cases. With apps and add-ons, for example, a provider can see the patient’s
ECG immediately, or the patient can monitor his or her ECG and send it to the provider as necessary. A sensor attached to the patient’s
mobile device could monitor blood glucose levels.
We are just beginning to realize the vast potential of these mobile devices—and the threats they sometimes pose. Google Glass, for
example, can take photos and videos (Stern, 2013) without anyone knowing that this is occurring; in the healthcare environment, such
a technological advancement can violate patients’ privacy and confidentiality. Add these evolving developments to healthcare
providers’ engagement in social media use with their patients, and it becomes clear that personal and ethical dilemmas abound for
nurses in the new über-connected world.
Ethical Issues and Social Media
As connectivity has improved owing to emerging technologies, a rapid explosion in the phenomenon known as social media has
occurred. Social media are defined as “a group of Internet-based applications that build on the ideological and technological
foundations of Web 2.0 and that allow the creation and exchange of user-generated content” (Spector & Kappel, 2012, p. 1). Just as the
electronic health record serves as a real-time event in recording patient–provider contact, so the use of social media represents an
instantaneous form of communication. Healthcare providers—particularly nurses—can enhance the patient care delivery system,
promote professional collegiality, and provide timely communication and education regarding health-related matters by using this
forum (“White paper,” 2011, p. 1). In all cases, however, nurses must exercise judicious use of social media to protect patients’ rights.
Nurses must understand their obligation to their chosen profession, particularly as it relates to personal behavior and the perceptions of
their image as portrayed through social media. Above all, nurses must be mindful that once communication is written and posted on
the Internet, there is no way to retract what was written; it is a permanent record that can be tracked, even if the post is deleted
(Englund, Chappy, Jambunathan, & Gohdes, 2012, p. 242).
Social media platforms include such electronic communication outlets as Facebook, Twitter, LinkedIn, and YouTube. Other
widely used means of instantaneous communications include wikis, blogs, tweeting, Skype, and the “hangout” on Google+. Even as
recently as 5 years ago, some of these means of exchanging information were unknown (Spector & Kappel, 2012, p. 1).
Use of social networking has increased dramatically among all age groups, including a 78% increase in use among the 50- to 64-
year-old age group, and a 42% increase in use among persons older than 65 years over a time frame of a little more than 3 years.
Facebook reported in June 2012 that it had 955 million active monthly users. Twitter’s influence on health care is suggested by the fact
that more than 100 million pieces of healthcare information have been tweeted, with as many as 140 million tweets being recorded in a
day’s time (Prasad, 2013, p. 492). Moreover, people spend more than 700 billion minutes per month actively engaged with the
Facebook site (Miller, 2011, p. 307).
The rapid growth of social media has found many healthcare professionals unprepared to face the new challenges or to exploit the
opportunities that exist with these forums. The need to maintain confidentiality presents a major obstacle to the healthcare industry’s
widespread adoption of such technology; thus social networking has not yet been fully embraced by many health professionals
(Anderson, 2012, p. 1). Englund and colleagues (2012) note that undergraduate nursing students may face ambiguous and understudied
professional and ethical implications when using social networking venues.
Another confounding factor is the increased use of mobile devices by health professionals as well as the public (Swartz, 2011, p.
345). The mobile device known as the smartphone has the capability to take still pictures as well as make live recordings; it has found
its way into treatment rooms around the globe.
As a consequence of more stringent confidentiality laws and more widespread availability and use of social and mobile media,
numerous ethical and legal dilemmas have been posed to nurses. What are not well defined are the expectations of healthcare providers
regarding this technology. In some cases, nurses employed in the emergency department (ED) setting have been subjected to video and
audio recordings by patients and families when they perform procedures and give care during the ED visit. Nurses would be wise to
inquire—before an incident occurs—about the hospital policy regarding audio/video recording by patients and families, as well as the
state laws governing two-party consent laws. Such laws require consent of all parties to any recording or eavesdropping activity
(Lyons & Reinisch, 2013, p. 54).
Sometimes the enthusiasm for patient care and learning can lead to ethics violations. In one case, an inadvertent violation of
privacy laws occurred when a nurse in a small town blogged about a child in her care whom she referred to as her “little handicapper.”
The post also noted the child’s age and the fact that the child used a wheelchair. A complaint about this breach of confidentiality was
reported to the Board of Nursing. A warning was issued to the nurse blogging this information, although a more stringent disciplinary
action could have been taken (Spector & Kappel, 2012, p. 2).
In another case cited by Spector and Kappel (2012), a student nurse cared for a 3-year-old leukemia patient whom she wanted to
remember after finishing her pediatric clinical experience. She took the child’s picture, and in the background of the photo the patient’s
room number was clearly displayed. The child’s picture was posted on the student nurse’s Facebook page, along with her statement of
how much she cared about this child and how proud she was to be a student nurse. Someone forwarded the picture to the nurse
supervisor of the children’s hospital. Not only was the student expelled from the program, but the clinical site offer made by the
children’s hospital to the nursing school was rescinded. In addition, the hospital faced citations for violations of the Health Insurance
Portability and Accountability Act (HIPAA) owing to the student nurse’s transgression (p. 3).
A white paper published by the National Council of State Boards of Nursing (NCSBN; White paper, 2011) provides a thorough
discussion of the issues associated with nurses’ use of social media.
Ethical Dilemmas and Morals
Ethical dilemmas arise when moral issues raise questions that cannot be answered with a simple, clearly defined rule, fact, or
authoritative view. Morals refer to social convention about right and wrong human conduct that is so widely shared that it forms a
stable (although usually incomplete) communal consensus (Beauchamp & Childress, 1994). Moral dilemmas arise with uncertainty,
as is the case when some evidence a person is confronted with indicates an action is morally right and other evidence indicates that this
action is morally wrong. Uncertainty is stressful and, in the face of inconclusive evidence on both sides of the dilemma, causes the
person to question what he or she should do. Sometimes the individual concludes that based on his or her moral beliefs, he or she
cannot act. Uncertainty also arises from unanticipated effects or unforeseeable behavioral responses to actions or the lack of action.
Adding uncertainty to the situational factors and personal beliefs that must be considered creates a need for an ethical decision-making
model to help one choose the best action.
Ethical Decision Making
Ethical decision making refers to the process of making informed choices about ethical dilemmas based on a set of standards
differentiating right from wrong. This type of decision making reflects an understanding of the principles and standards of ethical
decision making, as well as the philosophic approaches to ethical decision making, and it requires a systematic framework for
addressing the complex and often controversial moral questions.
Research Brief
Using an online survey of 1,227 randomly selected respondents, Bodkin and Miaoulis (2007) sought to describe the characteristics of information
seekers on e-health websites, the types of information they seek, and their perceptions of the quality and ethics of the websites. Of the respondents,
74% had sought health information on the Web, with women accounting for 55.8% of the health information seekers. A total of 50% of the seekers
were between 35 and 54 years of age. Nearly two thirds of the users began their searches using a general search engine rather than a health-specific
site, unless they were seeking information related to symptoms or diseases. Top reasons for seeking information were related to diseases or
symptoms of medical conditions, medication information, health news, health insurance, locating a doctor, and Medicare or Medicaid information.
The level of education of information seekers was related to the ratings of website quality, in that more educated seekers found health information
websites more understandable, but were more likely to perceive bias in the website information. The researchers also found that the ethical codes for
e-health websites seem to be increasing consumers’ trust in the safety and quality of information found on the Web, but that most consumers are not
comfortable purchasing health products or services online.
Source: The full article appears in Bodkin, C., & Miaoulis, G. (2007). eHealth information quality and ethics issues: An exploratory study of
consumer perceptions. International Journal of Pharmaceutical and Healthcare Marketing, 1(1), 27–42. Retrieved from ABI/INFORM Global
(Document ID: 1515583081).
As the high-speed era of digital communications evolves, the rights and the needs of individuals and groups will be of the utmost
concern to all healthcare professionals. The changing meaning of communication, for example, will bring with it new concerns among
healthcare professionals about protecting patients’ rights of confidentiality, privacy, and autonomy. Systematic and flexible ethical
decision-making abilities will be essential for all healthcare professionals.
Notably, the concept of nonmaleficence (“do no harm”) will be broadened to include those individuals and groups whom one may
never see in person, but with whom one will enter into a professional relationship of trust and care. Mack (2000) has discussed the
popularity of individuals seeking information online instead of directly from their healthcare providers and the effects this behavior has
on patient–provider relationships. He is emphatic in his reminder that “organizations and individuals that provide health information
on the Internet have obligations to be trustworthy, provide high-quality content, protect users’ privacy, and adhere to standards of best
practices for online commerce and online professional services in healthcare” (p. 41).
Makus (2001) suggests that both autonomy and justice are enhanced with universal access to information, but that tensions may be
created in patient–provider relationships as a result of this access to outside information. Healthcare workers need to realize that they
are no longer the sole providers and gatekeepers of health-related information; ideally, they should embrace information empowerment
and suggest websites to patients that contain reliable, accurate, and relevant information (Resnick, 2001).
It is clear that patients’ increasing use of the Internet for healthcare information may prompt entirely new types of ethical issues,
such as who is responsible if a patient is harmed as a result of following online health advice. Derse and Miller (2008) discuss this
issue extensively and conclude that a clear line separates information and practice. Practice occurs when there is direct or personal
communication between the provider and the patient, when the advice is tailored to the patient’s specific health issue, and when there
is a reasonable expectation that the patient will act in reliance on the information.
A summit sponsored by the Internet Healthcare Coalition (www.ihealthcoalition.org) in 2000 developed the E-Health Code of
Ethics (eHealth code, n.d.), which includes eight standards for the ethical development of health-related Internet sites: (1) candor, (2)
honesty, (3) quality, (4) informed consent, (5) privacy, (6) professionalism, (7) responsible partnering, and (8) accountability. For
more information about each of these standards, access the full discussion of the E-Health Code of Ethics
(http://www.ihealthcoalition.org/ehealth-code/).
It is important to realize that the standards for ethical development of health-related Internet sites are voluntary; there is no
overseer perusing these sites and issuing safety alerts for users. Although some sites carry a specific symbol indicating that they have
been reviewed and are trustworthy (HONcode and Trust-e), the healthcare provider cannot control which information patients access
or how they perceive and act related to the health information they find online. The research brief on the previous page describes one
study of consumer perceptions of health information on the Web.
Theoretical Approaches to Healthcare Ethics
Theoretical approaches to healthcare ethics have evolved in response to societal changes. In a 30-year retrospective article for the
Journal of the American Medical Association, Pellegrino (1993) traced the evolution of healthcare ethics from the Hippocratic ethic, to
http://www.ihealthcoalition.org
http://www.ihealthcoalition.org/ehealth-code/
principlism, to the current antiprinciplism movement.
The Hippocratic tradition emerged from relatively homogenous societies where beliefs were similar and most societal members
shared common values. The emphasis was on duty, virtue, and gentlemanly conduct.
Principlism arose as societies became more heterogeneous and members began experiencing a diversity of incompatible beliefs
and values; it emerged as a foundation for ethical decision making. Principles were expansive enough to be shared by all rational
individuals, regardless of their background and individual beliefs. This approach continued into the 1900s and was popularized by two
bioethicists, Beauchamp and Childress (1977, 1994), in the last quarter of the 20th century. Principles are considered broad guidelines
that provide guidance or direction but leave substantial room for case-specific judgment. From principles, one can develop more
detailed rules and policies.
Beauchamp and Childress (1994) proposed four guiding principles: (1) respect for autonomy, (2) nonmaleficence, (3) beneficence,
and (4) justice.
Autonomy refers to the individual’s freedom from controlling interferences by others and from personal limitations that prevent
meaningful choices, such as adequate understanding. Two conditions are essential for autonomy: liberty, meaning the
independence from controlling influences, and the individual’s capacity for intentional action.
Nonmaleficence asserts an obligation not to inflict harm intentionally and forms the framework for the standard of due care to
be met by any professional. Obligations of nonmaleficence are obligations of not inflicting harm and not imposing risks of
harm. Negligence—a departure from the standard of due care toward others—includes intentionally imposing risks that are
unreasonable and unintentionally but carelessly imposing risks.
Beneficence refers to actions performed that contribute to the welfare of others. Two principles underlie beneficence: Positive
beneficence requires the provision of benefits, and utility requires that benefits and drawbacks be balanced. One must avoid
negative beneficence, which occurs when constraints are placed on activities that, even though they might not be unjust, could
in some situations cause detriment or harm to others.
Justice refers to fair, equitable, and appropriate treatment in light of what is due or owed to a person. Distributive justice refers
to fair, equitable, and appropriate distribution in society determined by justified norms that structure the terms of social
cooperation.
Beauchamp and Childress also suggest three types of rules for guiding actions: substantive, authority, and procedural. (Rules are
more restrictive in scope than principles and are more specific in content.) Substantive rules are rules of truth telling, confidentiality,
privacy, and fidelity, and those pertaining to the allocation and rationing of health care, omitting treatment, physician-assisted suicide,
and informed consent. Authority rules indicate who may and should perform actions. Procedural rules establish procedures to be
followed.
The principlism advocated by Beauchamp and Childress has since given way to the antiprinciplism movement, which emerged in
the 21st century with the expansive technological changes and the tremendous rise in ethical dilemmas accompanying these changes.
Opponents of principlism include those who claim that its principles do not represent a theoretical approach as well as those who claim
that its principles are too far removed from the concrete particularities of everyday human existence; are too conceptual, intangible, or
abstract; or disregard or do not take into account a person’s psychological factors, personality, life history, sexual orientation, or
religious, ethnic, and cultural background. Different approaches to making ethical decisions are next briefly explored, providing the
reader with an understanding of the varied methods professionals may use to arrive at an ethical decision.
The casuist approach to ethical decision making grew out of the call for more concrete methods of examining ethical dilemmas.
Casuistry is a case-based ethical reasoning method that analyzes the facts of a case in a sound, logical, and ordered or structured
manner. The facts are compared to decisions arising out of consensus in previous paradigmatic or model cases. One casuist proponent,
Jonsen (1991), prefers particular and concrete paradigms and analogies over the universal and abstract theories of principlism.
The Husted bioethical decision-making model centers on the healthcare professional’s implicit agreement with the patient or client
(Husted & Husted, 1995). It is based on six contemporary bioethical standards: (1) autonomy, (2) freedom, (3) veracity, (4) privacy,
(5) beneficence, and (6) fidelity.
The virtue ethics approach emphasizes the virtuous character of individuals who make the choices. A virtue is any characteristic
or disposition desired in others or oneself. It is derived from the Greek word aretai, meaning “excellence,” and refers to what one
expects of oneself and others. Virtue ethicists emphasize the ideal situation and attempt to identify and define ideals. Virtue ethics
dates back to Plato and Socrates. When asked “whether virtue can be taught or whether virtue can be acquired in some other way,
Socrates answers that if virtue is knowledge, then it can be taught. Thus, Socrates assumes that whatever can be known can be taught”
(Scott, 2002, para. 9). According to this view, the cause of any moral weakness is not a matter of character flaws but rather a matter of
ignorance. In other words, a person acts immorally because the individual does not know what is really good for him or her. A person
can, for example, be overpowered by immediate pleasures and forget to consider the longterm consequences. Plato emphasized that to
lead a moral life and not succumb to immediate pleasures and gratification, one must have a moral vision. He identified four cardinal
virtues: (1) wisdom, (2) courage, (3) self-control, and (4) justice.
Aristotle’s Nicomachean principles (Aristotle, 350 BC) also contribute to virtue ethics. According to this philosopher, virtues are
connected to will and motive because the intention is what determines if one is or is not acting virtuously. Ethical considerations,
according to his eudaemonistic principles, address the question, “What is it to be an excellent person?” For Aristotle, this ultimately
means acting in a temperate manner according to a rational mean between extreme possibilities.
Virtue ethics has experienced a recent resurgence in popularity (Healthcare Ethics, 2007). Two of the most influential moral and
medical authors, Pellegrino and Thomasma (1993), have maintained that virtue theory should be related to other theories within a
comprehensive philosophy of the health professions. They argue that moral events are composed of four elements (the agent, the act,
the circumstances, and the consequences), and state that a variety of theories must be interrelated to account for different facets of
moral judgment.
Care ethics is responsiveness to the needs of others that dictates providing care, preventing harm, and maintaining relationships.
This viewpoint has been in existence for some time. Engster (n.d.) states that “Carol Gilligan’s In a Different Voice (1982) established
care ethics as a major new perspective in contemporary moral and political discourse” (p. 2). The relationship between care and virtue
is complex, however. Benjamin and Curtis (1992) base their framework on care ethics; they propose that “critical reflection and
inquiry in ethics involves the complex interplay of a variety of human faculties, ranging from empathy and moral imagination on the
one hand to analytic precision and careful reasoning on the other” (p. 12). Care ethicists are less stringently guided by rules, but rather
focus on the needs of others and the individual’s responsibility to meet those needs. As opposed to the aforementioned theories that are
centered on the individual’s rights, an ethic of care emphasizes the personal part of an interdependent relationship that affects how
decisions are made. In this theory, the specific situation and context in which the person is embedded become a part of the decision-
making process.
The consensus-based approach to bioethics was proposed by Martin (1999), who claims that American bioethics harbors a variety
of ethical methods that emphasize different ethical factors, including principles, circumstances, character, interpersonal needs, and
personal meaning. Each method reflects an important aspect of ethical experience, adds to the others, and enriches the ethical
imagination. Thus working with these methods provides the challenge and the opportunity necessary for the perceptive and shrewd
bioethicist to transform them into something new with value through the process of building ethical consensus. Diverse ethical insights
can be integrated to support a particular bioethical decision, and that decision can be understood as a new, ethical whole.
Applying Ethics to Informatics
With the Knowledge Age has come global closeness, meaning the ability to reach around the globe instantaneously through
technology. Language barriers are being broken through technologically based translators that can enhance interaction and exchange of
data and information. Informatics practitioners are bridging continents, and international panels, committees, and organizations are
beginning to establish standards and rules for the implementation of informatics. This international perspective must be taken into
consideration when informatics dilemmas are examined from an ethical standpoint; it promises to influence the development of ethical
approaches that begin to accept that healthcare practitioners are working within international networks and must recognize, respect,
and regard the diverse political, social, and human factors within informatics ethics.
The various ethical approaches can be used to help healthcare professionals make ethical decisions in all areas of practice. The
focus of this text is on informatics. Informatics theory and practice have continued to grow at a rapid rate and are infiltrating every area
of professional life. New applications and ways of performing skills are being developed daily. Therefore, education in informatics
ethics is extremely important.
Typically, situations are analyzed using past experience and in collaboration with others. Each situation warrants its own
deliberation and unique approach, because each individual patient seeking or receiving care has his or her own preferences, quality of
life, and healthcare needs in a situational milieu framed by financial, provider, setting, institutional, and social context issues.
Clinicians must take into consideration all of these factors when making ethical decisions.
The use of expert systems, decision support tools, evidence-based practice, and artificial intelligence in the care of patients creates
challenges in terms of who should use these tools, how they are implemented, and how they are tempered with clinical judgment. All
clinical situations are not the same, and even though the result of interacting with these systems and tools is enhanced information and
knowledge, the clinician must weigh this information in light of each patient’s unique clinical circumstances, including that
individual’s beliefs and wishes. Patients are demanding access to quality care and the information necessary to control their lives.
Clinicians need to analyze and synthesize the parameters of each distinctive situation using a specific decision-making framework that
helps them make the best decisions. Getting it right the first time has a tremendous impact on expected patient outcomes. The focus
should remain on patient outcomes while the informatics tools available are ethically incorporated.
Facing ethical dilemmas on a daily basis and struggling with unique client situations may cause many clinicians to question their
own actions and the actions of their colleagues and patients. One must realize that colleagues and patients may reach very different
decisions, but that does not mean anyone is wrong. Instead, all parties reach their ethical decision based on their own review of the
situational facts and understanding of ethics. As one deals with diversity among patients, colleagues, and administrators, one must
constantly strive to use ethical imagination to reach ethically competent decisions.
Balancing the needs of society, his or her employer, and patients could cause the clinician to face ethical challenges on an everyday
basis. Society expects judicious use of finite healthcare resources. Employers have their own policies, standards, and practices that can
sometimes inhibit the practice of the clinician. Each patient is unique and has life experiences that affect his or her healthcare
perspective, choices, motivation, and adherence. Combine all of these factors with the challenges posed by informatics, and it is clear
that the evolving healthcare arena calls for an informatics-competent, politically active, consumer-oriented, business-savvy, ethical
clinician to rule this ever-changing landscape known as health care.
The goal of any ethical system should be that a rational, justifiable decision is reached. Ethics is always there to help the
practitioner decide what is right. Indeed, the measure of an adequate ethical system or theory or approach is, in part, its ability to be
useful in novel contexts. A comprehensive, robust theory of ethics should be up to the task of addressing a broad variety of new
applications and challenges at the intersection of informatics and health care.
The information concerning an ethical dilemma must be viewed in the context of the dilemma to be useful. Bioinformatics could
gather, manipulate, classify, analyze, synthesize, retrieve, and maintain databases related to ethical cases, the effective reasoning
applied to various ethical dilemmas, and the resulting ethical decisions. This input would certainly be potent—but the resolution of
dilemmas cannot be achieved simply by examining relevant cases from a database. Instead, clinicians must assess each situational
context and the patient’s specific situation and needs and make their ethical decisions based on all of the information they have at
hand.
Ethics is exciting, and competent clinicians need to know about ethical dilemmas and solutions in their professions. Ethicists have
often been thought of as experts in the arbitrary, ambiguous, and ungrounded judgments of other people. They know that they make
the best decisions they can based on the situation and stakeholders at hand. Just as clinicians try to make the best healthcare decisions
with and for their patients, ethically driven practitioners must do the same. Each healthcare provider must critically think through the
situation to arrive at the best decision.
To make ethical decisions about informatics technologies and patients’ intimate healthcare data and information, the healthcare
provider must be competent in informatics. To the extent that information technology is reshaping healthcare practices or promises to
improve patient care, healthcare professionals must be trained and competent in the use of these tools. This competency needs to be
evaluated through instruments developed by professional groups or societies; such assessment will help with consistency and quality.
For the healthcare professional to be an effective patient advocate, he or she must understand how information technology affects the
patient and the subsequent delivery of care. Information science and its effects on health care are both interesting and important. It
follows that information technology and its ethical, social, and legal implications should be incorporated into all levels of
professional education.
The need for confidentiality was perhaps first articulated by Hippocrates; thus, if anything is different in today’s environment, it is
simply the ways in which confidentiality can be violated. Perhaps the use of computers for clinical decision support and data mining in
research will raise new ethical issues. Ethical dilemmas associated with the integration of informatics must be examined to provide an
ethical framework that considers all of the stakeholders. Patients’ rights must be protected in the face of a healthcare provider’s duty to
his or her employer and society at large when initiating care and assigning finite healthcare resources. An ethical framework is
necessary to help guide healthcare providers in reference to the ethical treatment of electronic data and information during all stages of
collection, storage, manipulation, and dissemination. These new approaches and means come with their own ethical dilemmas. Often
they are dilemmas not yet faced owing to the cutting-edge nature of these technologies.
Just as processes and models are used to diagnose and treat patients in practice, so a model in the analysis and synthesis of ethical
dilemmas or cases can also be applied. An ethical model for ethical decision making (Box 5-1) facilitates the ability to analyze the
dilemma and synthesize the information into a plan of action (McGonigle, 2000). The model presented here is based on the letters in
the word ethical. Each letter guides and prompts the healthcare provider to think critically (think and rethink) through the situation
presented. The model is a tool because, in the final analysis, it allows the nurse objectively to ascertain the essence of the dilemma and
develop a plan of action.
BOX 5-1 ETHICAL MODEL FOR ETHICAL DECISION MAKING
Examine the ethical dilemma (conflicting values exist).
Thoroughly comprehend the possible alternatives available.
Hypothesize ethical arguments.
Investigate, compare, and evaluate the arguments for each alternative.
Choose the alternative you would recommend.
Act on your chosen alternative.
Look at the ethical dilemma and examine the outcomes while reflecting on the ethical decision.
APPLYING THE ETHICAL MODEL
Examine the ethical dilemma:
Use your problem-solving, decision-making, and critical-thinking skills.
What is the dilemma you are analyzing? Collect as much information about the dilemma as you can, making sure to gather the relevant facts that
clearly identify the dilemma. You should be able to describe the dilemma you are analyzing in detail.
Ascertain exactly what must be decided.
Who should be involved in the decision-making process for this specific case?
Who are the interested players or stakeholders?
Reflect on the viewpoints of these key players and their value systems.
What do you think each of these stakeholders would like you to decide as a plan of action for this dilemma?
How can you generate the greatest good?
Thoroughly comprehend the possible alternatives available:
Use your problem-solving, decision-making, and critical-thinking skills.
Create a list of the possible alternatives. Be creative when developing your alternatives. Be open minded; there is more than one way to reach a
goal. Compel yourself to discern at least three alternatives.
Clarify the alternatives available and predict the associated consequences—good and bad—of each potential alternative or intervention.
For each alternative, ask the following questions:
Do any of the principles or rules, such as legal, professional, or organizational, automatically nullify this alternative?
If this alternative is chosen, what do you predict as the best-case and worst-case scenarios?
Do the best-case outcomes outweigh the worst-case outcomes?
Could you live with the worst-case scenario?
Will anyone be harmed? If so, how will they be harmed?
Does the benefit obtained from this alternative overcome the risk of potential harm that it could cause to anyone?
Hypothesize ethical arguments:
Use your problem-solving, decision-making, and critical-thinking skills.
Determine which of the five approaches apply to this dilemma.
Identify the moral principles that can be brought into play to support a conclusion as to what ought to be done ethically in this case or similar
cases.
Ascertain whether the approaches generate converging or diverging conclusions about what ought to be done.
Investigate, compare, and evaluate the arguments for each alternative:
Use your problem-solving, decision-making, and critical-thinking skills.
Appraise the relevant facts and assumptions prudently.
Is there ambiguous information that must be evaluated?
Are there any unjustifiable factual or illogical assumptions or debatable conceptual issues that must be explored?
Rate the ethical reasoning and arguments for each alternative in terms of their relative significance.
4 = extreme significance
3 = major significance
2 = significant
1 = minor significance
Compare and contrast the alternatives available with the values of the key players involved.
Reflect on these alternatives:
Does each alternative consider all of the key players?
Does each alternative take into account and reflect an interest in the concerns and welfare of all of the key players?
Which alternative will produce the greatest good or the least amount of harm for the greatest number of people?
Refer to your professional codes of ethical conduct. Do they support your reasoning?
Choose the alternative you would recommend:
Use your problem-solving, decision-making, and critical-thinking skills.
Make a decision about the best alternative available.
Remember the Golden Rule: Does your decision treat others as you would want to be treated?
Does your decision take into account and reflect an interest in the concerns and welfare of all of the key players?
Does your decision maximize the benefit and minimize the risk for everyone involved?
Become your own critic; challenge your decision as you think others might. Use the ethical arguments you predict they would use and defend
your decision.
Would you be secure enough in your ethical decision-making process to see it aired on national television or sent out globally over the
Internet?
Are you secure enough with this ethical decision that you could have allowed your loved ones to observe your decision-making process, your
decision, and its outcomes?
Act on your chosen alternative:
Use your problem-solving, decision-making, and critical-thinking skills.
Formulate an implementation plan delineating the execution of the decision.
This plan should be designed to maximize the benefits and minimize the risks.
This plan must take into account all of the resources necessary for implementation, including personnel and money.
Implement the plan.
Look at the ethical dilemma and examine the outcomes while reflecting on your ethical decision:
Use your problem-solving, decision-making, and critical-thinking skills.
Monitor the implementation plan and its outcomes. It is extremely important to reflect on specific case decisions and evaluate their outcomes to
develop your ethical decision-making ability.
If new information becomes available, the plan must be reevaluated.
Monitor and revise the plan as necessary.
Source: The ethical model for ethical decision making was developed by Dr. Dee McGonigle and is the property of Educational Advancement
Associates (EAA). The permission for its use in this text has been granted by Mr. Craig R. Goshow, Vice President, EAA.
Case Analysis Demonstration
The following case study is intended to help readers think through how to apply the ethical model. Review the model and then read
through the case. Try to apply the model to this case or follow along as the model is implemented. Readers are challenged to determine
their decision in this case and then compare and contrast their response with the decision the authors reached. Several more case
studies presented for practice in implementing the ethical model for ethical decision making are available on the companion website
for this text (http://nursing.jbpub.com/informatics).
Allison is a charge nurse on a busy medical–surgical unit. She is expecting the clinical instructor from the local university at
2:00 pm to review and discuss potential patient assignments for the nursing students scheduled for the following day. Just as
the university professor arrives, one of the patients on the unit develops a crisis requiring Allison’s attention. To expedite the
student nurse assignments for the following day, Allison gives her electronic medical record access password to the instructor.
Examine the Ethical Dilemma
Allison made a commitment to meet with the university instructor to develop student assignments at 2:00 pm. The patient emergency
that developed prevented Allison from living up to that commitment. Allison had an obligation to provide patient care during the
emergency and a competing obligation to the professor. She solved the dilemma of competing obligations by providing her electronic
medical record access password to the university professor.
By sharing her password, Allison most likely violated hospital policy related to the security of healthcare information. She may
also have violated the American Nurses Association code of ethics, which states that nurses must judiciously protect information of a
confidential nature. Because the university professor was also a nurse and had a legitimate interest in the protected healthcare
information, there might not be a code of ethics violation.
Thoroughly Comprehend the Possible Alternatives Available
The possible alternatives available include the following: (1) Allison could have asked the professor to wait until the patient crisis was
resolved; (2) Allison could have delegated another staff member to assist the university professor; or (3) Allison could have logged on
to the system for the professor.
http://nursing.jbpub.com/informatics
Hypothesize Ethical Arguments
The utilitarian approach applies to this situation. An ethical action is one that provides the greatest good for the greatest number; the
underlying principles in this perspective are beneficence and nonmaleficence. The rights to be considered are as follows: right of the
individual to choose for himself or herself (autonomy); right to truth (veracity); right of privacy (the ethical right to privacy avoids
conflict and, like all rights, promotes harmony); right not to be injured; and right to what has been promised (fidelity).
Does the action respect the moral rights of everyone? The principles to consider are autonomy, veracity, and fidelity.
As for the fairness or justice, how fair is an action? Does it treat everyone in the same way, or does it show favoritism and
discrimination? The principles to consider are justice and distributive justice.
Thinking about the common good assumes one’s own good is inextricably linked to good of the community; community members
are bound by pursuit of common values and goals and ensure that the social policies, social systems, institutions, and environments on
which one depends are beneficial to all. Examples of such outcomes are affordable health care, effective public safety, a just legal
system, and an unpolluted environment. The principle of distributive justice is considered.
Virtue assumes that one should strive toward certain ideals that provide for the full development of humanity. Virtues are attitudes
or character traits that enable one to be and to act in ways that develop the highest potential; examples include honesty, courage,
compassion, generosity, fidelity, integrity, fairness, self-control, and prudence. Like habits, virtues become a characteristic of the
person. The virtuous person is the ethical person. Ask yourself, what kind of person should I be? What will promote the development
of character within myself and my community? The principles considered are fidelity, veracity, beneficence, nonmaleficence, justice,
and distributive justice.
In this case, there is a clear violation of an institutional policy designed to protect the privacy and confidentiality of medical
records. However, the professor had a legitimate interest in the information and a legitimate right to the information. Allison trusted
that the professor would not use the system password to obtain information outside the scope of the legitimate interest. However,
Allison cannot be sure that the professor would not access inappropriate information. Further, Allison is responsible for how her access
to the electronic system is used. Balancing the rights of everyone—the professor’s right to the information, the patients’ rights to
expect that their information is safeguarded, and the right of the patient in crisis to expect the best possible care—is important and is
the crux of the dilemma. Does the patient care obligation outweigh the obligation to the professor? Yes, probably. Allison did the right
thing by caring for the patient in crisis. By giving out her system access password, Allison also compromised the rights of the other
patients on the unit to expect that their confidentiality and privacy would be safeguarded.
Virtue ethics suggests that individuals use power to bring about human benefit. One must consider the needs of others and the
responsibility to meet those needs. Allison must simultaneously provide care, prevent harm, and maintain professional relationships.
Allison may want to effect a long-term change in hospital policy for the common good. It is reasonable to assume that this event
was not an isolated incident and that the problem may recur in the future. Can the institutional policy be amended to provide professors
with access to the medical records system? As suggested in the HIPAA administrative guidelines, the professor could receive the same
staff training regarding appropriate and inappropriate use of access and sign the agreement to safeguard the records. If the institution
has tracking software, the professor’s access could be monitored to watch for inappropriate use.
Identify the moral principles that can be brought into play to support a conclusion as to what ought to be done ethically in this case
or similar cases. The International Council of Nurses (2006) code of ethics states that “The nurse holds in confidence personal
information and uses judgment in sharing this information” (p. 4). The code also states, “The nurse uses judgment in relation to
individual competence when accepting and delegating responsibilities” (p. 5). Both of these statements apply to the current situation.
Ascertain whether the approaches generate converging or diverging conclusions about what ought to be done. From the analysis, it
is clear that the best immediate solution is to delegate assisting the professor with assignments to another nurse on the unit.
Investigate, Compare, and Evaluate the Arguments for Each Alternative
Review and think through the items listed in Table 5-1.
TABLE 5-1 DETAILED ANALYSIS OF ALTERNATIVE ACTIONS
Choose the Alternative You Would Recommend
The best immediate solution is to delegate another staff member to assist the professor. The best long-term solution is to change the
hospital policy to include access for professors, as described previously.
Act on Your Chosen Alternative
Allison should delegate another staff member to assist the professor in making assignments.
Look at the Ethical Dilemma and Examine the Outcomes While Reflecting on the Ethical Decision
As already indicated in the alternative analyses, delegation may not be an ideal solution because the staff nurse who is assigned to
assist the professor may not possess the same extensive information about all of the patients as the charge nurse. It is, however, the
best immediate solution to the dilemma and is certainly safer than compromising the integrity of the hospital’s computer system. As
noted previously, Allison may want to pursue a long-term solution to a potentially recurring problem by helping the professor gain
legitimate access to the computer system with the professor’s own password. The system administrator would then have the ability to
track who used the system and which types of information were accessed during use.
This case analysis demonstration provides the authors’ perspective on this case and the ethical decision made. If your decision did
not match this perspective, what was the basis for the difference of opinion? If you worked through the model, you might have reached
a different decision based on your individual background and perspective. This does not make the decision right or wrong. A decision
should reflect the best decision one can make given review, reflection, and critical thinking about this specific situation.
Six additional cases are provided in the online learner’s manual for review. Apply the model to each case study, and discuss these
cases with colleagues or classmates.
New Frontiers in Ethical Issues
The expanding use of new information technologies in health care will bring about new and challenging ethical issues. Consider that
patients and healthcare providers no longer have to be in the same place for a quality interaction. How, then, does one deal with
licensing issues if the electronic consultation takes place across a state line? Derse and Miller (2008) describe a second-opinion
medical consultation on the Internet where the information was provided to the referring physician and not to the patient, thus avoiding
the licensing issue.
Consider also the ethical issues created by genomic databases or by sharing of information in a health information exchange to
promote population health. Alpert (2008) asks, “Is it wise to put genomic sequence data into electronic medical records that are poorly
protected, that cannot adhere well to Fair Information Practice Principles for privacy, and that can potentially be seen by tens of
thousands of people/entities, when it is clear that we do not understand the functionality of the genome and likely will not for several
years?” (p. 382).
Further, how does one really obtain informed consent for such data collection, when how the data will ultimately be used is not
known, but clearly that application will be important to health research uses that go beyond the immediate medical care of the patient?
Angst (2009) asks whether the public good outweighs individual interests in such a case because the information contained in these
databases is important to developing new understandings and creating new knowledge by matching data in aggregated pools: “Thus,
science adds meaning and context to data, but to what extent do we agree to make the data available such that this discovery process
can take place, and are the impacts of discovery great enough to justify the risks?” (p. 172). Further, if a voluntary system where
patients can opt out of such data collection is adopted, then are healthcare disparities related to incomplete electronic health records
created?
In an ideal world, healthcare professionals must not be affected by conflicting loyalties; nothing should interfere with judicious,
ethical decision making. As the technologically charged waters of health care are navigated, one must hone a solid foundation of
ethical decision making and practice it consistently.
Summary
As science and technology advance, and policy makers and healthcare providers continue to shape healthcare practices including
information management, it is paramount that ethical decisions are made. Healthcare professionals are typically honest, trustworthy,
and ethical, and they understand that they are duty bound to focus on the needs and rights of their patients. At the same time, their day-
to-day work is conducted in a world of changing healthcare landscapes populated by new technologies, diverse patients, varied
healthcare settings, and changing policies set by their employers, insurance companies, and providers. Healthcare professionals need to
juggle all of these balls simultaneously, a task that often results in far too many gray areas or ethical decision-making dilemmas with
no clear correct course of action.
Patients rely on the ethical competence of their healthcare providers, believing that their situation is unique and will be respected
and evaluated based on their own needs, abilities, and limitations. The healthcare professional cannot allow conflicting loyalties to
interfere with judicious, ethical decision making. Just as in the opening example of the Apollo mission, it is uncertain where this
technologically heightened information era will lead, but if a solid foundation of ethical decision making is relied upon, duties and
rights will be judiciously and ethically fulfilled.
THOUGHT-PROVOKING
QUESTIONS
1. Identify moral dilemmas in healthcare informatics that would best be approached with the use of an ethical decision-making framework, such as
the use of smartphones to interact with patients as well as to monitor and assess patient health.
2. Discuss the evolving healthcare ethics traditions within their social and historical contexts.
3. Differentiate among the theoretical approaches to healthcare ethics as they relate to the theorists’ perspectives of individuals and their
relationships.
4. Select one of the healthcare ethics theories and support its use in examining ethical issues in healthcare informatics.
5. Select one of the healthcare ethics theories and argue against its use in examining ethical issues in healthcare informatics.
References
Alpert, S. (2008). Privacy issues in clinical genomic medicine, or Marcus Welby, M.D., meets the $1000 genome. Cambridge Quarterly of Healthcare
Ethics, 17(4), 373–384. Retrieved from Health Module (Document ID: 1880623501).
Anderson, K. (2012, September). Patient care and social media. Australian Nursing Journal, 22. http://go.galegroup.com.silk.library.umass.edu/ps/i.do?
id=GALE%CA301964363&v=2.1&u=mlin_w_umassamh&it=r&p+AONE&sw=w
Angst, C. (2009). Protect my privacy or support the common-good? Ethical questions about electronic health information exchanges. Journal of
Business Ethics, 90(suppl), 169–178. Retrieved from ABI/INFORM Global (Document ID: 2051417481).
Aristotle. (350 BC). Nichomachean ethics. Book I. (W. D. Ross, Trans.). Retrieved from http://www.constitution.org/ari/ethic_00.htm
Beauchamp, T. L., & Childress, J. F. (1977). Principles of biomedical ethics. New York, NY: Oxford University Press.
Beauchamp, T. L., & Childress, J. F. (1994). Principles of biomedical ethics (4th ed.). New York, NY: Oxford University Press.
Benjamin, M., & Curtis, J. (1992). Ethics in nursing (3rd ed.). New York, NY: Oxford University Press.
Derse, A., & Miller, T. (2008). Net effect: Professional and ethical challenges of medicine online. Cambridge Quarterly of Healthcare Ethics, 17(4),
453–464. Retrieved from Health Module (Document ID: 1540615461).
eHealth code. (n.d.). http://www.ihealthcoalition.org/ehealth-code/
Englund, H., Chappy, S., Jambunathan, J., & Gohdes, E. (2012, November/December). Ethical reasoning and online social media. Nurse Educator, 37,
242–247. http://dx.doi.org/10.1097/NNE.0b013e31826f2c04
Engster, D. (n.d.). Can care ethics be institutionalized? Toward a caring natural law theory.
https://lmstest.manhattan.edu/pluginfile.php/39833/mod_resource/content/1/Engster%20Care%20and%20Political%20Theory
Healthcare Ethics. (2007). Virtue ethics. http://www.ascensionhealth.org/ethics/public/issues/virtue.asp
Howard, R. (Director). (1995). Apollo 13 [Motion picture]. Universal City, CA: MCA Universal Studios.
Husted, G. L., & Husted, J. H. (1995). Ethical decision-making in nursing (2nd ed.). New York, NY: Mosby.
International Council of Nurses (ICN). (2006). The ICN code of ethics for nurses. http://www.icn.ch/icncode
Jonsen, A. R. (1991). Casuistry as methodology in clinical ethics. Theoretical Medicine, 12, 295–307.
Lyons, R., & Reinisch, C. (2013). The legal and ethical implications of social media in the emergency department. Advanced Emergency Nursing
Journal, 35(1), 53–56. http://dx.doi.org/10.1097/TME.0b013e31827a4926
Mack, J. (2000). Patient empowerment, not economics, is driving e-health: Privacy and ethics issues need attention too! Frontiers of Health Services
Management, 17(1), 39–43; discussion 49–51. Retrieved from ABI/INFORM Global (Document ID: 59722384).
Makus, R. (2001). Ethics and Internet healthcare: An ontological reflection. Cambridge Quarterly of Healthcare Ethics, 10(2), 127–136. Retrieved from
Health Module (Document ID: 1409693941).
Martin, P. A. (1999). Bioethics and the whole: Pluralism, consensus, and the transmutation of bioethical methods into gold. Journal of Law, Medicine &
Ethics, 27(4), 316–327.
McGonigle, D. (2000). The ethical model for ethical decision making. Inside Case Management, 7(8), 1–5.
Miller, L. A. (2011). Social media: Friend and foe. Journal of Perinatal and Neonatal Nursing, 307–309.
http://dx.doi.org/10.1097/JPN.0b013e31823506e9
Pellegrino, E. D. (1993). The metamorphosis of medical ethics: A thirty-year retrospective. Journal of the American Medical Association, 269, 1158–
1162.
Pellegrino, E., & Thomasma, D. (1993). The virtues in medical practice. New York, NY: Oxford University Press.
Prasad, B. (2013). Social media, health care, and social networking. Gastrointestinal Endoscopy, 77, 492–495.
Resnik, D. (2001). Patient access to medical information in the computer age: Ethical concerns and issues. Cambridge Quarterly of Healthcare Ethics,
10(2), 147–154; discussion 154–156. Retrieved from Health Module (Document ID: 1409693961).
Scott, A. (2002). Plato’s Meno. http://www.angelfire.com/md2/timewarp/plato.html
Spector, N., & Kappel, D. M. (2012). Guidelines for using electronic and social media. Online Journal of Nursing, 17.
http://www.medscape.com/viewarticle/780050
Stern, J. (2013). Google Glass: What you can and can’t do with Google’s wearable computer. http://abcnews.go.com/Technology/google-glass-googles-
wearable-gadget/story?id=19091948
Swartz, M. K. (2011, November/December). The potential for social media. Journal of Pediatric Health Care, 25, 345.
Velasquez, M., Andre, C., Shanks, T., & Myer, M. (for the Markkula Center for Applied Ethics). (1987). What is ethics?
http://www.scu.edu/SCU/Centers/Ethics/practicing/decision/whatisethics.shtml
White paper: A nurse’s guide to the use of social media. (2011). www.ncsbn.org
Section II
Perspectives on Nursing Informatics
Chapter 6 Overview of Nursing Informatics
Chapter 7 Informatics Roles and the Knowledge Work of Nursing
Chapter 8 Information and Knowledge Needs of Nurses in the 21st Century
Chapter 9 Legislative Aspects of Nursing Informatics: HITECH and HIPAA
Nursing informatics (NI) is the synthesis of nursing science, information science, computer science, and cognitive science for the
purpose of managing and enhancing healthcare data, information, knowledge, and wisdom to improve patient care and the nursing
profession. In the Building Blocks of Nursing Informatics section, the reader learned about the four sciences of NI, also referred to as
the four building blocks, and the ethical application of these sciences to manage patient information. Nursing knowledge workers must
be able to understand the evolving specialty of NI to harness and use the tools available for managing the vast amount of healthcare
data and information. It is essential that NI capabilities be appreciated, promoted, expanded, and advanced to facilitate the work of the
nurse, improve patient care, and enhance the nursing profession.
This section presents the perspectives of nursing experts on NI. The Overview of Nursing Informatics chapter begins this
exploration. In the Informatics Roles and the Knowledge Work of Nursing chapter, the reader learns about NI roles, competencies, and
skills. The Information and Knowledge Needs of Nurses in the 21st Century chapter considers the evolving NI needs of nurses and the
development of standardized terminologies in NI. The Legislative Aspects of Nursing Informatics: HITECH and HIPAA chapter
follows.
In the Overview of Nursing Informatics chapter, interrelationships among major NI concepts are discussed. As data are transformed
into information and information into knowledge, increasing complexity and interrelationships ensue. The boundaries between
concepts can become blurred, and feedback loops from one concept level to another evolve. Structured languages and human–
computer interaction concepts, which are critical elements for NI, are noted in this chapter. Taxonomies and other current structured
languages for nursing are listed. Human–computer interaction concepts are briefly defined and discussed because they are critical to
the success of informatics solutions. Importantly, the construct of decision making is added to the traditional nursing metaparadigms:
nurse, person, health, and environment. Decision making is not only at the crux of nursing practice in all settings and roles, but it is a
fundamental concern of NI. The work of nursing is centered on the concepts of NI: data, information, knowledge, and wisdom.
Information technology per se is not the focus; it is the information that the technology conveys that is central. Moreover, NI is no
longer the domain of experts in the field. More interestingly, one does not need technology to perform informatics. The centerpiece of
informatics is the manipulation of data, information, and knowledge, especially related to decision making in any aspect of nursing or
in any setting. In a way, nurses are all already informatics nurses.
The Informatics Roles and the Knowledge Work of Nursing chapter discusses NI as a relatively new nursing specialty that
combines the building block sciences covered earlier in the text. Combining these sciences results in nurses being able to care for their
patients effectively and safely because the information that they need is readily available. Nurses have been actively involved in NI
since computers were introduced into health care. With the advent of electronic health records, it became apparent that nursing needed
to develop its own language for this evolving field. NI was instrumental in assisting in nursing language development. The healthcare
industry employs the largest number of knowledge workers—a fact that has resulted in the realization that healthcare administrators
must begin to change the way they look at their employees. Nurses and physicians are bright, highly skilled, and dedicated to giving
the best patient care. Administrators who tap into this wealth of knowledge find that patient care becomes safer and more efficient.
NI is governed by standards established by the American Nurses Association and is a very diverse field, which results in many
nurse informaticist specialists becoming focused on one segment of NI. Although NI is a recognized specialty area of practice, in the
future all nurses will be expected to have some knowledge of the field. NI competencies have been developed to ensure that all entry-
level nurses are ready to enter a field that is becoming more technologically advanced. The competencies may also be used to
determine the educational needs of currently practicing nurses. Nurse informatics specialists no longer have to enter the field solely
through on-the-job exposure, but can now obtain an advanced degree in NI at many well-established universities throughout the United
States. NI has grown tremendously as a specialty since its inception and is predicted to continue growing.
The Information and Knowledge Needs of Nurses in the 21st Century chapter notes that the core concepts and competencies of
informatics are particularly well suited to a model of interprofessional education. Ideally, when educational programs are emulating
clinical settings, informatics knowledge should be integrated with the processes of interprofessional teams and decision making.
Because simulation laboratories are becoming increasingly common fixtures in the delivery of health-related professional education,
they provide a perfect opportunity to incorporate the electronic health records applications. The learning laboratory for nursing
education will then more closely approximate the information technology–enabled clinical settings that are emerging in the real world.
A presumption is often made that future graduates will be more computer literate than nurses currently in practice. Although this may
be true, computer literacy or comfort does not equate to an understanding of the facilitative and transformative role of information
technology. It is essential that the future curricula of basic nursing programs embed the concepts of the role of information technology
in supporting clinical care delivery. The need for standardizing nursing terminology is also discussed in this chapter as a way to
improve the clinical support functions of the electronic health record.
Equally important in informatics practice is a thorough understanding of current legislation and regulations that shape 21st century
practice. The Legislative Aspects of Nursing Informatics: HITECH and HIPAA chapter provides insights into HIPAA rules and an
overview of the rules associated with technology implementation as defined by the HITECH Act. The information provided in this text
reflects current rules that were in effect at the time of publication. The reader should follow the rules development and evolution of
informatics legislation at the U.S. Department of Health and Human Services website (www.hhs.gov) to obtain the most current
information related to health information management.
There is an emerging global focus on information technology to support clinical care and on the potential benefits for clinicians
and patients. In the future, nurses will likely have sufficient computing power at their disposal to aggregate and transform additional
multidimensional data and information sources (e.g., historical, multisensory, experiential, genetic) into a clinical information system
to engage with individuals, families, and groups in ways not yet imagined. Every nurse’s practice will make contributions to new
nursing knowledge in these dynamically interactive clinical information system environments. With the right tools to support the
management of data, complex information processing, and ready access to knowledge, the core concepts and competencies associated
with informatics will be embedded in the practice of every nurse, whether administrator, researcher, educator, or practitioner.
Information technology is not a panacea, but it provides the profession with unprecedented capacity to generate and disseminate new
knowledge more rapidly.
The material within this text is placed within the context of the Foundation of Knowledge model (Figure II-1) to meet the needs of
healthcare delivery systems, organizations, patients, and nurses. Through involvement in NI and learning about this evolving specialty,
one will be able to use the current theories, architecture, and tools, while beginning to challenge what is known. This questioning and
search for what could be will provide the basis for the future landscape of nursing. By using the Foundation of Knowledge model as an
organizing framework for this text, the authors have attempted to capture this process.
Figure II-1 Foundation of Knowledge Model
Source: Designed by Alicia Mastrian.
In this section, the reader learns about NI. Those readers who are beginning their education will consciously focus on input and
knowledge acquisition, trying to glean as much information and knowledge as possible. As these readers become more comfortable in
their clinical setting and with nursing science, they will begin to take over some of the other knowledge functions. Experienced nurses,
also known as “seasoned nurses,” question what is known and search for ways to enhance their knowledge and the knowledge of
others. What is not available must be created. It is through these leaders, researchers, or clinicians that new knowledge is generated
and disseminated and nursing science is advanced. Sometimes, however, to keep up with the explosion of information in nursing and
health care, one must continue to rely on the knowledge generated and disseminated by others. In this sense, nurses are committed to
lifelong learning and the use of knowledge in the practice of nursing science. How nurses interact within their environment and apply
what is learned depends on their placement in the Foundation of Knowledge model.
Readers of this section are challenged to ask how they can (1) apply knowledge gained from the practice setting to benefit patients
and enhance their practice, (2) help colleagues and patients understand and use current technology, and (3) use wisdom to help create
the theories, tools, and knowledge of the future.
numbers have been changed to correspond to this chapter.
The conceptual framework for NI is based on work by Graves and Corcoran (1989), who provided the initial definition and
description of data, information, and knowledge as these terms apply to the science and practice of NI. Nelson (Nelson, 2002; Nelson
& Joos, 1989) and Joos (Joos, Nelson, & Smith, 2010) added the concept of wisdom and reconceptualized how these concepts
interrelate. In addition, the discussion of the definition and goal of nursing presented in the Scope and Standards of Practice evolved
from work by Staggers and Thompson (2002). NI is one example of a discipline-specific informatics practice within the broader
category of health informatics. NI has become well established within nursing since its recognition as a specialty for registered nurses
by the ANA in 1992. It focuses on the representation of nursing data, information, knowledge, and wisdom and the management and
communication of nursing information within the broader context of health informatics. NI (1) provides a nursing perspective, (2)
illuminates nursing values and beliefs, (3) denotes a practice base for nurses in NI, (4) produces unique knowledge, (5) distinguishes
groups of practitioners, (6) focuses on the phenomena of interest for nursing, and (7) provides needed nursing language and word
context to health informatics (Brennan, 2003).
Metastructures, Concepts, and Tools of NI
To understand the foundations of NI, one must begin by exploring its metastructures, sciences, concepts, and tools. Metastructures are
overarching concepts used in theory and science. Also of interest are the sciences underpinning NI, concepts and tools from
information science and computer science, human–computer interaction (HCI) and ergonomics concepts, and the phenomena of
nursing.
Metastructures: Data, Information, Knowledge, and Wisdom
In the mid-1980s, Blum (1986) introduced the concepts of data, information, and knowledge as a framework for understanding clinical
information systems and their impact on health care. He did this by classifying the then-current clinical information systems by the
three types of objects that these systems processed: data, information, and knowledge. He noted that the classification was artificial
with no clear boundaries; however, increasing complexity between the concepts existed.
In 1989, Graves and Corcoran built on this work when they published their seminal work describing the study of NI using the
concepts of data, information, and knowledge. The article contributed two broad principles to NI that are acknowledged here: a
definition of NI that has been widely accepted in the field; and an information model that identified data, information, and knowledge
as key components of NI practice. The Graves model is presented in Figure 6-1.
Graves and Corcoran (1989) drew from Blum (1986) to define the three concepts as follows: (1) data are discrete entities
described objectively without interpretation; (2) information is data that are interpreted, organized, or structured; and (3) knowledge
is information that is synthesized so that relationships are identified and formalized. Drawing on this work, Nelson (2002; Nelson &
Joos, 1989) defined wisdom as the appropriate application of knowledge to the management and solution of human problems.
Data, which are processed to create information and then knowledge, may be obtained from individuals, families, communities,
and populations and the environment in which they exist. Data, information, knowledge, and wisdom are of concern to nurses in all
areas of practice. For example, data derived from direct care of an individual may then be compiled across persons and aggregated for
decision making by nurses, nurse administrators, or other health professionals. Further aggregation may address communities and
populations. Nurse educators may create case studies using these data, and nurse researchers may access aggregated data for
systematic study.
Figure 6-1 Conceptual framework for the study of nursing knowledge
Source: American Nurses Association (ANA). (2008). Nursing informatics: Scope & standards of practice. Springfield, MD: nursebooks.org.
As an example, an instance of vital signs for an individual’s heart rate, respiration, temperature, and blood pressure can be
considered a set of data elements. A serial set of vital signs taken over time, placed into a context, and used for longitudinal
comparisons is considered information. That is, a dropping blood pressure and increasing heart rate, respiratory rate, and fever in an
elderly, catheterized person are recognized as being abnormal for this person. The recognition that the person may be septic and,
therefore, may need certain nursing interventions reflects information synthesis (knowledge) based on nursing knowledge and
experience.
Figure 6-2 builds on the work of Graves and Corcoran (1989) by adding the concept of wisdom and reconfiguring the
interrelationships between and among the concepts. As data are transformed into information and information into knowledge, each
level increases in complexity and requires greater application of human intellect. The x-axis in (Figure 6-2) represents interactions
within and between the concepts as one moves from data to wisdom; the y-axis represents the increasing complexity of the concepts’
http://nursebooks.org
increasing interrelationships.
Wisdom is knowing when and how to apply knowledge to deal with complex problems or specific human need (Nelson, 2002;
Nelson & Joos, 1989). Although knowledge focuses on what is known, wisdom focuses on the appropriate application of that
knowledge. For example, a knowledge base may include several options for managing an anxious family, whereas wisdom would
guide the decisions about which of these options are most appropriate within a specific family. As this example demonstrates, the
scope of NI is based on the scope of nursing practice and nursing science with a concentration on data, information, knowledge, and
wisdom. It is not limited by current technology. If the study of NI was limited to what the computer can process, the study of
informatics could not fully appreciate or support the full scope and complexity of nursing practice. NI must consider how nurses
impact technology and how technology impacts nursing. An understanding of this interaction makes it possible to understand how
nurses create knowledge and how they make use of that knowledge in their practices.
Figure 6-2 The relationship of data, information, knowledge, and wisdom
Source: Copyright Ramona Nelson. Used with the permission of Ramona Nelson, President Ramona Nelson Consulting at ramonanelson@verizon.net.
All rights reserved.
The appropriate use of knowledge involves the integration of empirical, ethical, personal, and aesthetic knowledge in the process
of implementing actions. The individual must apply a high level of empirical knowledge in understanding the current situation, apply a
professional value system in considering possible actions, be able to predict the potential outcome of these actions with a high level of
accuracy, and then have the willpower to carry out the selected action in the current environment. An example of applied wisdom
demonstrating this integration in NI is the appropriate use of information management and technological tools to support effective
nursing practice.
The addition of wisdom raises new and important research questions. This addition challenges the discipline to develop tools and
processes for classifying, measuring, and coding wisdom as it relates to nursing, NI, and informatics education. These research
avenues help clarify the relationships between wisdom and the intuitive thinking of expert nurses. Such research is invaluable in
building information systems to support expert healthcare practitioners and to support the decision process of more novice nurses.
Two interrelated forces have encouraged expansion of the NI model to include wisdom. First, the initial work was limited to the
types of objects processed by automated systems in the mid-1980s. However, NI is now concerned with the use of information
technology to improve the access and quality of health care that is delivered to individuals, families, and communities. Addition of the
concept of wisdom expands the focus of the model from the technology and the processing of objects to include interaction of the
human with the technology and resultant outcomes.
The previous ANA Scope and Standards of Practice (2001) had considered the inclusion of the concept of wisdom as too
controversial. However, with the recognition of this concept in the 2008 ANA Scope and Standards of Practice, nurses are now
demonstrating the practical application of the concept to the practice of NI. For example, Schleyer and Beaudry (2009) described how
the data-to-wisdom continuum applies to informatics in telephone triage nursing practice. Another example from acute care is a
statement by Troy Seagondollar posted on the NI Listserv ni-wg on November 19, 2010, at 11:27 am:
Content removed due to copyright restrictions.
More formally, the philosophical underpinnings and validity of the concept of wisdom in the data–information–knowledge–
wisdom framework are discussed in more detail by Matney, Brewster, Sward, Cloyes, and Staggers (2011). Essentially, two
perspectives in philosophy, postpositivism and hermeneutics, together support the addition of wisdom in the framework for NI.
Sciences Underpinning NI
mailto:ramonanelson@verizon.net
A significant contribution of Graves and Corcoran (1989) was a description and definition of NI that was widely accepted in the field
in the 1990s. It stated that NI is a combination of nursing science, information science, and computer science to manage and process
nursing data, information, and knowledge to facilitate the delivery of health care. The central notion was that the application of these
three core sciences was what made NI unique and differentiated it from other informatics specialties.
In addition to these three core sciences, other sciences may be required to solve informatics issues. James Turley (1996) expanded
the model of NI to include cognitive science. Certainly, the cognitive aspect of humans is a critical piece for the informatics nurse
specialist (INS) and the informatics nurse (IN) to understand. However, other sciences may be equally as critical, depending on the
issue at hand. For example, if the INS is dealing with a systems implementation in an institution, an understanding of organizational
theory may be germane to successful implementation (Staggers & Thompson, 2002). As science evolves, it may be necessary to
include other core sciences in future models.
Although the core sciences are foundational to the work of NI, the practice of the specialty is considered an applied science rather
than a basic science. The combination of sciences creates a unique blend that is greater than the sum of its parts, a unique combination
that creates the definitive specialty of NI. Further, informatics realizes its full potential within health care when it is grounded within a
discipline; in this case, the discipline is nursing. Computer and information science applied in isolation have less impact than if applied
within a disciplinary framework. Through application, the science of informatics can solve critical healthcare issues of concern to a
particular discipline.
Structured Language as a Tool for NI
Many of the tools used by the IN and INS are based on metastructures and concepts that incorporate knowledge from nursing and other
health and information sciences. Nursing knowledge is gained by the ability to extract data that specifically defines nursing
phenomena. Many different languages and ways of organizing data, information, and knowledge exist based on different concepts.
The creation of nursing taxonomies and nomenclatures has occurred over the past years, allowing these iterations to occur. The
ANA has formalized the recognition of these languages and vocabularies through a review process of the Committee on Nursing
Practice Information Infrastructure (CNPII). Box 6-1 lists the ANA-approved nursing languages (as of August 2012) and provides a
website for each approved language
(http://www.nursingworld.org/MainMenuCategories/ThePracticeofProfessionalNursing/NursingStandards/Recognized-Nursing-
Practice-Terminologies.aspx).
At a higher level of structure, several resources have been developed to facilitate interoperability among different systems of
concepts and nomenclature. For instance, the Systemized Nomenclature of Medicine (SNOMED CT) is considered a universal
healthcare terminology and messaging structure. In nursing, SNOMED enables terminology from one system to be mapped to
concepts from another system, such as the North American Nursing Diagnosis Association (NANDA) terminology, Nursing
Intervention Classification (NIC), and Nursing Outcome Classification (NOC). On a larger scale, the Unified Medical Language
System of the National Library of Medicine (UMLS; http://www.nlm.nih.gov/research/umls) incorporates the work of more than 100
vocabularies, including SNOMED (http://www.nlm.nih.gov/research/umls/knowledge_sources/metathesaurus/). The INS must be
aware of these tools and may be called on to understand the concepts of one or more languages, the relationships between related
concepts, and integration into existing vocabularies for a given organization.
BOX 6-1 ANA-RECOGNIZED TERMINOLOGIES THAT SUPPORT NURSING PRACTICE (AUGUST 2012)
1. NANDA: Nursing Diagnoses, Definitions, and Classification, 1992 Website: www.nanda.org
2. Nursing Interventions Classification System (NIC) 1992 Website:
www.nursing.uiowa.edu/excellence/nursing_knowledge/clinical_effectiveness/index.htm
(NIC/NOC can be obtained from the same source)
3. Clinical Care Classification (CCC), 1992 Formerly Home Health Care Classification (HHCC) Website: www.sabacare.com
4. Omaha System, 1992 Website: www.omahasystem.org
5. Nursing Outcomes Classification (NOC), 1997 Sue Moorehead, PhD, RN, Center Director
Website: www.nursing.uiowa.edu/excellence/nursing_knowledge/clinical_effectiveness/index.htm
(NIC/NOC can be obtained from the same source)
6. Nursing Management Minimum Data Set (NMMDS), 1998 Website: http://www.nursing.umn.edu/icnp/usa-nmds
7. PeriOperative Nursing Data Set (PNDS) 1999
Website: www.aorn.org or http://www.aorn.org/workarea/DownloadAsset.aspx?id=21663
8. SNOMED CT, 1999
Website: www.ihtsdo.org/snomed-ct/
9. Nursing Minimum Data Set (NMDS), 1999
Website: http://www.nursing.umn.edu/icnp/usa-nmds/
10. International Classification for Nursing Practice (ICNP), 2000 Website: http://www.icn.ch/icnp.htm
11. ABC Codes, 2000
Website: www.abccodes.com
Prepared from information located at
http://www.nursingworld.org/MainMenuCategories/ThePracticeofProfessionalNursing/NursingStandards/Recognized-Nursing-Practice-
Terminologies.aspx
12. Logical Observation Identifiers Names and Codes (LOINC), 2002 Website: http://loinc.org
13. Retired Data Sets
Patient Care Data Set (PCDS), 1998
The importance of languages and vocabularies cannot be overstated. The INS must seek a broader picture of the implications of his
or her work and the uses and outcomes of languages and vocabularies for end users. For instance, nurses working in mapping a home
care vocabulary with an intervention vocabulary must see beyond the technical aspect of the work. They must understand that there
may be a case manager for a multisystem health organization or a home care agency that is developing knowledge of nursing acuity
and case mix based on the differing vocabularies that they have integrated. The INS must envision the differing functions that may be
used with the data, information, and knowledge that have been created. See the Informatics Roles and the Knowledge Work of Nursing
chapter for a more thorough discussion of terminologies.
Concepts and Tools from Information Science and Computer Science
Computer science focuses on the study of the theoretical foundations of computation processes and of practical techniques for their
application in computer systems. Information science is an interdisciplinary science concerned with the collection, classification,
manipulation, storage, retrieval, and dissemination of information.
Informatics tools and methods from computer and information sciences are considered fundamental elements of NI, including
information technology, information structures, information management, and information communication.
Information technology includes computer hardware, software, communication, and network technologies, derived primarily from
computer science. The other three elements are derived primarily from information science. Information structures organize data,
information, and knowledge for processing by computers. Information management is an elemental process within informatics in
which one is able to file, store, and manipulate data for various uses. Information communication processes enable systems to send
data and to present information in a format that improves understanding. The use of information technology distinguishes informatics
from more traditional methods of information management. Thus, NI incorporates the four previously mentioned additional elements
from computer and information science. Underlying all of these are HCI concepts, discussed next.
HCI and Related Concepts
HCI, usability, and ergonomics concepts are of fundamental interest to the INS. Essentially, HCI deals with people, software
applications, computer technology, and the ways they influence each other (Dix, Finlay, Abowd, & Beale, 2004). Elements of HCI are
rooted in psychology, social psychology, and cognitive science. However, the design, development, implementation, and evaluation of
applications derive from applied work in computer science, the specific discipline at hand (in this case, nursing), and information
science. For example, an INS assesses an application before purchase to determine whether the application design complements the
way nurses cognitively process medication orders.
A related concept is “usability,” which deals with specific issues of human performance during computer interactions for specific
tasks within a particular context (Dix et al., 2004). Usability issues address the efficiency and effectiveness of an application. For
example, an INS might study the ease of learning an application, the ease of using an application, or the speed of task completion and
errors that occurred during application use when determining which system or application would be best used on a nursing unit.
The term ergonomics typically is used in the United States to describe the design and implementation of equipment, tools, and
machines related to human safety, comfort, and convenience. Commonly, the term ergonomics refers to attributes of physical
equipment or to principles of arrangement of equipment in the work environment. For instance, an INS may have a role in ensuring
that good ergonomics principles are used in an intensive care unit to select and arrange various devices to support workflow for cross-
disciplinary providers and patients’ families.
HCI, usability, and ergonomics are related concepts that are typically subsumed under the rubric of human factors, or how humans
and technology relate to each other. The overall goal is better design for software, devices, and equipment to promote optimal task
completion in various contexts or environments. Optimal task completion includes the concepts of efficiency and effectiveness,
including considerations about the safety of the user. It is essential that the INS understand these concepts to be able to develop
effective strategies to select, implement, and evaluate information structures and informatics solutions.
The importance of human factors in health care was elevated with the Institute of Medicine’s 2001 report. Before this, HCI and
usability assessments and methods were being incorporated into health care at a glacial speed. In the past 5 years, the number of HCI
and usability publications in health care has increased substantially. Vendors have installed usability laboratories and incorporated
usability testing of their products into their systems life cycles. The Food and Drug Administration (FDA) has mandated usability
testing as part of its approval process for any new devices (Medical Devices Today, 2007). Thus HCI and usability are critical concepts
for INs and INSs to understand. Numerous usability methods and tools are available (e.g., heuristics [rules of thumb], naturalistic
observation, and think-aloud protocols). Readers are referred to HCI references and The Human–Technology Interface chapter to learn
more about these methods.
Phenomena of Nursing
The metaparadigm of nursing comprises four key concepts: (1) nurse, (2) person, (3) health, and (4) environment. Nursing actions are
based on interrelationships between the concepts and are related to the values nurses hold relative to them. Nurses make decisions
about interventions from their unique perspectives. Decision making is the process of choosing among alternatives. The decisions that
nurses make can be characterized by both the quality of decisions and the impact of the actions resulting from those decisions. As
knowledge workers, nurses make numerous decisions that affect the life and well-being of individuals, families, and communities. The
process of decision making in nursing is guided by the concept of critical thinking. Critical thinking is the intellectually disciplined
process of actively and skillfully using knowledge to conceptualize, apply, analyze, synthesize, or evaluate data and information as a
guide to belief and action (Scriven & Paul, 1997).
Clinical wisdom is the ability of the nurse to add experience and intuition to a situation involving the care of a person (Benner,
Hooper-Kyriadkidis, & Stannard, 1999). Wisdom is demonstrated in informatics by the ability of the INS to evaluate the
documentation drawn from a health information system (HIS) and the ability to adapt or change the system settings or parameters to
improve the workflow of the clinical nurse.
Nurses’ decision making is described as an array of decisions that include specific behaviors and cognitive processes surrounding a
cluster of issues. For example, nurses use data transformed into information to determine interventions for persons, families, and
communities. Nurses make decisions about potential problems presented by an individual and about appropriate recommendations for
addressing those problems. They also make decisions in collaboration with other healthcare professionals, such as physicians,
pharmacists, or social workers. Decisions also may occur within specific environments, such as executive offices, classrooms, and
research laboratories.
An information system collects and processes data and information. Decision support systems are computer applications designed
to facilitate human decision-making processes. Decision support systems are typically rule based, using a specified knowledge base
and a set of rules to analyze data and information and provide recommendations. Other decision support systems are based on
knowledge models induced directly from data, regression, or classification models that predict characteristics or outcomes.
Recommendations take the form of alerts (i.e., calling user attention to abnormal laboratory results or potential adverse drug events) or
suggestions (e.g., appropriate medications, therapies, or other actions) (Haug, Gardner, & Evans, 1999).
An expert system is a type of decision support system that implements the knowledge of one or more human experts without
human intervention. For example, an insulin pump that senses the patient’s blood glucose level and administers insulin based on those
data is a form of expert system. Whereas control systems implement decisions without involvement of a user, decision support systems
merely provide recommendations and rely on the wisdom of the user for appropriate application of these provided recommendations.
Within informatics, there is always a tension between which decisions should be automated and which decisions require human
intervention. The relationships among these concepts and information, decision support, and expert systems are depicted in Figure 6-3.
An INS must be able to navigate the complexity of the relationships between the following elements and understand how they
facilitate decision making:
Data, information, knowledge, and wisdom
Nursing science, information science, computer science, and other sciences of interest to the issue at hand (e.g., cognitive
science)
Nurse, person, health, and environment
Information structures, information technology, managing and communicating information
Figure 6-3 Levels and types of automated systems
Source: Modified from Englebardt, S. & Nelson, R. (2002). Health care informatics: An Interdisciplinary approach. St. Louis, MO: Mosby. Used with
the permission of Ramona Nelson, President Ramona Nelson Consulting at ramonanelson@verizon.net. All rights reserved.
The Future of NI
The future of NI will impact and be impacted by several driving trends. These include (1) changes in society, such as the aging
population and the increased use of participatory and mobile technology; (2) changes in healthcare delivery, including the changing
and expanding role of nursing within healthcare delivery; and (3) changes in technology, such as nanotechnology, which promises to
redefine the composition of nearly every human-made material and drastically alter biomedical applications (Alivisatos, 2001).
All of these changes will drive increased saturation of informatics concepts and solutions into mainstream nursing and healthcare
practices. As informatics solutions become as common a tool as the stethoscope, every nurse, to be safe and effective, will need to
incorporate informatics concepts into all aspects of practice. One example is the increased recognition of the concept of wisdom as a
key concept for NI. Its more detailed definition and measurement will be a part of the future practice of all nursing.
Summary
This chapter outlined the foundations of NI. The following definition for NI is offered: NI is a specialty that integrates nursing science,
computer science, and information science to manage and communicate data, information, knowledge, and wisdom in nursing practice.
As part of this chapter’s consideration of NI, interrelationships among major NI concepts were discussed. As data are transformed into
information and information into knowledge, and knowledge is applied through wisdom to ensure appropriate, effective, and
compassionate nursing care, increasing complexity and interrelationships ensue. The boundaries between concepts can become blurred
and feedback loops from one concept level to another emerge. These major concepts can be related to various types of systems,
including information, expert, and decision support systems. The following sciences are intimately intertwined with NI: nursing,
computer, and information science. Other sciences are used as required by the issue at hand.
Critical elements for NI include structured languages and HCI concepts. Taxonomies and other current structured languages for
nursing were listed within this chapter. HCI concepts were briefly defined and discussed because they are critical to the success of
mailto:ramonanelson@verizon.net
informatics solutions.
In an ideal world, the authors would like to see the following:
Patients who are truly active participants in managing their own health. The use of technology, such as personal health records
and automated monitoring devices, along with the implementation of participatory medicine, will make that possible.
Safe care, where more than 100,000 patients do not die each year from medical errors. Improved use of technology, such as
decision support systems and alerts, will make that possible.
Health care that can be afforded by all. Increased use of prevention strategies and efficiencies gained from technology will
make that possible.
A working environment for all nurses where data, information, and knowledge are effectively managed to ensure wisdom
guides all nursing decisions.
THOUGHT-PROVOKING
QUESTIONS
1. How is the concept of wisdom in NI like or unlike professional nursing judgment?
2. Can you create examples of how expert systems (not decision support systems but true expert systems) can be used to support nursing practice?
3. How would you incorporate the data-to-wisdom continuum into a job description for an NI specialist?
4. Can any aspect of nursing wisdom be automated?
5. The chapter states that research will be invaluable in building information systems to support expert healthcare practitioners and support the
decision-making processes of more novice nurses. What is the significance of this statement to the study of HCI in NI?
References
Alivisatos, A. P. (2001). Less is more in medicine: Sophisticated forms of nanotechnology will find some of the first real-world applications in
biomedical research, disease diagnosis and possibly therapy. Scientific American, 285(3), 66–73.
American Nurses Association (ANA). (2008). Nursing informatics: Scope and standards of practice. Springfield, MD: Nursesbooks.org
Benner, P., Hooper-Kyriadkidis, P., & Stannard, D. (1999). Clinical wisdom and interventions in critical care: A thinking-in-action approach.
Philadelphia, PA: W. B. Saunders.
Blum, B. (1986). Clinical information systems. New York, NY: Springer-Verlag.
Brennan, R. (2003). One size doesn’t fit all: Pedagogy in the online environment: Vol. 1. Adelaide, Australia: National Centre for Vocational Education
Research. http://www.ncver.edu.au/research/proj/nr0F05e.htm
Dix, A., Finlay, J., Abowd, G., & Beale, R. (2004). Human–computer interaction. Harlow, UK: Pearson, Prentice Hall.
Graves, J., & Corcoran, S. (1989). The study of nursing informatics. Image, 21(4), 227–230.
Haug, P., Gardner, R., & Evans, S. (1999). Hospital-based decision support. In E. S. Berner (Ed.), Clinical decision support systems: Theory and
practice (pp. 77–104). New York, NY: Springer-Verlag.
Institute of Medicine. (2001). Crossing the quality chasm: A new health system for the 21st century. Washington, DC: National Academies Press.
Joos, I., Nelson, R., & Smith, M. (2010). Introduction to computers for healthcare professionals (5th ed.). Sudbury, MA: Jones and Bartlett.
Matney, S., Brewster, P., Sward, K., Cloyes, K., & Staggers, N. (2011, March). Philosophical approaches to the data–information–knowledge–wisdom
framework. Advances in Nursing Science, 34(1) 6–18.
Medical Devices Today. (2007). New usability standard aims to help firms institute human factors programs.
http://www.medicaldevicestoday.com/2007/04/new_usability_s.html
Nelson, R. (2002). Major theories supporting health care informatics. In S. Englebardt & R. Nelson (Eds.), Health care informatics: An interdisciplinary
approach (pp. 3–27). St. Louis, MO: Mosby-Year Book.
Nelson, R., & Joos, I. (1989, Fall). On language in nursing: From data to wisdom. Pennsylvania League for Nursing PLN Vision (p. 6).
Schleyer, R., & Beaudry, S. (2009). Data to wisdom: Informatics in telephone triage nursing practice. AAACN Viewpoint, 31(5), 1, 10–13.
Scriven, M., & Paul, R. (1997). A working definition of critical thinking. Retrieved March 2008 from http://lonestar.texas.net/~mseifert/crit2.html
Staggers, N., & Thompson, C. B. (2002). The evolution of definitions for nursing informatics: A critical analysis and revised definition. Journal of the
American Medical Informatics Association, 33(1), 75–81.
Turley, J. (1996). Toward a model for nursing informatics. Image: Journal of Nursing Scholarship, 28(4), 309–313.
Chapter 7
Informatics Roles and the Knowledge Work of Nursing
Julie A. Kenney and Ida Androwich
OBJECTIVES
1. Explore the concept of nurses as knowledge workers.
2. Discuss the evolving roles and competencies of nursing informatics practice.
Key Terms
Advocate/policy developer
Certification
Cognitive activity
Consultant
Continuous learner
Data
Data gatherer
Decision support/outcomes manager
Educator
Entrepreneur
Industrial Age
Informatics
Informatics innovator
Informatics nurse specialist
Information
Information Age
Information user
Interdisciplinary knowledge team
Knowledge
Knowledge builder
Knowledge user
Knowledge worker
Medical informatics
Nursing informatics competencies
Product developer
Project manager
Researcher
Technologist
TIGER initiative
Introduction
The world has witnessed an unprecedented number of technological advances during the last 100 years. The early 20th century saw the
invention of the car and the airplane; both modes of transportation drastically changed how people work and play. The entertainment
world was dramatically altered by the invention of radio and television. The introduction of the computer altered the way data and
information are viewed and used and changed the way business is conducted. The computer is now changing nursing and health care.
Nurses have historically gathered and interpreted data. Florence Nightingale is credited as one of the first statisticians to collect and
use data to change the way she cared for her patients. While serving in the Crimean War, she began to gather data regarding the
conditions in which patients were living and the diseases they contracted and from which they died. These data were later used to
improve patient conditions at both city and military hospitals (O’Connor & Robertson, 2003).
Today, nurses are able to access information much more quickly and easily than their predecessors. Accessing information via the
Internet or the electronic health record (EHR) allows the nurse to provide the best possible patient care. Genomic health care and the
interaction between genetic factors and the environment require new understanding of the various types of information needed to make
decisions—a vast array of data characterized as a “data tsunami” (Bakken, Stone, & Larson, 2008).
Nursing recognized early on that computers would change health care and became actively involved in shaping how computers
were used in health care. The American Nurses Association (ANA) first recognized nursing informatics (NI) as a specialty in 1992
(ANA, 2008; Saba & McCormick, 2006). The introduction of this specialty has spurred the development of many informatics jobs,
organizations, and publications. Nurses now have the ability to further their education by attending informatics conferences, reading
journals, obtaining certificates and advanced degrees, and participating in numerous hospital-based and national and international
informatics committees and groups.
The Nurse as a Knowledge Worker
As described in the Overview of Nursing Informatics chapter, all nurses use data and information. This information is then converted to
knowledge. The nurse then acts on this knowledge by initiating a plan of care, updating an existing one, or maintaining status quo.
Does this use of knowledge make the nurse a knowledge worker? This section focuses on the definition of a knowledge worker, the
history of the term, and the ways in which it is used in health care and business. This chapter as a whole examines how nursing relates
to the term knowledge worker and the effect a knowledge worker has on health care.
Definitions
Knowledge can be defined as “the distillation of information that has been collected, classified, organized, integrated, abstracted, and
value added” (HIMSS, 2006, p. 49). A worker is “one that works especially at manual or industrial labor or with a particular material”
(Merriam-Webster Online, 2011). The term knowledge worker was first coined by Peter Drucker in his 1959 book, Landmarks of
Tomorrow (Drucker, 1994). Knowledge work is defined as nonrepetitive, nonroutine work that entails a significant amount of
cognitive activity (Sorrells-Jones & Weaver, 1999a). Drucker (1994) describes a knowledge worker as one who has advanced formal
education and is able to apply theoretical and analytical knowledge. According to Drucker, the knowledge worker must be a
continuous learner and a specialist in a field. McCormick (2009) estimates that a knowledge worker spends at least 50% of his or her
work time searching for and evaluating information.
According to Androwich (2010), it is important to understand that there is a dual role for accessing and using information (content)
in health care. In the first instance, when the nurse is caring for an individual patient, evidenced-based information (content) and
patient data need to be available at the point of care to inform the present patient encounter. In the second instance, patient data that are
entered by the nurse in the process of documentation need to be entered in such a manner that they are able to be aggregated to inform
future patient encounters.
Knowledge Worker Concept
The world is transitioning from the Industrial Age to the Information Age (Snyder-Halpern, Corcoran-Perry, & Narayan, 2001;
Sorrells-Jones & Weaver, 1999a). In the early 1900s, the workforce consisted predominantly of farmers. After World War I, the
workforce began to become predominantly industrial. This transition occurred when many farmers and domestic help moved to the
cities to take jobs at factories. Today, the industrial worker is slowly being replaced by the technologist (Drucker, 1994); the
technologist is adept at using both mind and hand. Many industrial workers are finding it increasingly more difficult to obtain jobs
because they do not have the educational base or mindset required of knowledge workers (Drucker, 1994). The technologist is no
longer trained on the job, as industrial workers traditionally were, which can cause significant problems for the industrial worker who
does not have the education required to transition to a knowledge worker position (Drucker, 1994; Sorrells-Jones & Weaver, 1999a).
Knowledge workers are innovators, and the work they produce is the foundation for organizational sustainability and growth.
Knowledge workers are specialized, have advanced education, and typically have a high degree of autonomy and control over their
own work environments (Davenport, Thomas, & Cantrell, 2002; Sorrells-Jones & Weaver, 1999a). Such individuals are most efficient
when they are working in a multidisciplinary team. These teams are typically composed of members with complementary knowledge
bases. The team members possess problem-solving and decision-making skills and advanced interpersonal skills. All members of the
team are considered equal and are there to contribute their expertise. Leadership shifts and changes as the team tackles different parts
of the project, with the topic expert taking the lead. A well-functioning team can consistently outperform an individual (Sorrells-Jones
& Weaver, 1999b). Many of these teams become focused and passionate about the project on which they are working.
A key impediment to team effectiveness is a lack of understanding between team members and a lack of respect for one another’s
knowledge and experience (Sorrells-Jones & Weaver, 1999a). Another barrier to efficiency within the multidisciplinary team is the
individual knowledge worker who does not want to give up his or her own identity even though he or she may be swayed by other
professional opinions. Professionals have a more difficult time adjusting to working in a team than do nonprofessionals. Professionals
fail very few times in their lives, which often results in their not being able to learn from their failures (Sorrells-Jones & Weaver,
1999b). Knowledge workers also tend to be resistant to change, and as a result they dig in their heels and refuse to adapt to changes
that management has implemented to improve the work process or workflow (Davenport et al., 2002).
Companies that employ knowledge workers have been forced to change their management structures to better support these
employees. Management no longer commands, but rather seeks to inspire workers to produce the best product (Drucker, 1992).
Companies that rely on knowledge workers have come to the realization that the machines are unproductive without the knowledge of
those workers. Loyalty is no longer purchased with a paycheck but is earned by giving knowledge workers the ability to use their
knowledge effectively and innovatively (Drucker, 1992). In turn, the physical environment and workplace arrangements have been
adjusted to maximize the workflow of the knowledge workers (Davenport et al., 2002). Many of these changes have occurred in the
business world but have been slow to be adopted in health care.
Knowledge Workers and Health Care
The healthcare industry is firmly rooted in the Industrial Age. This long-standing tradition has resulted in an industry that is not
conducive to support the knowledge workers who represent most of the workforce (Sorrells-Jones & Weaver, 1999a; Wickramasinghe
& Ginzberg, 2001). Sorrells-Jones and Weaver (1999a) state that “healthcare institutions are among the most rigidly bureaucratic and
hierarchical, discipline-fragmented organizations in the U.S.” (p. 16). This organizational arrangement is evidenced by the multiple
administrative levels that manage a single-function unit. Corporate values within health care reflect the desire for employees to be
loyal and compliant, to avoid risk, and to see failure as negative instead of positive. Senior leadership keeps information tightly
controlled and fails to see the need to bring in external intelligence and influence. Rewards are based on individual rather than team
performance, and a significant pay difference exists between those at the top and those who produce the product (Weaver & Sorrells-
Jones, 1999).
Right now, health care is in the process of transitioning from the Industrial Age to the Information Age. This transition has proved
challenging because of the success of healthcare institutions that have enjoyed using current management methods. Its history of
success will make it difficult for the healthcare industry to abandon the old so as to learn the new. A new philosophy recognizing that
employees are mature, self-reliant, independent-thinking adults who function as partners in carrying out the work of the organization is
needed. The organization needs to view (knowledge worker) employees as an asset and supply the resources, tools, information, and
power they need to self-manage their work. Innovation needs to be supported, especially when it meets the customers’ needs, desires,
and wishes (Weaver & Sorrells-Jones, 1999).
Currently, there is a healthcare management trend toward adoption of flatter management styles with fewer layers of
administration. Organizations are beginning to switch to a clinical product or service line format. This format is typically designed
with the physician serving as the content expert and the nurse serving as the patient care expert. Unfortunately, this approach does not
represent a significant change from the way things are currently done (Weaver & Sorrells-Jones, 1999).
In the future, management needs to understand and support the knowledge work and nonknowledge work that are performed daily
in health care, as both types of work are integral to caring for patients safely. Organizations must switch from measuring the number of
tasks completed to measuring the outcomes obtained by knowledge workers (Sorrells-Jones & Weaver, 1999b). This trend is becoming
more evident with the posting of hospital report cards that demonstrate how effectively the hospital is caring for certain types of
patients.
Nurses as Knowledge Workers
The question to ask is, “Are nurses knowledge workers?” As shown in Table 7-1, when nursing characteristics are compared to the
characteristics of a knowledge worker, the nurse does meet the criteria for a knowledge worker. Nursing entails a significant amount of
knowledge and nonknowledge work. Knowledge work includes such duties as interpreting trends in laboratories and symptoms.
Nonknowledge work includes such tasks as calling the laboratory to check on laboratory results or making beds. Nurses, on a daily
basis, rely on their extensive clinical information and specialized knowledge to implement and evaluate the processes and outcomes
related to patient care (Snyder-Halpern et al., 2001).
TABLE 7-1 A COMPARISON OF KNOWLEDGE WORKER CHARACTERISTICS AND NURSING
CHARACTERISTICS
Knowledge Worker
Characteristics Nursing Characteristics
Advanced formal education • All nurses have college degrees ranging from AD/AS to PhD.
Able to apply theoretical and
analytical knowledge
• Nurses are educated on nursing theory and how to apply it in patient situations.
Continuous learner
• Obtain advanced degrees.
• Attend seminars.
• Earn contact hours.
Specialized • Nursing specialties are as numerous as medical specialties.
Innovator
• Nurses become innovative when they do not have proper equipment to care for the patients or
they feel that current products are inadequate.
Team member • Have been a member of the interdisciplinary team for a significant amount of time.
Snyder-Halpern et al. (2001) have identified four tasks associated with human information processing: (1) data gathering, (2)
information use, (3) creative application of knowledge to clinical practice, and (4) generation of new knowledge. These four tasks are
associated with four roles that nursing takes on as a knowledge worker: data gatherer, information user, knowledge user, and
knowledge builder, respectively.
Nurses are data gatherers by nature. They collect and record objective clinical data on a daily basis. These items include such
things as patient history information, vital signs, and patient assessment data. Nurses as data gatherers transition to information users
when they begin to interpret the data that they have collected and recorded. Nurses as information users then structure the clinical data
into information that can be used to guide patient care decisions (Snyder-Halpern et al., 2001). An example of this is when the nurse
notices that the patient’s blood pressure is elevated. Information users transition to knowledge users when they begin to notice trends
in a patient’s clinical data and determine whether the clinical data fall within or outside of the normal data range. Nurses transition
from knowledge users to knowledge builders when they examine clinical data and trends across groups of patients. These trends are
interpreted and compared to current scientific data to determine whether these data would improve the nursing knowledge domain. An
example of the transition of a nurse as knowledge user to a nurse as knowledge builder is an observation of medication compliance
rates over a specified time period for patients diagnosed with chronic high blood pressure, with the nurse then comparing these rates to
evidence-based literature to determine if this information improves the nursing knowledge base (Snyder-Halpern et al., 2001).
Snyder-Halpern et al. (2001) found that as nurses assumed each of these roles, they required different types of decision support
processes to support their knowledge needs. The data gatherer requires a system that captures and stores data accurately and reliably
and allows the data to be readily accessed. Most current healthcare decision support systems (DSSs) support the nurse in this role. The
information user role requires a system that can transform clinical data into a format that allows for easy recognition of patterns and
trends. These systems recognize the trend and display it for the nurse, who in turn uses this information to adjust the plan of care for
the patient. The information user role is generally well supported by current DSSs. The knowledge user role is the least supported role,
and many systems are currently looking at ways to support nurses in this role. One advantage of these DSSs is their ability to bring
knowledge to nurses so that they do not have to retrieve the information themselves, which allows them to adjust a patient’s plan of
care in a more efficient and timely manner. The knowledge builder role is typically seen in conjunction with the nurse researcher role
and quality management roles. These roles typically look at aggregated data that have been captured over time and from numerous
patients, with these data then being compared to clinical variables and interventions; this analysis results in the development of new
domain knowledge (Snyder-Halpern et al., 2001). The knowledge needs of nurses will continue to evolve as the systems improve.
The Challenge of Nurses as Knowledge Workers
For nurses to be treated as knowledge workers, they must first be recognized as knowledge workers (Snyder-Halpern et al., 2001).
Nurses have been part of the interdisciplinary team for years, but are they ready to become part of the interdisciplinary knowledge
team? Nursing may be ready to take that step, but are other members of the healthcare team ready to acknowledge nurses as respected
members of the team (Sorrells-Jones & Weaver, 1999a)? One reason that acceptance may be difficult to achieve is the fact that nurses
tend to be the least educated members of the interdisciplinary knowledge team, although this situation is slowly changing. Another
reason is that nurses, historically, have had a difficult time being active members of the interdisciplinary team (Sorrells-Jones &
Weaver, 1999b). Nursing still has a long way to go before nurses are fully accepted as equal participants in the interdisciplinary
knowledge team. For nurses to reach this goal, a major attitude change toward nursing needs to take place. In addition, nurses must
become better educated and more involved in the interdisciplinary knowledge team.
The Knowledge Needs and Competencies of Nurses
In the early days of medicine, the entire body of medical knowledge could fit into a single volume. Today, the amount of information
available is vast and expanding exponentially, a fact that makes the healthcare industry the world’s most knowledge-intense
environment (Snyder-Halpern et al., 2001). Computers, technology, and the informatics fields are assisting healthcare workers in
dealing with this information explosion.
Knowledge Needs
Nurses deal with a vast amount of information and knowledge every day, which they use to care for their patients. Nurses rely on an
extensive amount of clinical information and specialized knowledge to evaluate the processes they have implemented and to measure
the corresponding outcomes (Snyder-Halpern et al., 2001). Although nurses rely on their own knowledge, sometimes this knowledge
base is not adequate; on such occasions, they must access information to provide safe patient care. In a national survey, consulting a
peer was reported to be the most frequent way that information was obtained. The same survey also found that most of those surveyed
did not use information resources to gather practice information, and that only approximately 25% had been trained on how to use an
electronic database (Barton, 2005). If a peer does not have the information the nurse is seeking, the nurse is likely to turn to a hospital
policy, a journal, a textbook, a drug book, an online resource, the EHR, or many other possible sources to find the needed information.
New technology tools are likely to change these behaviors so that the best and most current information is readily available and
regularly used.
For this information to be beneficial to the nurse and the patient, it must be reliable and credible. The resource must be easily
accessible and packaged in such a way that the nurse is able to find the necessary information quickly and with a minimal amount of
difficulty. One way this can be accomplished is by implementing a DSS, which is designed to support nurses in their decision-making
activities. DSSs are increasingly being incorporated into the EHR.
Nursing Informatics Competencies
One challenge that health care is currently facing relates to the vast differences in computer literacy and information management
skills that healthcare workers possess (McNeil, Elfrink, Beyea, Pierce, & Bickford, 2006). Barton (2005) believes that new nurses
should have the following critical skills: use e-mail, operate Windows applications, search databases, and know how to work with the
institution-specific nursing software used for charting and medication administration. These skills should not be limited to just new
nurses, but instead should be required of all nurses and healthcare workers.
Staggers, Gassert, and Curran (2001) advocate that nursing students and practicing nurses should be educated on core NI
competencies. Although information technology and informatics concepts certainly need to be incorporated into nursing school
curricula, progress in this area has been slow. In the 1980s, a nursing group of the International Medical Informatics Association
convened to develop the first level of nursing competencies. While developing these competencies, the nursing group found that nurses
fell in to one of the following three categories: (1) user, (2) developer, or (3) expert. These categories have since been expanded.
Staggers et al. (2001) decided that the NI competencies developed in the 1980s were inadequate and needed to be updated. These
authors reviewed 35 NI competency articles and 14 job descriptions, which resulted in 1,159 items that were sorted into three broad
categories: (1) computer skills, (2) informatics knowledge, and (3) informatics skills. These items were then placed in a database,
where redundant items were removed. When this process was completed, 313 items remained.
When these items were then further subdivided, Staggers and colleagues, along with the American Medical Informatics
Association (AMIA) work group, realized that these competencies were not universal to all nurses; thus, before it could be determined
if the competency was an NI competency, nursing skill levels needed to be defined. The group determined that practicing nurses could
be classified into four categories:
(1) beginning nurse, (2) experienced nurse, (3) informatics nurse specialist (INS), and (4) informatics innovator. Each of these
skill levels needed to be defined before Staggers et al. (2001) could determine which level was the most appropriate for that skill set.
Table 7-2 provides the definition criteria for each skill level. Once the levels were defined, the group determined that 305 items were
NI competencies and placed them into appropriate categories.
Staggers, Gassert, and Curran (2002) conducted a Delphi study to validate the placement of the competencies into the correct skill
level. Of the 305 original competencies identified, 281 achieved an 80% approval rating for both importance as a competency and
placement in the correct practice level. The authors stress that this is a comprehensive list; thus, for a nurse to enter a particular skill
level, he or she need not have mastered every item listed for that skill level. To access the entire list of competencies by skill level,
visit http://www.nursingconsult.com/nursing/journals/0029-6465/full-text/PDF/s0029646508000546 ?issn=0029-
6465&full_text=pdf&pdfName=s0029646508000546 &spid=21480607&article_id=666314. Table 7-3 provides a modified version
of the list.
TABLE 7-2 DEFINITIONS OF FOUR LEVELS OF PRACTICING NURSES
Beginning Nurse
• Has basic computer technology skills and information management skills
• Uses institution’s information systems and the contained information to manage patients
Experienced Nurse
• Proficient in a specialty
• Highly skilled in using computer technology skills and information management skills to support his or her specialty area of practice
• Pulls trends out of data and makes judgments based on this information
• Uses current systems, but will collaborate with informatics nurse specialist regarding concerns or suggestions provided by staff
Informatics Nurse Specialist
• RN with advanced education who possesses additional knowledge and skills specific to computer technology and information
management
• Focuses on nursing’s information needs, which include education, administration, research, and clinical practice
• Application and integration of the core informatics sciences: information, computer, and nursing science
• Uses critical thinking, process skills, data management skills, systems life cycle development, and computer skills
Informatics Innovator
• Conducts informatics research and generates informatics theory
• Vision of what is possible
• Keen sense of timing to make things happen
• Creative in developing solutions
• Leads the advancement of informatics practice and research
• Sophisticated level of skills and understanding in computer technology and information management
• Cognizant of the interdependence of systems, disciplines, and outcomes and is able to finesse situations to obtain the best outcome
Source: Republished with permission of SLACK Incorporated, from Journal of Nursing Education, Staggers, N., Gassert, C., & Curran, C. Staggers,
N 40(7), 2001; 303–316; permission conveyed through Copyright Clearance Center, Inc.
In 2004, a group of nurses came together after attending a national informatics conference to ensure that nursing was equally
recognized in the national informatics movement. This so-called Technology Informatics Guiding Education Reform (TIGER) team
determined that using informatics was a core competency for all healthcare workers. They also determined that many nurses lack
information technology skills, which limits their ability to access evidence-based information that could otherwise be incorporated into
their daily practice. This group is currently working on a plan to include informatics courses in all levels of nursing education; when
that effort is complete, they will examine how to get the information out to practicing nurses who are not currently enrolled in an
academic program (TIGER Initiative, 2006). Many of the items identified by the TIGER team as lacking in both nursing students and
practicing nurses are items that Staggers et al. (2002) determined to be NI competencies. To learn more about the TIGER initiative,
visit http://www.tigersummit.com/.
What Is Nursing Informatics Specialty Practice?
NI is an established, yet ever-evolving profession that began when computers were introduced into health care. Those choosing NI as a
career find it full of numerous and varied opportunities. Until recently, most nurse informaticists entered the field by showing an
understanding and enthusiasm for working with computers. Now, however, nurses have many educational opportunities available to
become formally trained in the field of NI. This section of the chapter explores the scope and standards of NI, NI roles, education and
specialization, rewards of working in the field, and organizations and professional journals of the INS.
Nursing Contributions to Healthcare Informatics
Nursing has been involved in the purchase, design, and implementation of IS since the 1970s (Saba & McCormick, 2006). One of the
first health IS vendors studied how nurses managed patient care and realized that nursing activity was the core of patient activity and
needed to be the foundation of the health or clinical IS. Nursing informaticians have been instrumental in developing, critiquing, and
promoting standard nursing terminologies to be used in the health IS. Nursing is involved heavily in the design of educational
materials for practicing nurses, student nurses, other healthcare workers, and patients. Computers have revolutionized the way
individuals access information and have revolutionized educational and social networking processes.
Scopes and Standards
NI is important to nursing and health care because it focuses on representing nursing data, information, and knowledge. NI meets the
following needs for health informatics (ANA, 2008; Brennan, 1994):
Provides a nursing perspective
Showcases nursing values and beliefs
Provides a foundation for nurses in NI
Produces unique knowledge
Distinguishes groups of practitioners
Emphasizes the interest for nursing
Provides needed nursing language and word context
In 2008, the ANA published a revised scope and standards of nursing informatics practice. This publication includes the most
recent INS standards of practice and the INS standards of professional performance. There are three overarching standards of practice
(ANA, 2008, p. 33):
Entrepreneur. Those nurses involved in the entrepreneur role analyze nursing information needs and develop and market
solutions.
Specialty Education and Certification
Many nurses who entered into NI did so without any formal education in this field. In many cases, these nurses served as the unit
resource for computer or program questions. Often, they acquired their skills through on-the-job training or by attending classes.
Although this pathway to the NI field is still available today, more formal ways of acquiring these skills also exist. The informatics
nurse has a bachelor of science degree in nursing and additional knowledge and expertise in the informatics field (ANA, 2008). The
INS holds an advanced degree or a post-master’s certificate and is prepared to assume roles requiring this advanced knowledge. INSs
may attend informatics conferences and obtain contact hours or continuing education units.
Box 7-2 lists some of the pioneering colleges and universities that offer advanced degrees or certificates in NI. This is not a
comprehensive list; new programs are continually being developed. Local colleges and universities should be researched to see which
may have informatics programs.
BOX 7-2 FORMAL NURSING INFORMATICS EDUCATIONAL PROGRAMS
Graduate Degree Programs
Duke University: http://nursing.duke.edu/modules/son_academic/index.php?id=101
Excelsior College: http://www.excelsior.edu/nursing-masters-informatics-faq
Loyola University Chicago: http://www.luc.edu/nursing/graduate_hsm.shtml
New York University: http://www.nyu.edu/nursing/academicprograms/masters/programs/informatics.html
Northeastern University: http://www.healthinformatics.neu.edu/
University of Alabama at Birmingham: Retrieved September 2010 from http://main.uab.edu/shrp/default.aspx?pid=77369
University of Colorado at Denver: http://www.ucdenver.edu/academics/colleges/nursing/programs-admissions/masters-programs/ms-
program/specialties/healthcareinformatics/Pages/default.aspx
University of Iowa: http://www.nursing.uiowa.edu/center-for-nursing-classification-and-clinical-effectiveness
University of Kansas: http://nursing.kumc.edu/academics/master-of-science/nursing-informatics.html
University of Maryland: http://nursing.umaryland.edu/academic-programs/grad/masters-degree/ms-academicprogram/nursing-informatics
University of North Carolina at Chapel Hill: https://nursing.unc.edu/academics/master-of-science-in-nursing/health-care-systems-msn/
University of Pittsburgh: http://www.unmc.edu/nursing/programs.html
University of Utah: http://nursing.utah.edu/programs/msnursinginformatics.php
University of Washington: http://www.son.washington.edu/portals/cipct/
Vanderbilt University: http://www.nursing.vanderbilt.edu/msn/ni.html
Certificate Programs
Chamberlain College of Nursing: http://www.chamberlain.edu/admissions/graduate/graduate-certificate-programs
Indiana University: http://nursing.iupui.edu/continuing/informatics.shtml
Loyola University Chicago: http://www.luc.edu/media/lucedu/nursing/pdfs/Informatics%20Certificate
Northeastern University: http://www.healthinformatics.neu.edu/
Penn State University: http://www.worldcampus.psu.edu/degrees-and-certificates/nursing-informatics-certificate/overview
University of Iowa: http://informatics.grad.uiowa.edu/health-informatics/curriculum
Nurses who choose to specialize in NI have two certifications available to them. The first is obtained through the American
Nurses Credentialing Center. The Center’s examination is specific for the informatics nurse. The applicant must be a licensed
registered nurse with at least 2 years of recent experience and have a baccalaureate degree in nursing. The applicant must have
completed 30 contact hours of continuing education in informatics. The applicant must meet one of the following criteria: (1) 2,000
hours practicing as an informatics nurse, (2) 1,000 hours practicing as an informatics nurse and 12 semester hours of graduate
academic credit toward an NI degree, or (3) completion of an NI degree that included at least 200 supervised practicum hours. For
further information on this certification examination, visit
http://www.nursecredentialing.org/Certification/NurseSpecialties/Informatics. This website includes the aforementioned criteria and
provides further information about test eligibility, fees, examination content, examination locations, study materials, and practice tests.
The second certification examination is sponsored by the Healthcare Information and Management Systems Society (HIMSS).
Candidates who successfully pass this examination are designated as certified professionals in healthcare information and management
systems. The HIMSS examination is open to any candidate who is involved in healthcare informatics. Candidates must hold positions
in the following fields: administration/management, clinical IS, e-health, IS, or management engineering. Candidates may include any
of the following: chief executive officers, chief information officers, chief operating officers, senior executives, senior managers, IS
technical staff, physicians, nurses, consultants, attorneys, financial advisors, technology vendors, academicians, management
engineers, and students. Candidates must meet the following criteria to be eligible to sit for the examination: a baccalaureate degree
plus 5 years of associated information and management systems experience, with 3 of those years being in health care; or a graduate
degree plus 3 years of associated information and management systems experience, with 2 of those years being in health care. The
information discussed in this text and additional information about the examination can be found by visiting
http://www.himss.org/ASP/certification_cphims.asp.
Rewards of NI Practice
NI is a nursing specialty that does not focus on direct patient care but instead focuses on enhancing patient care and safety and
improving the workflow and work processes of nurses and other healthcare workers. The INS is instrumental in designing the
electronic healthcare records that healthcare workers use on a daily basis. This nurse is also responsible for designing tools that allow
healthcare workers to access patient information more efficiently than they have been able to do so in the past. Watching these changes
take place brings great satisfaction to the INS.
Change is a factor that an INS deals with on a daily basis. This dynamic nature of the position is probably its most difficult aspect,
because people deal with change differently. Understanding change theory and processes and appreciating how change affects people
assist the INS in developing strategies to encourage healthcare workers to accept changes and become proficient in informatics
solutions that have been implemented. Seeing the change adopted with a minimal amount of discord is very rewarding to the INS.
The INS also participates in informatics organizations that allow INSs to network and share experiences with one another. Such
interactions allow INSs to bring these new solutions back to their respective organizations and improve informatics trouble spots.
Attending professional conferences allows the INS to stay abreast of changes in the industry. Continuing education may help the INS
to improve a process or workflow within the hospital or to change the way a system upgrade is rolled out.
NI Organizations and Journals
One of the first informatics organizations founded was the Healthcare Information and Management Systems Society. HIMSS, which
celebrated its 50th year in 2011, was launched in 1961 and now has offices throughout the United States and Europe. HIMSS currently
represents 20,000 individuals and 300 corporations. This organization supports both local and national chapters. It has many associated
work groups, one of which is an NI work group. HIMSS is well known for its development of industry-wide policies and its
educational and professional development initiatives, all of which are directed toward the goal of ensuring safe patient care.
Membership in HIMSS offers many advantages for nurses, such as access to numerous weekly and monthly publications, and a
scholarly journal, The Journal of Healthcare Information Management. HIMSS offers many educational programs, including virtual
expos, which allow participants to experience the expo without having to travel. These educational opportunities allow participants to
network with colleagues and peers, which is a valuable asset in this field. To find out more about HIMSS, visit its homepage at
http://www.himss.org/ASP/index.asp (HIMSS, 2013). HIMSS also periodically conducts NI workforce surveys; see
http://www.himss.org/ResourceLibrary/ResourceDetail.aspx?ItemNumber=11587 for the 2011 results.
The American Medical Informatics Association (AMIA) was founded in 1990 when 3 health informatics associations merged.
AMIA currently has more than 3,000 members who reside in 42 countries. This organization focuses on the development and
application of biomedical and healthcare informatics. Members include physicians, nurses, dentists, pharmacists, health information
technology professionals, and biomedical engineers. AMIA offers many benefits to its members, such as weekly and monthly
publications and a scholarly journal, JAMIA—The Journal of the American Medical Informatics Association. Members may join a
working group that is specific to their specialty, including an NI work group. AMIA offers multiple educational opportunities and
many opportunities for networking with colleagues. To view this information and to see other AMIA offerings, visit
http://www.amia.org (AMIA, 2013).
The American Nursing Informatics Association (ANIA) was established in 1992 to provide an opportunity for southern California
informatics nurses to meet. It has since grown to a national organization whose members include healthcare professionals who work
with clinical IS, educational applications, data collection/research applications, and administrative/DSS, and those who have an
interest in the field of NI. In 2009, ANIA merged with the Capital Area Roundtable on Informatics in Nursing (CARING).
Membership benefits include the following:
Access to a network of more than 3,200 informatics professionals in 50 states and 30 countries
Active e-mail list
Quarterly newsletter indexed in CINAHL and Thomson
Job bank with employee-paid postings
Reduced rate at the ANIA Annual Conference
Reduced rate for CIN: Computers, Informatics, Nursing
ANIA Online Library of on-demand and webinar education activities
Membership in the Alliance for Nursing Informatics
Web-based meetings
In-person meetings and conferences held nationally and worldwide
To view this information and learn more about ANIA, visit https://www.ania.org/ (ANIA, 2013).
The Alliance of Nursing Informatics (ANI) is a coalition of NI groups that represents more than 3,000 nurses and 20 distinct NI
groups in the United States. Its membership represents local, national, and international NI members and groups. These individual
groups have developed organizational structures and have established programs and publications. ANI functions as the link between
NI organizations and the general nursing and healthcare communities and serves as the united voice of NI. To view this information
and learn more about ANI, visit http://www.allianceni.org (Alliance of Nursing Informatics, 2013).
BOX 7-3 NURSING INFORMATICS WEBSITES AND CORRESPONDING JOURNALS
Alliance for Nursing Informatics
Website: www.allianceni.org
American Health Information Management Association
Website: www.ahima.org
Journal: Journal of AHIMA & Perspectives in Health Information Management (online)
American Medical Informatics Association
Website: www.amia.org
Journal: JAMIA—Journal of the American Medical Informatics Association
NI website: http://www.amia.org/mbrcenter/wg/ni
American Nursing Informatics Association
(includes Capital Area Roundtable on Informatics in Nursing [CARING])
Website: www.ania.org
Resources link: http://www.ania.org/Resources.htm
Journal: CIN: Computers, Informatics, Nursing
Health Information and Management Systems Society
Website: www.himss.org
Chapter websites: http://www.himss.org/ASP/chaptersHome.asp
Journal: The Journal of Healthcare Information Management
NI website: http://www.himss.org/asp/topics_nursingInformatics.asp
International Medical Informatics Association
Website: www.imia.org
Journal: International Journal of Medical Informatics
NI website: http://www.imia.org/ni
Online Journal of Nursing Informatics
Website: http://www.ojni.org
These groups have been instrumental in establishing the informatics community. Many other informatics groups that have not been
covered here also exist. Box 7-3 lists some of these organizations and their publications.
The Future of Nursing Informatics
NI is in its infancy, as is the technology that the INS uses on a daily basis. NI will continue to influence development of the EHR. In
turn, the EHR will continue to improve and will one day accurately capture the care nurses give to their patients. This is a formidable
challenge because much of the care provided by nurses is intangible in nature. In the future, the EHR will provide data to the INS that
can then be used to improve nursing workflow and to determine whether current practices are the most efficient and beneficial to the
patient.
Nursing and health care are on a roller-coaster ride that will undoubtedly prove very interesting. New technology is being introduced at a breakneck
speed, and nursing and health care must be ready to ride this roller coaster. Programs need to be developed to keep nurses and healthcare workers abreast
of the new technological changes as they occur, and educating new and current nurses presents a significant challenge to the INS. Overall, the INS’s
future looks very promising and rewarding.
In the future, all healthcare providers will likely receive education on informatics. All healthcare providers need basic informatics
skills, such as the ability to use search engines to find information about a specific topic. Consequently, all healthcare providers need
to be able to attend classes to improve their computer literacy. Those entering the nursing field need a general knowledge of computer
capabilities. Many new trends—such as Web 2.0, increased attention to evidence-based practice, and a better understanding of
genomics—will impact care delivery in the 21st century, and NI nurses need to be prepared to lead these efforts to improve care
(Bakken et al., 2008).
Change plays a significant part in health care today, and those interested in NI must embrace change. They must also be good at
enticing others to embrace change. Nevertheless, NI candidates must realize that change is often accompanied by resistance. For their
part, INSs must be ready to leave the bedside, because nurses entering into this field will no longer be giving hands-on care.
NI is a very challenging but very rewarding field. In an ideal world, all healthcare agencies will employ at least one INS, and all
nurses will embrace the knowledge worker title.
Summary
Nursing informatics is an emerging nursing specialty that combines nursing science, information science, and computer science.
Informatics practices support nurses as they seek to care for their patients effectively and safely, by making the information that they
need more readily available. Nurses have been actively involved in this field since computers were introduced to health care. With the
advent of the EHR, it became apparent that nursing needed to develop its own terminology related to the new technology and its
applications; NI has been instrumental in this process.
Today, the healthcare industry employs the largest number of knowledge workers in the world. As a consequence of this trend,
healthcare administrators now realize that they must begin to change the way that they view their employees. Nurses and physicians
are bright, highly skilled, and dedicated to giving the best patient care. Administrators who tap into this wealth of knowledge will
discover that they have happier employees and find that patient care has become safer and more efficient.
NI is a specialty governed by standards that have been established by the ANA. Because NI is a very diverse field, many INSs
eventually specialize in one segment of the field. NI is a recognized specialty, but it affects all nurses. Nursing informatics
competencies have been developed to ensure that all entry-level nurses are ready to enter the more technologically advanced field of
nursing. These competencies may be used to determine the educational needs of current staff members.
The growth of the NI field has resulted in the formation of numerous NI organizations or subgroups of the medical informatics
organizations. Nurses no longer have to enter the field by chance but can obtain an advanced degree in NI at many well-established
universities. In addition, INSs may continue their learning by attending the numerous conferences offered.
NI has grown tremendously as a specialty since its inception and has the expectation of continued growth. It will be interesting to
see where technology takes health care in the future.
THOUGHT-PROVOKING
QUESTIONS
1. A hospital is seeking to implement an EHR. It has been suggested that an INS be hired. This position does not involve direct patient care and the
administration is struggling with how to justify the position. How can this position be justified?
2. This chapter discusses the fact that nurses are knowledge workers. How does nursing move from measuring the tasks completed to measuring the
final outcome of the patient?
References
Alliance of Nursing Informatics. (2013). Homepage. http://www.allianceni.org American Medical Informatics Association. (2013). Homepage.
http://www.amia.org
American Nurses Association (ANA). (2008). Nursing informatics: Scope and standard of practice. Silver Spring, MD: Nursesbooks.org.
American Nursing Informatics Association (ANIA). (2013). Homepage. http://www.ania.org Androwich, I. (2010, June). Paper presented at Delaware
Valley Nursing Informatics Annual Meeting, Malvern, PA.
Bakken, S., Stone, P., & Larson, E. (2008). A nursing informatics research agenda for 2008–2018: Contextual influences and key components. Nursing
Outlook, 56(5), 206–214.
Barton, A. J. (2005). Cultivating informatics competencies in a community of practice. Nursing Administration Quarterly, 29(4), 323–328.
Brennan, P. F. (1994). On the relevance of discipline to informatics. Journal of the American Medical Informatics Association, 1(2), 200–201.
Davenport, T. H., Thomas, R., & Cantrell, S. (2002). The mysterious art and science of knowledge worker performance. MIT Sloan Management
Review, 44(1), 23–30.
Drucker, P. F. (1992). The new society of organizations. Harvard Business Review, 70(5), 95–104.
Drucker, P. F. (1994). The age of social transformation. Atlantic Monthly, 274(5), 52–80.
Health Information and Management Systems Society (HIMSS). (2006). HIMSS dictionary of healthcare information technology terms, acronyms and
organizations. Chicago, IL: Author.
Health Information and Management Systems Society (HIMSS). (2013). Homepage. http://www.himss.org
HIMSS Nursing Informatics Awareness Task Force. (2007). An emerging giant: Nursing informatics. Nursing Management, 13(10), 38–42.
McCormick, J. (2009, May 14). Preparing for the future of knowledge work: A day in the life of the knowledge worker. Infomanagment Direct Online.
http://www.information-management.com/infodirect/2009_121/knowledge_worker_information_management_ecm_content_management-
10015405-1.html?pg=1
McNeil, B. J., Elfrink, V., Beyea, S. C., Pierce, S., & Bickford, C. J. (2006). Computer literacy study: Report of qualitative findings. Professional
Nursing, 22(1), 52–59.
Merriam-Webster Online. (2011). http://mw1.merriam-webster.com/dictionary/worker
O’Connor, J. J., & Robertson, E. F. (2003). Florence Nightingale biography. http://www-history.mcs.st-
andrews.ac.uk/history/Printonly/Nightingale.html
Saba, V. K., & McCormick, K. A. (Eds.). (2006). Essentials of nursing informatics (4th ed.). New York, NY: McGraw-Hill.
Snyder-Halpern, R., Corcoran-Perry, S., & Narayan, S. (2001). Developing clinical practice environments supporting the knowledge work of nurses.
Computers in Nursing, 19(1), 17–26.
Sorrells-Jones, J., & Weaver, D. (1999a). Knowledge workers and knowledge-intense organizations, Part 1: A promising framework for nursing and
healthcare. Journal of Nursing Administration, 29(7/8), 12–18.
Sorrells-Jones, J., & Weaver, D. (1999b). Knowledge workers and knowledge-intense organizations, Part 3: Implications for preparing healthcare
professionals. Journal of Nursing Administration, 29(10), 14–21.
Staggers, N., Gassert, C., & Curran, C. (2001). Informatics competencies for nurses at four levels of practice. Journal of Nursing Education, 40(7), 303–
316.
Staggers, N., Gassert, C., & Curran, C. (2002). A Delphi study to determine informatics competencies for nurses at four levels of practice. Nursing
Research, 51(6), 383–390.
Thede, L. Q. (2003). Informatics and nursing: Opportunities and challenges (2nd ed.). Philadelphia, PA: Lippincott Williams & Wilkins.
TIGER Initiative. (2006). Welcome to TIGER! http://www.tigersummit.com
Weaver, D., & Sorrells-Jones, J. (1999). Knowledge workers and knowledge-intense organizations, Part 2: Designing and managing for productivity.
Journal of Nursing Administration, 29(9), 19–25.
Wickramasinghe, N., & Ginzberg, M. J. (2001). Integrating knowledge workers and the organization: Role of IT. International Journal of Health Care
Quality Assurance, 14(6), 245–253.
Chapter 8
Information and Knowledge Needs of Nurses in the 21st
Century
Lynn M. Nagle, Nicholas Hardiker, Kathleen Mastrian, and Dee McGonigle
OBJECTIVES
1. Describe the goal of nursing informatics.
2. Explore the need for consistent terminology in nursing.
3. Describe the different approaches to terminology development.
4. Describe how clinical information technologies are impacting and will impact nursing practice.
5. Explore how nurses can create and derive clinical knowledge from information systems.
6. Speculate on the future of nursing in the context of health informatics.
Key Terms
Accessibility
Clinical decision support
Clinical information system
Enumerative approach
Evidence-based practice
International Classification of Nursing Practice
Nursing informatics
Nursing knowledge
Ontological approach
Ontology
Reusability
Standardized nursing terminology
Term
Terminology
Ubiquity
Introduction
The information and knowledge informing the 21st century of healthcare delivery have been growing at an unprecedented pace in
recent years. Research in particular has propelled the understanding of the efficacy of various clinical practices, treatment regimens,
and interventions. Extended and expanded access to clinical research findings and decision support tools has been significantly
influenced by the advent of computerization and the Internet. Indeed, the conduct of research itself has been accelerated by virtue of
ubiquitous computing. Working in environments of increasingly complex clinical care and contending with the management of large
volumes of information, nurses need to avail themselves of the technological tools that can support quality practice that is optimally
safe, informed, and knowledge based. Although the increased deployment of information technologies within healthcare settings
presumes that nurses and other health professionals are proficient in the use of computing devices, the processes and potential
outcomes associated with informatics are yet to be fully realized or understood. Nurses need to participate in the creation of those
possibilities.
This chapter defines and addresses the goal of informatics as it relates to nursing. More specifically, benefits to be derived from the
creation of a culture of knowledge-based nursing practice that is enabled and advanced through the use of information and
communication technologies are described. The chapter also addresses some of the challenges associated with the attainment of this
goal, as well as the opportunities for nurses to create and derive knowledge from emerging health information technologies. Finally,
the chapter provides a contemplative view of the future for nurses and informatics.
CASE STUDY: CASTING TO THE FUTURE
In the year 2025, nursing practice enabled by technology has created a professional culture of reflection, critical inquiry, and interprofessional
collaboration. Nurses use technology at the point of care in all clinical settings (e.g., primary care, acute care, community, and long-term care) to
inform their clinical decisions and effect the best possible outcomes for their clients. Information is gathered and retrieved via human–technology
biometric interfaces including voice, visual, sensory, gustatory, and auditory interfaces, which continuously monitor physiologic parameters for
potentially harmful imbalances. Longitudinal records are maintained for all citizens from their initial prenatal assessment to death; all lifelong
records are aggregated into the knowledge bases of expert systems. These systems provide the basis of the artificial intelligence being embedded in
emerging technologies. Smart technologies and invisible computing are ubiquitous in all sectors where care is delivered. Clients and families are
empowered to review and contribute actively to their record of health and wellness. Invasive diagnostic techniques are obsolete, nanotechnology
therapeutics are the norm, and robotics supplement or replace much of the traditional work of all health professions. Nurses provide expertise to
citizens to help them effectively manage their health and wellness life plans, and navigate access to appropriate information and services.
Definition and Goal of Informatics
Recall the definition of nursing informatics (NI) presented in the Overview of Nursing Informatics chapter (Staggers & Thompson,
2002):
A specialty that integrates nursing science, computer science, and information science to manage and communicate data,
information, and knowledge in nursing practice. NI facilitates the integration of data, information, and knowledge to support
patients, nurses, and other providers in their decision-making in all roles, and settings. This support is accomplished through
the use of information structures, information processes, and information technology. (p. 260)
Nurses in identified informatics roles typically focus their efforts on articulating meaningful clinical nursing data and information
structures that can be codified and processed; identifying the information processes associated with nurses’ work; and determining
ways in which information and communication technologies can be most effectively used to support the capture, retrieval, and use of
data, information, and knowledge. For nurses in other roles, however, the term “informatics” remains substantively obscure and
misunderstood, if understood at all, as do the relevance and importance of the associated work.
More timely access to data and information—both clinical and financial—has been identified as a necessity in the climate of
healthcare delivery in the 21st century (Hannah, 1995). Health service organizations, societies, and governments throughout the
industrialized world are obsessed with ensuring that healthcare delivery is safer, knowledge based, cost-effective, seamless, and
timely. Beyond these deliverables, there are expectations of improved efficiency and quality and of the active engagement of
consumers in their care. In particular, given the evolving emphasis on such issues as chronic disease management and aging at home,
the goals of informatics need to include the use of technologies to empower citizens to manage their own health and wellness more
effectively.
A current challenge within the nursing profession is the pending human resources crisis and dire projections of imminent nurse
shortages. Consequently, nursing’s focus on information technology (IT) has been elevated as a central means by which nurses can be
sufficiently supported in their work environments. Most importantly, IT has the potential to reduce the waste of valuable nursing
resources by reducing the time spent in the “care and feeding” of patient records. Having more time for direct client care that is
supported by ready access to information and knowledge translates into the provision of safer, higher quality care. Nurses need to be
appropriately equipped with the tools to manage data, information, and knowledge effectively and efficiently. The work of nurse
informaticists has become germane to the future of all nurses’ work.
Health Information Technologies Impacting Nursing
As established in the Informatics Roles and the Knowledge Work of Nursing chapter, nurses are classified as knowledge workers.
Studies have identified that, depending on the setting, nurses spend between 25% and 50% of their day managing and recording
clinical information and seeking knowledge to inform their practice (Gugerty et al., 2007). Nurses gather atomic-level data (e.g., blood
pressure, pulse, blood glucose, pallor), aggregate data to derive information (e.g., impending shock), and apply knowledge (e.g.,
lowering the head of the bed to minimize the potentially deleterious effects of impending shock). Over the years, these data have been
recorded into individuals’ hard-copy health records, thereby chronicling findings, actions, and outcomes; these data and information
are then forever lost unless manually extracted for research purposes.
As evidence to support nursing practice continues to be uncovered by researchers and integrated into healthcare delivery, attention
must be given to the tools that afford ready and easy access to this evidence. It could be suggested that as knowledge workers, nurses
are also, albeit unwittingly, informaticians to a large extent. With the advent of clinical information systems (CISs), specifically
electronic documentation and clinical decision support (CDS) applications, every nurse has the capacity to contribute to the
advancement of nursing knowledge on many levels. Imagine the use of IT solutions to capture not only discrete, quantifiable data, but
also the nurse’s experiential and intuitive personal knowledge not typically documented in paper records. Further add to that mix the
family history, culture, environmental and social factors, past experiences, and perspectives from patients and families, and it becomes
clear that the possibilities for generating new understandings within populations and across the life span and care continuum are
endless.
The impact of CISs on the practice of nursing is just beginning to be explicated because the opportunities to study wholly
computerized clinical environments have been limited to date. As yet, the evidence that CISs actually improve nursing efficiency has
been inconclusive. Poissant, Pereira, Tamblyn, and Kawasumi (2005) found that bedside terminals and central station desktops
resulted in a 24% reduction in the time nurses spent on documentation activities. However, surveys of nurses’ perceptions and attitudes
toward CISs have suggested that although the quality of documentation may improve, the amount of time associated with completing
computer-related tasks increases (DesRoches, Donelan, Buerhaus, & Zhonghe, 2008; Kossman & Scheidenhelm, 2008). Others have
not found a significant time savings for nurses using specific CIS applications (Asaro & Boxerman, 2008; Franklin, O’Grady, Donyai,
Jacklin, & Barber, 2007; Hakes & Whittington, 2008). A 2005 Health Information Management Systems Society survey of nurses (n =
1,760) revealed that most nurses believe that CISs improve patient safety (86%) and facilitate interdisciplinary collaboration (69%)
and independent decision making (72%) (Dykes et al., 2005).
A 2011 study by Hripcsak, Vawdrey, Fred, and Bostwick used clinical log data to track various members of the healthcare team
and their time spent generating and reading clinical notes. Nurses spent between 21.4 and 38.2 minutes per day authoring notes and
between 9.0 and 16.9 minutes per day viewing notes. This study did not track minutes per day spent on documenting items on flow
sheets reflecting the plan of care, which most likely accounts for the bulk of nursing documentation time. Perhaps the most interesting
finding of this study is the low rate at which members of the team actually read the notes generated by members of the team.
These findings are reflective of what promises to be a growing trend in clinical settings as the sophistication and functionality of
CISs continue to advance. However, more research is needed to understand the full extent of the impact of the current and future CISs
on nursing practice.
Nurses Creating and Deriving New Knowledge
Nursing Data Standards
There are major efforts under way—internationally through the International Council of Nurses’ (2010) International Classification
of Nursing Practice (ICNP) and in many other initiatives among and within countries—in which nurses are attempting to standardize
the language of nursing practice (Hannah, White, Nagle, & Pringle, 2009). These efforts are particularly important in the face of CISs,
because the capacity to enforce consistent nomenclatures that reflect the practice of nurses is now possible. Standardized language
gives both the nursing profession and healthcare delivery systems the capability to capture, codify, retrieve, and analyze the impact of
nursing care on client outcomes. For example, with the use and documentation of standardized client assessments, including risk
measures, interventions based on best practices, and consistently measured outcomes within different care settings and across the
continuum of care, there will be an ability to demonstrate more clearly the contributions and impact of nursing care through the
analysis of CIS outputs. Additionally, clinical outcomes can be further understood in the context of care environments, particularly
implications related to the availability of human and material resources to support care delivery. The standardization of clinical inputs
and outputs into CISs will eventually provide a rich knowledge base from which practice and research can be enhanced, and will
inform better administrative and policy decisions (Nagle, White, & Pringle, 2010).
Although significant progress has been made in this standardization work, it is still in its early days. Box 8-1 discusses
standardizing terminologies in nursing; it was contributed by Nicholas Hardiker (2011), a leader in the development of standardized
languages that support clinical applications of information and communication technology.
BOX 8-1 THE NEED FOR STANDARDIZED TERMINOLOGIES TO SUPPORT NURSING PRACTICE
Nicholas Hardiker
Agreement on the consistent use of a term, such as “impaired physical mobility,” allows that term to be used for a number of purposes: to provide
continuity of care from care provider to care provider, to ensure care quality by facilitating comparisons between care providers, or to identify trends
through data aggregation. Since the early 1970s, there has been a concerted effort to promote consistency in nursing terminology. This work
continues today, driven by the following increasing demands placed on health-related information and knowledge:
Accessibility: It should be easy to access the information and knowledge needed to deliver care or manage a health service.
Ubiquity: With changing models of healthcare delivery, information and knowledge should be available anywhere.
Longevity: Information should be usable beyond the immediate clinical encounter.
Reusability: Information should be useful for a range of purposes.
Without consistent terminology, nursing runs the risk of becoming invisible; it will remain difficult to quantify nursing, the unique contribution and
impact of nursing will go unrecognized, and the nursing component of electronic health record systems will remain at best rudimentary. Not least,
without consistent terminology, the nursing knowledge base will suffer in terms of development and in terms of access, thereby delaying the
integration of evidence-based health care into nursing practice.
External pressures merely compound this problem. For example, in the United States, the Health Information Technology for Economic and
Clinical Health (HITECH) Act, signed in January 2009, provides a financial incentive for the use of electronic health records; similar steps are being
taken in other regions. The HITECH Act mandates that electronic health records are used in a meaningful way; achieving this goal will be
problematic without consistent terminology (see the Legislative Aspects of Nursing Informatics: HITECH and HIPAA chapter for more information
on the HITECH Act). Finally, the current and future landscape of information and communication technologies (e.g., connection anywhere,
borderless communication, Web-based applications, collaborative working, disintermediation and reintermediation, consumerization, ubiquitous
advanced digital content [van Eecke, da Fonseca Pinto, & Egyedi, 2007]) and their inevitable infiltration into health care will only serve to reinforce
the need for consistent nursing terminology while providing an additional sense of urgency.
This box explains what is meant by a standardized nursing terminology and lists several examples. It describes in detail the different approaches
taken in the development of two example terminologies. It presents, in the form of an international technical standard, a means of ensuring
consistency among the plethora of contemporary standardized nursing terminologies, with a view toward harmonization and possible convergence.
Finally, it provides a rationale for the shared development of models of terminology use—models that embody both clinical and pragmatic
knowledge to ensure that contemporary nursing record systems reflect the best available evidence and fit comfortably with routine practice.
STANDARDIZED NURSING TERMINOLOGIES
A term at its simplest level is a word or phrase used to describe something concrete (e.g., leg) or abstract (e.g., plan). A nursing terminology is a
body of the terms used in nursing. Many nursing terminologies exist, both formal and informal. Nursing terminologies allow nurses to consistently
capture, represent, access, and communicate nursing data, information, and knowledge. A standardized nursing terminology is a nursing
terminology that is in some way approved by an appropriate authority (de jure standardization) or by general consent (de facto standardization).
In North America, one such authority is the American Nurses Association (ANA, 2007), which operates a process of de jure standardization
through its Committee for Nursing Practice Information Infrastructure (CNPII; http://www.nursingworld.org/MainMenuCategories/Policy-
Advocacy/Positions-and-Resolutions/ANAPositionStatements/Position-Statements-Alphabetically/PrivacyandConfidentiality.html). The ANA-
approved list of nursing languages was presented in the Overview of Nursing Informatics chapter.
CNPII has also recognized two data element sets: the Nursing Minimum Data Set (NMDS) and the Nursing Management Minimum Data Set
(NMMDS). Work on a standardized data element set for nursing, which in the United States began in the 1980s with the NMDS (Werley & Lang,
1988), provided an additional catalyst for the development of many of the aforementioned nursing terminologies that could provide values (e.g.,
chronic pain) for particular data elements in the NMDS (e.g., nursing diagnosis). The data element sets provide a framework for the uniform
collection and management of nursing data; the use of a standardized nursing terminology to represent those data serves further to enhance
consistency.
APPROACHES TO NURSING TERMINOLOGY
From relatively humble beginnings, nursing terminologies have evolved significantly over the past several decades in line with best practices in
terminology work. The enumerative approach consists of simple lists of words or phrases represented in a list or a simple hierarchy. In the nursing
diagnosis terminology system of the North American Nursing Diagnosis Association (NANDA), a nursing diagnosis has an associated name or label
and a textual definition (NANDA International, 2008). Each nursing diagnosis may have a set of defining characteristics and related or risk factors.
These additional features do not constitute part of the core terminology but instead are intended to be used as an aid to diagnosis. What an
enumerative approach to standardizing terminology may lack in terms of hierarchical sophistication, it makes up for in terms of simplicity and
potential ease of implementation and use.
In contrast, the ontological approach is compositional in nature and provides a partial representation of the entities within a domain and the
relationships that hold between them. The evolution of this approach to terminology standardization has been facilitated by advances in knowledge
representation (e.g., the refinement of the description logic that underpins many contemporary ontologies) and in their accompanying technologies
(e.g., automated reasoners that can check consistency and identify equivalence) as well as the subsumption (i.e., subclass–superclass) relationships
within those ontologies.
ICNP version 2 is an example of an ontology. ICNP is described as a unified nursing language system. It seeks to provide a resource that can be
used to develop local terminologies and to facilitate cross-mapping between terminologies to compare and combine data from different sources; the
existence of a number of overlapping but inconsistent standardized nursing terminologies is problematic in terms of data comparison and
aggregation. The core of ICNP is represented in the Web ontology language (OWL), a recommendation of the World Wide Web Consortium (W3C)
and a de facto standard language for representing ontologies (McGuiness & van Harmelen, 2004). Because it is underpinned by description logic,
OWL permits the use of automated reasoners that can check consistency, identify equivalence, and support classification within the ICNP ontology.
The results of contemporary terminology work are encouraging. Nevertheless, further work is needed to harmonize standardized nursing
terminologies and to scale up and mainstream the development and implementation of models of terminology use.
In an ideal world, one would see standardized nursing terminologies and the structures and systems that support their implementation and use
merely as means to an end—that is, as tools to support good nursing practice and good patient care. Standardized nursing terminologies are
important, but they do not obviate the need to think and work creatively, to do right by the people in our care, and to continue to advance nursing.
REFERENCES
American Nurses Association (ANA). (2007). Nursing practice information infrastructure.
http://www.nursingworld.org/MainMenuCategories/Policy-Advocacy/Positions-and-Resolutions/ANAPositionStatements/Position-Statements-
Alphabetically/PrivacyandConfidentiality.html
McGuiness, D. L., & van Harmelen, F. (Eds.). (2004). OWL Web ontology language overview. World Wide Web Consortium.
http://www.w3.org/TR/owl-features
NANDA International. (2008). Nursing diagnoses: Definitions and classification 2009–2011 edition. Indianapolis, IN: Wiley-Blackwell.
van Eecke, P., da Fonseca Pinto, P., & Egyedi, T., for the European Commission. (2007). EU study on the specific policy needs for ICT
standardisation [Final report]. http://ec.europa.eu/enterprise/ict/policy/doc/2007-ict-std-full-rep
Werley, H. H., & Lang, N. M. (Eds.). (1988). Identification of the Nursing Minimum Data Set. New York, NY: Springer.
Integrated Decision Support Tools
CDS tools have evolved beyond the previously prevailing notion of these tools as accessible reference texts and written resource
materials, such as policies and procedures. In the world of clinical computing, the capability to link various information sources and
present a clinician with immediate guidance and support has begun to net benefits for safer care and improved clinical outcomes.
Osheroff and colleagues (2007) defined CDS as tools that “provide clinicians, staff, patients, or other individuals with knowledge and
person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care” (p. 141).
Most available CDS tools for nursing practice, although promising, are simplistic and in early development. Typically, CDS
includes such tools as (1) computerized alerts and reminders (e.g., medication due, patient has an allergy, potassium level abnormal);
(2) clinical guidelines (e.g., best practice for prevention of skin breakdown); (3) online information retrieval (e.g., CINAHL, drug
information); (4) clinical order sets and protocols; and (5) online access to organizational policies and procedures. In the future, these
tools may be expanded to include applications with embedded case-based reasoning.
Nursing Knowledge in Evolution
Many renowned nurse authors have described the knowledge used by nurses (Benner, 1983; Carper, 1978; Schultz & Meleis, 1988).
According to Carper (1978), “nursing … depends on the scientific knowledge of human behavior in health and in illness, the esthetic
perception of significant human experiences, a personal understanding of the unique individuality of the self and the capacity to make
choices within concrete situations involving particular moral judgments” (p. 22).
In the context of nursing practice supported by CISs, nurses will eventually have access to evidence and knowledge derived from
large aggregates of clinical data, including nursing interventions and resultant outcomes. Experiential evidence provides practice
guidelines and directives to ensure concurrence with optimal clinical decisions and actions. To illustrate, consider this example: A
nurse assesses a patient who has experienced a stroke for signs of skin breakdown, photographs and documents early ulcerations, and
submits the photos and documentation to CIS. The nurse receives an option to review the best practices for care of the patient and to
submit a request for a consult to a wound management specialist. The ongoing clinical findings, treatment, and response are logged
and aggregated with similar cases, thereby contributing to the knowledge base related to nursing and care of the integumentary system.
The informational elements of CISs can also be designed to include specifics about individuals’ multicultural practices and beliefs.
Consider the situation where a client voices concerns about her prescribed dietary treatment and expresses a preference for a female
care provider. With a query to the CIS for the client’s history and sociocultural background, the nurse obtains explanations for these
requests that derive from the patient’s religious and cultural background and makes a notation to highlight and carry this information
forward for any future admissions. Future systems may also be designed to provide access to standards of ethical practice and online
access to experts in the field of moral reasoning to guide clinical interactions and decision making.
Through each and every instance of interacting with the CIS, nurses add to these repositories of knowledge by chronicling their
daily clinical challenges and queries. The continued expansion and aggregation of knowledge about clients and populations; their
personal, cultural, physical, and clinical presentations; and individuals’ experiences and the guidance received from others enhance the
delivery of personalized, knowledge-based care.
Generating Nursing Knowledge
Graves and Corcoran (1989) have suggested that nursing knowledge is “simultaneously the laws and relationships that exist between
the elements that describe the phenomena of concern in nursing (factual knowledge) and the laws or rules that the nurse uses to
combine the facts to make clinical nursing decisions” (p. 230). In their view, not only does knowledge support decision making, but it
also leads to new discoveries. Thus one might think about the future creation of nursing knowledge as being the discovery of new laws
and relationships that can continue to advance nursing practice.
New technologies have made the capture of multifaceted data and information possible through the use of such technologies as
digital imaging (e.g., photography to support wound management). Now included as part of the clinical record, such images add a new
dimension to the assessment, monitoring, and treatment of illness and the maintenance of wellness. Beyond the use of computer
keyboards, input devices are being integrated with CISs and used to gather data and information for the following clinical and
administrative purposes:
Biometrics (e.g., facial recognition, security)
Voice and video recordings (e.g., client interviews and observations, diagnostic procedures, ultrasounds)
Voice-to-text files (e.g., voice recognition for documentation)
Medical devices, (e.g., infusion pumps, ventilators, hemodynamic monitors)
Bar-code and radio-frequency identification (RFID) technologies (e.g., medication administration)
Telehomecare monitoring (e.g., for use in diabetes and other chronic disease management)
These are but a few of the emerging capabilities that allow for numerous data inputs to be transposed, combined, analyzed, and
displayed to provide information and views of clinical situations currently not possible in a world dominated by hard-copy
documentation. Through the application of information and communication technologies to support the capture and processing (i.e.,
interpretation, organization, and structuring) of all relevant clinical data, relationships can be identified and formalized into new
knowledge. This transformational process is at the core of generating new nursing knowledge at a rate never experienced before; in the
context of current research paradigms, the same relationships would likely take years to uncover.
As CISs advance, nurses will eventually become generators of new knowledge by virtue of designs that embed machine learning
and case-based reasoning methods within their core functionality. This functionality will become possible only with national and
international adoption of standardized nursing language, as previously described. Imagine the power of having access to systems that
aggregate the same data elements and information garnered from multiple clinical situations and provide a probability estimate of the
likely outcome for individuals of a certain age, with a specific diagnosis and comorbid conditions, medication profile, symptoms, and
interventions. How much more rapidly would an understanding of the efficacy of clinical interventions be elucidated? Historically,
some knowledge might have taken years of research to discover (e.g., that long-standing practices are sometimes more harmful than
beneficial). A case in point is the long-standing practice of instilling endotracheal tubes with normal saline before suctioning (O’Neal,
Grap, Thompson, & Dudley, 2001). Based on the evidence gathered through several studies, the potentially deleterious effects of this
practice have become widely recognized. Conceivably, a meta-analysis approach to clinical studies will be expedited by convergence
of large clinical data repositories across care settings, thereby making available to practitioners the collective contributions of health
professionals and longitudinal outcomes for individuals, families, and populations.
Nurses need to be engaged in the design of CIS tools that support access to and the generation of nursing knowledge. Of particular
importance to the future design of CIS is the adoption of clinical data standards. Although this research is beyond the scope of this
chapter, at least two decades of work effort has been directed toward articulating standardized data elements that reflect nursing
practice. The nursing profession has been steadily moving toward consensus on the adoption of data standards, and recent work
suggests that significant strides have been achieved (Bickford & Hunter, 2006; Delaney, 2006; Hannah et al., 2009). Consider that as
CISs are widely implemented, as standards for nursing documentation and reporting are adopted, and as healthcare IT solutions
continue to evolve, the synthesis of findings from a variety of methods and worldviews becomes much more feasible.
Challenges in Getting There
Leadership
The number of nurse leaders in health informatics has grown to a marked extent in the past two decades. Nevertheless, a significant
knowledge gap remains to be addressed within the nursing leadership community. Many nurse leaders need to acquire a new set of
skills and knowledge to understand and advance the adoption of information tools and technologies to support the delivery of clinical
care. For several years, nurse informaticians have advocated for the need for all nursing leaders to become knowledgeable and engaged
in setting the direction for informatics in the profession (Nagle, 2005; Pringle & Nagle, 2009; Simpson, 2000). Strategies for achieving
this goal include the following: (1) identify the informatics education needs of nurse leaders, (2) develop mentorship programs for the
acquisition of informatics leadership skills, and (3) ensure enrollment of nurse leaders as sponsors for electronic health record (EHR)
initiatives.
Clinical Practice
Despite valiant efforts to implement comprehensive CIS throughout North American healthcare settings, there are still many provider
organizations with limited online functionality available to nurses. As indicated by numerous studies and reports on the state of IT
adoption, many providers remain in the early phases of CIS acquisition and implementation (Eggert & Protti, 2006). In an unexpected
twist, this lag is probably good news for nursing; it means there is an opportunity for nurses to immerse themselves in the
developmental work of IT solutions to support practice.
Over the years, nurses have frequently been on the receiving end of systems that either did not add value to their work or, by virtue
of their poor design, created additional work. Now nurses have the opportunity to head off future installations of IT solutions that do
nothing to benefit and support the clinical practice of nurses and healthcare teams. It behooves nurses to become engaged in the
acquisition, design, implementation, and evaluation of CIS to ensure that they realize the benefits of these systems for clinical care and
outcomes.
It is equally important to consider that because of the average age of most practicing nurses, many have yet to develop a comfort
level with the use of computers in their work settings. To minimize the anxiety associated with expected IT use, particular attention
needs to be given to the issue of computer literacy. If nurses lack a solid footing in computer use, expectations for integration of
informatics will prove difficult to realize. Strategies for nurses include the following: (1) seek encouragement and support to
participate in the acquisition, design, implementation, and evaluation phases of CIS; (2) demand the adoption of IT solutions that
support the delivery of safe, quality care; and (3) obtain the material and people resources needed to support their acquisition of
informatics competencies.
Education
Over the years, numerous efforts have been undertaken to identify the core informatics competencies needed by nurses. These efforts
have encompassed attempts to articulate core competencies for all nurses, from novice to expert (Hebert, 2000) and competencies for
informatics experts (Hersh, 2006). In recognizing NI as a specialty, the American Nurses Association (2008) has articulated scope and
standards of NI practice. What remains clear is that although progress has been made in the preparation of NI experts, much work
remains to be done at the grassroots level of nursing education.
Studies of schools of nursing indicate that few basic nursing education programs have embedded the concepts and processes
associated with informatics within the core curricula (Carty & Rosenfeld, 1998; Nagle & Clarke, 2004). Nevertheless, informatics
content is now being slowly integrated into curricula. The primary obstacles to realizing curricula with embedded informatics concepts
include a lack of faculty capacity, constraints of clinical practice environments (e.g., lack of student access to CISs), and limited
funding. These barriers need to be addressed to ensure that graduates of the future are prepared to work in settings using information
technology to support clinical care.
The core concepts and competencies of informatics are particularly well suited to a model of interprofessional education. Ideally,
when emulating clinical settings, informatics knowledge should be integrated with the processes of interprofessional teamwork and
decision making. Because simulation laboratories are becoming increasingly common fixtures in the delivery of health professional
education, they provide a perfect opportunity to incorporate EHR applications, including access to CDS. The learning laboratory will
then more closely approximate the IT-enabled clinical settings that are emerging in the real world.
An assumption is often made that future graduates will be more computer literate than the nurses currently in practice. Although
this is likely true, computer comfort does not equate to an understanding of the facilitative and transformative role that IT will have in
the future. It is essential that the future curricula of basic nursing programs incorporate the concepts related to the role of information
technology in supporting clinical care delivery. Strategies to address the educational issues related to CIS include the following: (1)
share prototypes of informatics integration among schools of nursing, (2) consider interprofessional education opportunities in
addressing informatics concepts and competencies, (3) obligate nursing faculty to attain basic informatics competencies and support
them as they do so, (4) seek and allocate funding for the development of innovative curricular models and associated technological
support, and (5) incorporate accreditation criteria that necessitate an integration of informatics core concepts and competencies in all
basic nursing programs.
In an Ideal World
The ideal is a nursing practice that has wholly integrated informatics and nursing education and that is driven by the use of information
and knowledge from a myriad of sources, creating practitioners whose way of being is grounded in informatics. Nursing research is
dynamic and an enterprise in which all nurses are engaged by virtue of their use of technologies to gather and analyze findings that
inform specific clinical situations. In every practice setting, the contributions of nurses to health and wellbeing of citizens will be
highly respected and parallel, if not exceed, the preeminence granted physicians.
The Future
The future landscape is yet to be fully understood, as technology continues to evolve with a rapidity and unfolding that is rich with
promise and potential peril. It is anticipated that computing power will be capable of aggregating and transforming additional
multidimensional data and information sources (e.g., historical, multisensory, experiential, and genetic sources) into CIS. With the
availability of such rich repositories, further opportunities will open up to enhance the training of health professionals, advance the
design and application of CDSs, deliver care that is informed by the most current evidence, and engage with individuals and families
in ways yet unimagined.
The basic education of all health professions will evolve over the next decade to incorporate core informatics competencies. In
general, the clinical care environments will be connected, and information will be integrated across disciplines to the benefit of care
providers and citizens alike. The future of health care will be highly dependent on the use of CIS and CDS to achieve the global
aspiration of safer, quality care for all citizens.
Summary
This chapter advanced the view that every nurse’s practice will make contributions to new nursing knowledge in dynamically
interactive CIS environments. The core concepts and competencies associated with informatics will become embedded in the practice
of every nurse, whether administrator, researcher, educator, or practitioner. Informatics will be prominent in the knowledge work of
nurses, yet it will be a subtlety because of its eventual fulsome integration with clinical care processes. Clinical care will be
substantially supported by the capacity and promise of technology today and tomorrow.
Most importantly, readers need to contemplate a future without being limited by the world of practice as it is known today.
Information technology is not a panacea for all of the challenges found in health care, but it will provide the nursing profession with an
unprecedented capacity to generate and disseminate new knowledge at rapid speed. Realizing these possibilities necessitates that all
nurses understand and leverage the informatician within and contribute to the future.
THOUGHT-PROVOKING
QUESTIONS
1. What are the possibilities to accelerate the generation and uptake of new nursing knowledge?
2. What should be the areas of priority for the advancement of informatics in nursing?
3. How can a single agreed-upon model of terminology use (with linkages to a single terminology) help to integrate knowledge into routine clinical
practice?
References
American Nurses Association (ANA). (2008). Scope and standards of nursing informatics practice. Washington, DC: Author.
Asaro, P.V., & Boxerman, S. B. (2008). Effects of computerized provider order entry and nursing documentation on workflow. Journal of the American
Medical Informatics Association, 14(4), 415–423.
Benner, P. (1983). Uncovering the knowledge embedded in clinical practice. Image: The Journal of Nursing Scholarship, 15(2), 36–41.
Bickford, C. J., & Hunter, K. M. (2006). Theories, models, and frameworks. In V. K. Saba & K. A. McCormick (Eds.), Essentials of nursing informatics
(4th ed., pp. 265–278). New York, NY: McGraw-Hill.
Carper, B. A. (1978). Fundamental patterns of knowing in nursing. Advances in Nursing Science, 1(1), 13–23.
Carty, B., & Rosenfeld, P. (1998). From computer technology to information technology: Findings from a national study of nursing education.
Computers in Nursing, 16(5), 259–265.
Delaney, C. W. (2006). Nursing minimum data set systems. In V. K. Saba & K. A. McCormick (Eds.), Essentials of nursing informatics (4th ed., pp.
249–261). New York, NY: McGraw-Hill.
DesRoches, C. K., Donelan, P., Buerhaus, P., & Zhonghe, L. (2008). Registered nurses’ use of electronic health records: Findings from a national
survey. Medscape Journal of Medicine, 10(7), 164–173.
Dykes, P., Cashen, M., Foster, M., Gallagher, J., Kennedy, M., MacCallum, R., … Whetstone, S. (2005). Surveying acute care providers in the U.S. to
explore the impact of HIT on the role of nurses and the interdisciplinary communication in acute care settings. Journal of Healthcare Information
Management, 20(2), 36–44.
Eggert, C., & Protti, D. (2006). Clinical electronic communications: A new paradigm that is here to stay? Electronic Healthcare, 5(2), 88–96.
Franklin, B. D., O’Grady, K., Donyai, P., Jacklin, A., & Barber, N. (2007). The impact of a closedloop electronic prescribing and administration system
on prescribing errors, administration errors, and staff time: A before and after study. Quality and Safety in Health Care, 16(4), 279–284.
Graves, J. R., & Corcoran, S. (1989). The study of nursing informatics. Image: The Journal of Nursing Scholarship, 21(4), 227–231.
Gugerty, B., Maranda, M. J., Beachley, M., Navarro, V. B., Newbold, S., Hawk, W., … Wilhelm, D. (2007). Challenges and opportunities in
documentation of the nursing care of patients. Baltimore, MD: Documentation Work Group, Maryland Nursing Workforce Commission.
http://www.mbon.org/commission2/documentation_challenges
Hakes, B., & Whittington, J. (2008). Assessing the impact of an electronic medical record on nurse documentation time. Computers Informatics Nursing,
26(4), 234–241.
Hannah, K. J. (1995). Transforming information: Data management support of healthcare reorganization. Journal of the American Medical Informatics
Association, 2(3), 147–155.
Hannah, K. J., White, P., Nagle, L. M., & Pringle, D. M. (2009). Standardizing nursing information in Canada for inclusion in electronic health records:
C-HOBIC. Journal of the American Medical Informatics Association, 16(4), 524–530.
Hardiker, N. (2011). Developing standardized terminologies to support nursing practice. In D. McGonigle & K. Mastrian (Eds.), Nursing informatics
and the foundation of knowledge (2nd ed., pp. 11–119). Sudbury, MA: Jones & Bartlett Learning.
Hersh, W. (2006). Who are the informaticians? What we know and should know. Journal of the American Medical Informatics Association, 13(2), 166–
170.
Hripcsak, G., Vawdrey, D., Fred, M., & Bostwick, S. (2011). Use of electronic clinical documentation: Time spent and team interactions. Journal of the
American Medical Informatics Association, 18, 112–117.
International Council of Nurses. (2010). ICNP version 2.0. http://www.icn.ch/pillarsprograms/international-classification-for-nursing-practicer/
Kossman, S. P., & Scheidenhelm, S. L. (2008). Nurses’ perceptions of the impact of electronic health records on work and patient outcomes. Computers
Informatics Nursing, 26(2), 69–77.
Nagle, L. M. (2005). Dr. Lynn Nagle and the case for nursing informatics. Canadian Journal of Nursing Leadership, 18(1), 16–18.
Nagle, L. M., & Clarke, H. F. (2004). Assessing informatics in Canadian schools of nursing. Proceedings 11th World Congress on Medical Informatics
[CD]. San Francisco, CA.
Nagle, L. M., White, P., & Pringle, D. (2010). Realizing the benefits of standardized measures of clinical outcomes. Electronic Healthcare, 9(2), e3–e9.
O’Neal, P. V., Grap, M. J., Thompson, C., & Dudley, W. (2001). Level of dyspnea experienced in mechanically ventilated adults with and without saline
instillation prior to endotracheal suctioning. Intensive Critical Care Nursing, 17(6), 356–363.
Osheroff, J. A., Teich, J. M., Middleton, B., Steen, E. B., Wright, A., & Detmer, D. E. (2007). A roadmap for national action on clinical decision
support. Journal of the American Medical Informatics Association, 14(2), 141–145.
Poissant, L. J., Pereira, R., Tamblyn, R., & Kawasumi, Y. (2005). The impact of electronic health records on time efficiency of physicians and nurses: A
systematic review. Journal of the American Medical Informatics Association, 12(5), 505–516.
Pringle, D., & Nagle, L. M. (2009). Leadership for the information age: The time for action is now. Canadian Journal of Nursing Leadership, 22(1), 1–
6.
Schultz, P. R., & Meleis, A. I. (1988). Nursing epistemology: Traditions, insights, questions. Image: The Journal of Nursing Scholarship, 20(4), 217–
221.
Simpson, R. L. (2000). Need to know: Essential survival skills for the information age. Nursing Administration Quarterly, 25(1), 142–147.
Staggers, N., & Thompson, C. B. (2002). The evolution of definitions for nursing informatics: A critical analysis and revised definition. Journal of the
American Medical Informatics Association, 9(3), 255–261.
Chapter 9
Legislative Aspects of Nursing Informatics: HITECH and
HIPAA
Kathleen M. Gialanella, Kathleen Mastrian, and Dee McGonigle
OBJECTIVES
1. Describe the purposes of the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009.
2. Explore how the HITECH Act is enhancing the security and privacy protections of the Health Insurance Portability and Accountability Act
(HIPAA) of 1996.
3. Determine how the HITECH Act and its impact on HIPAA apply to nursing practice.
Key Terms
Access
Agency for Healthcare Research and Quality
American National Standards Institute
American Recovery and Reinvestment Act
Centers for Medicare and Medicaid Services
Certified EHR technology
Civil monetary penalties
Compliance
Confidentiality
Consequences
Electronic health record
Enterprise integration
Entity
Gramm-Leach-Bliley Act
Health disparities
Health information technology
Health Insurance Portability and Accountability Act
Health Level 7
Healthcare-associated infections
International Standards Organization
Meaningful use
National Institute of Standards and Technology
Office of Civil Rights
Office of the National Coordinator for Health Information Technology
Open systems interconnection
Patient-centered care
Policy
Privacy
Protected health information
Qualified electronic health record
Rights
Sarbanes-Oxley Act
Security
Standard
Standards-developing organizations
Treatment/payment/operations
Introduction
The federal Health Information Technology for Economic and Clinical Health Act of 2009 (HITECH Act; Leyva & Leyva, 2011),
enacted February 17, 2009, is part of the American Recovery and Reinvestment Act (ARRA). The ARRA, also known as the
“Stimulus” law, was enacted to stimulate various sectors of the U.S. economy during the most severe recession this country had
experienced since the Great Depression of the late 1920s and early 1930s. The health information technology (HIT) industry was one
area where lawmakers saw an opportunity to stimulate the economy and improve the delivery of health care at the same time. This
explains why the title of the HITECH Act contains the phrase “for Economic and Clinical Health.”
The ARRA is a lengthy piece of legislation that is organized into two major sections: Division A and Division B. Each division
contains several titles. Title XIII of Division A of the ARRA is the HITECH Act. It addresses the development, adoption, and
implementation of HIT policies and standards and provides enhanced privacy and security protections for patient information—an
area of the law that is of paramount concern in nursing informatics. Title IV of Division B of the ARRA is considered part of the
HITECH Act. It addresses Medicare and Medicaid HIT and provides significant financial incentives to healthcare professionals and
hospitals that adopt and engage in the “meaningful use” of electronic health record (EHR) technology.
This chapter presents an overview of the HITECH Act, including the Medicare and Medicaid HIT provisions of the law. Nurses
need to be familiar with the goals and purposes of this law, know how it enhances the security and privacy protections of the Health
Insurance Portability and Accountability Act (HIPAA) of 1996, and appreciate how it otherwise affects nursing practice in the
emerging EHR age. The concepts of “meaningful use” and “certified EHR technology” also are explored in this chapter.
Overview of the HITECH Act
At the time the HITECH Act was enacted, it was estimated that less than 8% of U.S. hospitals used a basic EHR system in at least one
of their clinical units, and less than 2% of U.S. hospitals had an EHR system in all of their clinical settings (Ashish, 2009). Not
surprisingly, the cost of an EHR system has been a major barrier to widespread adoption of this technology in most healthcare
facilities. The HITECH Act seeks to change that situation by providing each person in the United States with an EHR. In addition, a
nationwide HIT infrastructure will be developed so that access to a person’s EHR will be readily available to every healthcare provider
who treats the patient, no matter where the patient may be located at the time treatment is rendered.
Definitions
The HITECH Act includes some important definitions that anyone involved in nursing informatics should know:
“Certified EHR Technology”: an EHR that meets specific governmental standards for the type of record involved, whether it
is an ambulatory EHR used by office-based healthcare practitioners or an inpatient EHR used by hospitals. The specific
standards that are to be met for any such EHRs are set forth in federal regulations.
“Enterprise Integration”: “the electronic linkage of healthcare providers, health plans, the government and other interested
parties, to enable the electronic exchange and use of health information among all the components in the health care
infrastructure.
“Healthcare Provider”: hospitals, skilled nursing facilities, nursing homes, long-term care facilities, home health agencies,
hemodialysis centers, clinics, community mental health centers, ambulatory surgery centers, group practices, pharmacies and
pharmacists, laboratories, physicians, and therapists, among others.
“Health Information Technology” (HIT): “hardware, software, integrated technologies or related licenses, intellectual property,
upgrades, or packaged solutions sold as services that are designed for or support the use by healthcare entities or patients for the
electronic creation, maintenance, access, or exchange of health information.”
“Qualified Electronic Health Record”: “an electronic record of health-related information on an individual.” A “qualified”
EHR contains a patient’s demographic and clinical health information, including the medical history and a list of health
problems, and is capable of providing support for clinical decisions and entry of physician orders. It must also have the capacity
“to capture and query information relevant to health care quality” and “exchange electronic health information with, and
integrate such information from other sources” (Readthestimulus.org, 2009, pp. 32–35).
Purposes
The HITECH Act established the Office of the National Coordinator for Health Information Technology (ONC) within the U.S.
Department of Health and Human Services (HHS). The ONC is headed by the national coordinator, who is responsible for overseeing
the development of a nationwide HIT infrastructure that supports the use and exchange of information to achieve the following goals:
1. Improve healthcare quality by enhancing coordination of services between and among the various healthcare providers a
patient may have, fostering more appropriate healthcare decisions at the time and place of delivery of services, and preventing
medical errors and advancing the delivery of patient-centered care
2. Reduce the cost of health care by addressing inefficiencies, such as duplication of services within the healthcare delivery
system, and by reducing the number of medical errors
3. Improve people’s health by promoting prevention, early detection, and management of chronic diseases
4. Protect public health by fostering early detection and rapid response to infectious diseases, bioterrorism, and other situations
that could have a widespread impact on the health status of many individuals
5. Facilitate clinical research
6. Reduce health disparities
7. Better secure patient health information
Improving healthcare quality has been an ongoing challenge in the United States. According to the Agency for Healthcare
Research and Quality (AHRQ), quality health care is care that is “safe, timely, patient centered, efficient, and equitable” (AHRQ,
2009, p. 1). AHRQ, an agency within HHS, has been releasing a national healthcare quality report (NHQR) every year since 2003, and
the current report, not unlike previous reports, finds the quality of health care in this country to be “suboptimal” (p. 2). The NHQR has
also discussed the need for HIT to support the goal of improving quality of care.
Providers need reliable information about their performance to guide improvement activities. Realistically, HIT infrastructure
is needed to ensure that relevant data are collected regularly, systematically, and unobtrusively while protecting patient
privacy and confidentiality … Systems need to generate information that can be understood by end users and that are
interoperable across different institutions’ data platforms …
Quality improvement typically requires examining patterns of care across panels of patients rather than one patient at a
time … Ideally, performance measures should be calculated automatically from health records in a format that can be easily
shared and compared across all providers involved with a patient’s care. (AHRQ, 2009, p. 13)
The prevalence of healthcare-associated infections serves as an excellent example of how use of EHR technology and a
nationwide HIT infrastructure can play a significant role in addressing healthcare quality issues. According to the NHQR, “wound
infections are a common occurrence following surgery, but hospitals can reduce the risk of these health care–associated infections by
making sure patients receive an appropriate antibiotic within an hour before their procedures” (AHRQ, 2009, p. 110). The Centers for
Medicare and Medicaid Services (CMS) already has the capacity to track Medicare patients who receive this prophylactic treatment
and the rate of postsurgical wound infections for those patients who do and do not receive the treatment. Imagine being able to track
this issue for all surgical patients and developing evidence-based care plans to ensure that all patients within the infrastructure receive
the same quality of care. This is just one of many examples in which the end result of EHR adoption is better patient outcomes.
EHR technology also will make it easier for all providers involved in a patient’s care to readily access that patient’s complete and
current healthcare record, thereby allowing providers to make well-informed, efficient, and effective decisions about a patient’s care at
the time those decisions need to be made. This is of tremendous benefit to the patient and promotes a higher level of patient-centered
care. It also allows effective coordination of care between and among all providers involved in the patient’s care, including doctors,
nurses, therapists, nutritionists, hospitals, nursing homes, rehabilitation facilities, home health agencies, laboratories, and other
diagnostic centers, thereby assuring the continuum of patient care.
Such an integrated system would have clear benefits for patients and providers alike. For example, imagine how much easier it
would be for a patient with a rare form of cancer to obtain a second oncologist’s opinion before beginning a course of treatment. The
patient’s complete record, including the results of numerous diagnostic tests conducted at multiple sites, such as blood tests, biopsies,
radiographs, and scans, would be readily available to the second oncologist. Imagine how much easier it would be for a patient with
end-stage renal disease, who is receiving outpatient hemodialysis several times a week, to receive appropriate treatment if he or she is
suddenly hospitalized or would like to take a vacation out of state. Imagine how much easier it would be for nurses to complete a
medication reconciliation for a newly admitted patient. The possibilities are endless, and the savings realized from enhancing quality,
avoiding duplication of services, and streamlining delivery of patient care are obvious.
Reducing healthcare errors has been another ongoing challenge in the United States. Healthcare providers strive to meet the
standard of care and avoid harm to patients. Patients have a right to receive appropriate care, but that does not always happen. Ten
years ago, the Institute of Medicine’s Committee on the Quality of Health Care in America undertook a comprehensive literature
review and summarized the results of more than 40 studies about healthcare errors in its seminal report, To Err Is Human: Building a
Safer Health System (Institute of Medicine, 2000). That report concluded that approximately 44,000–98,000 people in the United
States die each year as a result of healthcare errors. Many thousands more who do not die are seriously injured from such errors. In
addition to the human pain and suffering associated with healthcare errors, the monetary costs of these errors are substantial. Although
some progress in reducing healthcare errors has been made since the release of To Err Is Human, substantial work remains to be done.
It is anticipated that a nationwide HIT infrastructure will contribute to a reduction in healthcare errors by providing mechanisms to
assist with the prevention of errors and to provide timely warnings of the possibility of a repetitive error that may affect many patients.
Containing and reducing healthcare costs in the United States, where more than $2 trillion is spent on health care each year
(Keehan, Sisko, & Truffler, 2008), is another daunting challenge. Using EHR technology and a nationwide HIT infrastructure to
improve quality and reduce errors within the healthcare delivery system is one way to address this challenge. Imagine the billions of
dollars that could be saved just by reducing the estimated 1.7 million cases of healthcare-associated infections contracted by patients in
U.S. hospitals each year (AHRQ, 2009, p. 108).
Promoting prevention, early detection, and management of chronic diseases is another purpose of the HITECH Act. The delivery
of health care in the United States traditionally has been based on a disease model rather than a wellness model. Having an EHR for
each individual could help with the necessary transition as providers and their patients become more aware of the variables that
positively or negatively impact health. The ability to identify appropriate choices to promote wellness and either prevent illness and
injury or detect and manage chronic diseases sooner will be enhanced.
Chronic diseases are of major concern to this country, not only because of the impact they have on individuals, but also because of
the tremendous cost associated with providing treatment for patients with these conditions. Adult-onset diabetes, for example, has
reached epidemic proportions. A national HIT infrastructure will help providers better identify those patients who are at risk for
developing this disease and provide treatment strategies to avoid it. For those patients who develop type 2 diabetes, their providers will
be able to diagnose the condition much sooner and manage it more effectively because of the vast resources that a national HIT
infrastructure can provide.
Improving public health is another purpose of the HITECH Act. The recent H1N1 flu pandemic is illustrative of how a national
HIT infrastructure can protect public health by fostering early detection and rapid response to infectious diseases, bioterrorism, and
other situations that could have a widespread impact on the health status of many individuals and groups.
The impact that a national HIT infrastructure will have on clinical research is self-evident. Once the infrastructure becomes
operational, the amount of data that will become readily available for clinical research will increase exponentially compared to what is
available today. The ability of researchers to conduct studies and provide clinicians with the most current evidence-based practice will
be of tremendous benefit to patients everywhere.
Reducing health disparities is another purpose of the HITECH Act. According to the AHRQ (2010), “Health care disparities are
differences or gaps in the care experienced by one population compared with another population” (p. 1). Detailed information about
healthcare disparities can be found at the website for the Office of Minority Health and Health Disparities at www.cdc.gov/omhd. The
AHRQ routinely examines the issue of disparities in health care and reports its findings to the public. Its current report, the National
Healthcare Disparities Report of 2012, confirms that some Americans continue to receive inferior care because of such factors as race,
ethnicity, and socioeconomic status (AHRQ, 2013). This report found disparities in the following areas:
Across all dimensions of healthcare quality: effectiveness, patient safety, timeliness, and patient centeredness.
Across all dimensions of access to care: facilitators and barriers to care and health care utilization.
Across many levels and types of care: preventive care, treatment of acute conditions, and management of chronic diseases.
Across many clinical conditions: cancer, diabetes, end-stage renal disease, heart disease, HIV disease, mental health and
substance abuse, and respiratory diseases.
Across many care settings: primary care, home health care, hospice care, emergency department, hospitals, and nursing homes.
Within many subpopulations: women, children, older adults, residents of rural areas, and individuals with disabilities and other
special healthcare needs (AHRQ, 2013, pp. H1–H4).
All patients, regardless of race, ethnicity, or socioeconomic status, should receive care that is effective, safe, and timely. When the
national HIT infrastructure contemplated by the HITECH Act is fully implemented, such disparities are bound to decrease. The ability
to monitor for disparities and promote the delivery of appropriate care to all patients will be enhanced. Clinicians will be prompted to
base their treatments on appropriate factors and avoid biased care.
Perhaps the most important task facing the national coordinator during the development and implementation of a nationwide HIT
infrastructure is ensuring the security of the patient health information within that system. The ability to secure and protect confidential
patient information has always been of paramount importance to clinicians, who view this consideration as an ethical and legal
obligation of practice. Patients value their privacy and they have a right to expect that their confidential health information will be
properly safeguarded. Nurses have been complying with the regulatory requirements of HIPAA for years, and the HITECH Act has
enhanced the security and privacy protections each patient has a right to expect under HIPAA (Box 9-1). The specific changes are
discussed in greater detail later in this chapter.
BOX 9-1 OVERVIEW OF THE HEALTH INSURANCE PORTABILITY AND ACCOUNTABILITY ACT OF 1996
Dee McGonigle, Kathleen Mastrian, and Nedra Farcus
HIPAA was signed into law by President Bill Clinton in 1996. Hellerstein (1999) summarized the intent of the act as follows: to curtail healthcare
fraud and abuse, enforce standards for health information, guarantee the security and privacy of health information, and ensure health insurance
portability for employed persons. Consequences were put into place for institutions and individuals who violate the requirements of this act. For this
text, we concentrate on the health information security and privacy aspects of HIPAA, which are outlined as follows:
The privacy provisions of the federal law, the Health Insurance Portability and Accountability Act of 1996 (HIPAA), apply to health
information created or maintained by healthcare providers who engage in certain electronic transactions, health plans, and healthcare
clearinghouses. The Department of Health and Human Services (HHS) has issued the regulation, “Standards for Privacy of Individually
Identifiable Health Information,” applicable to entities covered by HIPAA. The Office for Civil Rights (OCR) is the Departmental
component responsible for implementing and enforcing the privacy regulation. (See the Statement of Delegation of Authority to the Office
for Civil Rights, as published in the Federal Register on December 28, 2000. (U.S. Department of Health and Human Services, 2006, para. 1)
The need and means to guarantee the security and privacy of health information have been the focus of numerous debates. Comprehensive
standards for the implementation of this portion of the act eventually were finalized, but the process to adopt final standards took years. In August
1998, the U.S. Department of Health and Human Services released a set of proposed rules addressing health information management. Proposed
rules specific to health information privacy and security were released in November 1999. The purpose of the proposed rules was to balance patients’
rights to privacy and providers’ needs for access to information (Hellerstein, 2000).
One of the biggest stumbling blocks to implementation of comprehensive standards for privacy was the associated cost. The administrative
simplification portion of HIPAA calling for standardized forms for claims, medical records, laboratory reports, insurance forms, and so forth was
expected to save as much as $250 billion after the initial conversion costs. HHS projected that compliance with the proposed security and privacy
rules would cost $6.7 billion. Not everyone agreed with the HHS estimate, however; a study conducted by Blue Cross/Blue Shield and reported by
Egger (2000) suggested that the costs would be closer to $43 billion over 5 years. An overview of the proposed standards helps to illustrate why
implementation was estimated to be so costly.
Hellerstein (2000) summarized the proposed privacy rules. The rules do the following:
Define protected health information as “information relating to one’s physical or mental health, the provision of one’s health care, or the payment
for that health care, that has been maintained or transmitted electronically and that can be reasonably identified with the individual it applies to”
(Hellerstein, 2000, p. 2).
Propose that authorization by patients for release of information is not necessary when the release of information is directly related to treatment
and payment for treatment. Specific patient authorization is not required for research, medical or police emergencies, legal proceedings, and
collection of data for public health concerns. All other releases of health information require a specific form for each release and only information
pertinent to the issue at hand is allowed to be released. All releases of information must be formally documented and accessible to the patient on
request.
Establish patient ownership of the healthcare record and allow for patient-initiated corrections and amendments.
Mandate administrative requirements for the protection of healthcare information. All healthcare organizations are required to have a privacy
official and an office to receive privacy violation complaints. A specific training program for employees that includes a certification of completion
and a signed statement by all employees that they will uphold privacy procedures must be developed and implemented. All employees must re-
sign the agreement to uphold privacy every 3 years. Sanctions for violations of policy must be clearly defined and applied.
http://www.cdc.gov/omhd
Mandate that all outside entities that conduct business with healthcare organizations (e.g., attorneys, consultants, auditors) must meet the same
standards as the organization for information protection and security.
Allow protected health information to be released without authorization for research studies. Patients may not access their information in blinded
research studies because this access may affect the reliability of the study outcomes.
Propose that protected health information may be deidentified before release in such a manner that the identity of the patient is protected. The
healthcare organization may code the deidentification so that the information can be reidentified once it has been returned.
Apply only to health information maintained or transmitted by electronic means.
As concerns mounted and deadlines loomed, the healthcare arena prepared to comply with the requirements of the law. The administrative
simplification portion of this law is intended to decrease the financial and administrative burdens by standardizing the electronic transmission of
certain administrative and financial transactions. This section also addresses the security and privacy of healthcare data and information for the
covered entities of healthcare providers who transmit any health information in electronic form in connection with a covered transaction, health
plans, and healthcare clearinghouses. For more information, visit http://aspe.hhs.gov/ (U.S. Department of Health and Human Services, 2007).
The privacy requirements, which went into effect on April 14, 2003, limit the release of protected health information without the patient’s
knowledge and consent. Covered entities must comply with the requirements. Notably, they must dedicate a privacy officer, adopt and implement
privacy procedures, educate their personnel, and secure their electronic patient records. Most individuals are familiar with the need to notify patients
of their privacy rights, having signed forms on interacting with healthcare providers.
According to the HHS (2002), the privacy rule provides certain rights to patients: the right to request restrictions to access of the health record;
the right to request an alternative method of communication with a provider; the right to receive a paper copy of the notice of privacy practices; the
right to file a complaint if the patient believes his or her privacy rights were violated; the right to inspect and copy one’s health record; the right to
request an amendment to the health record; and the right to see an account of disclosures of one’s health record. This places the burden of
maintaining privacy and accuracy on the healthcare system, rather than the patient.
On October 16, 2003, the electronic transaction and code set standards became effective. They do not require electronic transmission, but rather
mandate that if transactions are conducted electronically, they must comply with the required federal standards for electronically filed healthcare
claims. “The Secretary has made the Centers for Medicare & Medicaid Services (CMS) responsible for enforcing the electronic transactions and code
sets provisions of the law” (“Guidance on Compliance with HIPAA Transactions and Code Sets,” 2003, para. 3).
The security requirements went into effect on April 21, 2005, and require the covered entities to put safeguards that protect the confidentiality,
integrity, and availability of protected health information when stored and transmitted electronically into place. According to Savage (2006), “After
HIPAA’s security requirements went into effect, many organizations in the healthcare industry continue to improve their security” (para. 5). Some
organizations have needed to shell out enormous amounts of resource expenditures involving people, time, and money. Savage states that “While the
HIPAA rules have driven security improvements, their lack of specifics and a dearth of enforcement leaves much room for interpretation, making
compliance hard to gauge” (para. 5).
Because it is believed that the pursuit of a perfect security system is a wild goose chase, this journey should be reflective of the needs of each
organization and its unique requirements in relation to its obligations to the patients it serves while addressing the demands of HIPAA. The
safeguards that are addressed are administrative, physical, and technical. The administrative safeguards refer to the documented formal policies and
procedures that are used to manage and execute the security measures. They govern the protection of healthcare data and information and the conduct
of the personnel. The physical safeguards refer to the policies and procedures that must be in place to limit physical access to electronic information
systems. Technical safeguards are the policies and procedures used to control access to healthcare data and information. Safeguards need to be in
place to control access whether the data and information are at rest, residing on a machine or storage medium, being processed, or in transmission,
such as being backed up to storage or disseminated across a network.
How a National HIT Infrastructure Is Being Developed
Developing a national HIT infrastructure is an enormous and extremely complex undertaking that requires extensive financial,
technologic, and human resources. The HITECH Act established the ONC, as noted earlier, and HHS appointed a national coordinator,
who is responsible for the development of the infrastructure. The HITECH Act also established two committees within the ONC: the
HIT Policy Committee and the HIT Standards Committee.
The Policy Committee is responsible for making recommendations to the coordinator about how to implement the requirements of
the HITECH Act, such as the technologies to use in the infrastructure. The Policy Committee has a total of 20 members, one of whom
must be a member from a labor organization and two of whom must be healthcare providers. At least one of the healthcare providers
must be a physician. There is no specific requirement that a nurse be on the Policy Committee. A complete list of the Policy
Committee members is available at www.healthit.hhs.gov.
The Standards Committee is responsible for recommending standards by which health information is to be electronically
exchanged. The HITECH Act does not designate the number of members to be on the committee; however, its members include
healthcare providers, ancillary healthcare workers, consumers of health care, and others. Again, there is no specific requirement that a
nurse be on the Standards Committee, and a complete list of the Standards Committee members is available at www.healthit.hhs.gov.
The HITECH Act also made provisions to include meaningful public input in the development of a national HIT infrastructure.
Both the Policy Committee and the Standards Committee hold public meetings, and anyone interested in this process can participate. A
schedule of meetings, committee agendas, and the transcripts of past meeting are posted at www.healthit.hhs.gov.
The national coordinator has several duties. He or she decides whether to endorse the recommendations of the Policy and
Standards Committees and acts as a liaison among the committees and various federal agencies involved in the process of developing a
national HIT infrastructure. He or she consults with these other agencies, including the National Institute of Standards and
Technology, and along with those agencies updates the Federal HIT Strategic Plan (U.S. Department of Commerce, 2011). The
Federal HIT Strategic Plan was published in June 2008, before enactment of the HITECH Act, and can be accessed at
http://www.hrsa.gov/healthit/toolbox/ruralhealthittoolbox/gettingstarted/strategicplan.html.
The HITECH Act also provides significant monetary incentives for providers who engage in meaningful use of health information
technology. “Meaningful use” is defined as “using electronic health records (EHRs) in a meaningful manner, which includes, but is not
limited to electronically capturing health information in a coded format, using that information to track key clinical conditions,
communicating that information to help coordinate care, and initiating the reporting of clinical quality measures and public health
information” (CMS, 2010, para. 3).
Monetary incentives are available to clinicians and facilities that implement EHR systems that meet the specific standards.
Providers that fail to adopt such systems within a specified time frame may be subject to significant governmental penalties.
How the HITECH Act Changed HIPAA
HIPAA Privacy and Security Rules
Nurses have been complying with HIPAA for years. HIPAA was enacted by the federal government for several purposes, including
better portability of health insurance as a worker moved from one job to another; deterrence of fraud, abuse, and waste within the
healthcare delivery system; and simplification of the administrative functions associated with the delivery of health care, such as
reimbursement claims sent to Medicare and Medicaid. Simplification of administrative functions entailed the adoption of electronic
transactions that included sensitive healthcare information. To protect the privacy and security of health information, two sets of
federal regulations were implemented. The Privacy Rule became effective in 2003, and the Security Rule became effective in 2005.
Many practitioners that refer to HIPAA are not referring to the comprehensive federal statute enacted in 1996, but rather to the Privacy
Rule and the Security Rule—that is, the federal regulations that were adopted years after HIPAA became law.
Under the Privacy Rule, patients have a right to expect privacy protections that limit the use and disclosure of their health
information. Under the Security Rule, providers are obligated to safeguard their patients’ health information from improper use or
disclosure, maintain the integrity of the information, and ensure its availability. Both rules apply to protected health information
(PHI), defined as any physical or mental health information created, received, or stored by a “covered entity” that can be used to
identify an individual patient, regardless of the form of the health information (i.e., it can be electronic, handwritten, or verbal) (V|lex,
2011). Covered entities include hospitals and other healthcare providers that transmit any health information electronically, as well as
health insurance companies and healthcare clearinghouses (V|lex, 2011).
Clinicians have become very knowledgeable about the requirements of the Privacy and Security Rules. They are familiar with their
obligations to protect patient information and the rights afforded to their patients under these regulations. Patients are entitled to a
notice of privacy practices from their healthcare provider. Inpatients are entitled to opt out of the facility’s directory, thereby protecting
disclosure of information that they are even a patient in the facility. Under certain circumstances, patients must authorize disclosure of
their PHI before it can be released by the provider. Patients can request and obtain access to their own healthcare records and may
request that corrections and additions be made to their records. Providers must consider a patient’s request to amend a healthcare
record, but they are not required to make such an amendment if the request is unwarranted. Unauthorized access or use or any loss of
healthcare information must be disclosed to any patient affected by the breach. Patients may request an accounting of anyone who
accessed their healthcare information, and the provider is required to provide that information in a timely manner. Finally, patients
have a right to complain if they perceive that the privacy or security of their healthcare information has been compromised in some
way. Such complaints can be made directly to the provider or to the Office of Civil Rights (OCR).
The OCR, which is part of HHS, is responsible for enforcing HIPAA. It provides significant information and guidance to clinicians
who must comply with the Privacy and Security Rules. It has been tracking complaints and investigating violations since 2003.
Guidance and information about the complaint process and the violations that the OCR has handled are available on its website at
http://www.hhs.gov/ocr/privacy/hipaa/modelnotices.html. As an example, one such violation involved a nurse practitioner who had
privileges within a healthcare system. She accessed her ex-husband’s medical records without his authorization by using the system-
wide EHRs. A complaint was filed and the OCR investigated the matter. The OCR resolved the complaint with the healthcare system.
As part of this resolution, the healthcare system curtailed the nurse practitioner’s access to its EHRs and it required her to undergo
remedial training. In addition, it reported the nurse practitioner to her professional board (U.S. Department of Health and Human
Services, Office of Civil Rights, n.d.)
Many businesses are moving to enact a “bring your own device” (BYOD) policy for employees. This policy, which helps to
streamline the lives of employees by maintaining personal and business information on one device, can also result in cost savings for
the organization overall. BYOD is an issue, however, when dealing with PHI. Healthcare organizations typically do not encourage use
of personal devices for professional matters, and in many instances they actually have policies in place forbidding employees from
using personal devices in the workplace. According to HIT Consultant (2013), approximately 50% of healthcare organizations report
that personal mobile devices can be used to access the Internet within their facilities but these devices are not given access to the
organization’s network. Typically, only devices that are issued by the organization, secured, and routinely audited are able to access to
the network. Nurses must exercise caution when bringing their personal devices into the healthcare organization to ensure that they are
not violating any specifics of the BYOD policy.
Compliance with the Privacy and Security Rules is mandatory for all covered entities, and the HITECH Act extends compliance
with these requirements directly to other entities that are business associates of a covered entity. Requirements include designation of
privacy and information security officials to protect health information and appropriate handling of any complaints. Sanctions must be
imposed if a violation of HIPAA occurs. The Privacy and Security Rules also mandate that certain physical and technical safeguards
be implemented for PHI, and they require entities to conduct periodic training of all staff to ensure compliance with these safeguards.
Most entities adhere to industry standards and provide their personnel with yearly training. In addition, entities are to conduct regular
audits to ensure compliance, and any breaches in the privacy or security of PHI must be remedied immediately. It is important to avoid
a security incident, defined as “the attempted or successful unauthorized access, use, disclosure, modification, or destruction of
information or interference with system operations in an information system” (U.S. Department of Human Services, CMS, 2008, p. 1).
Such incidents trigger certain notification requirements.
The HITECH Act Enhanced HIPAA Protections
The HITECH Act has had a significant impact on HIPAA’s Privacy and Security Rules in the following ways:
HHS is to provide annual guidance about how to secure health information.
Notification requirements in the event of a breach in the security of health information have been enhanced.
HIPAA requirements now apply directly to any business associates of a covered entity.
The rules that pertain to providing an accounting to patients who want to know who accessed their health information have
changed.
Enforcement of HIPAA has been strengthened.
These measures are being implemented to provide further assurance that health information will be protected as the country
transitions to a nationwide HIT infrastructure (Box 9-2).
BOX 9-2 OTHER ORGANIZATIONS ASSISTING HIPAA
Dee McGonigle, Kathleen Mastrian, and Nedra Farcus
Several other organizations have been involved in HIPAA implementation. The American National Standards Institute (ANSI) X12N and Health
Level 7 (HL7) standards organizations worked together to develop an electronic standard for claims attachments to recommend to HHS (Spencer &
Bushman, 2006, para. 2). The American National Standards Institute (ANSI, n.d.) was founded in 1918 and has served as the coordinator of the U.S.
voluntary standards and conformity assessment system (para. 1). ANSI provides a forum where the private and public sectors can cooperatively work
together toward the development of voluntary national consensus standards and the related compliance programs (para. 2). HL7 (n.d.) is one of
several American National Standards Institute–accredited standards-developing organizations (SDOs) operating in the healthcare arena (para. 1). It
states that its mission is to provide standards for interoperability that improve care delivery, optimize workflow, reduce ambiguity, and enhance
knowledge transfer among all stakeholders, including healthcare providers, government agencies, the vendor community, fellow SDOs, and patients
(para. 5).
HL7 was initially associated with HIPAA in 1996 through the creation of a claims attachments special interest group charged with standardizing
the supplemental information needed to support healthcare insurance and other e-commerce transactions. The initial deliverable of this group was six
claim attachments. This special interest group is currently known as the Attachment Special Interest Group. As the attachment projects continue, they
are slated to include skilled nursing facilities, home health care, preauthorization, and referrals.
The “Level Seven” in HL7’s name refers to the highest level of the International Standards Organization’s (ISO) communications model for
Open Systems Interconnection (OSI) application level. The application level addresses definition of the data to be exchanged, the timing of the
interchange, and the communication of certain errors to the application. The seventh level supports such functions as security checks, participant
identification, availability checks, exchange mechanism negotiations and, most importantly, data exchange structuring. (HL7, n.d., para. 5)
The OSI was an attempt to standardize networking by the ISO. HL7 addresses the distinct requirements of the systems in use in hospitals and
other facilities, is more concerned with application than the other levels, and considers user authentication and privacy (Webopedia, 2008). The
lower levels of OSI address hardware, software, and data reformatting.
HL7’s mission is supported through two separate groups, the Extensible Markup Language (XML) special interest group and the structured
documents technical committee. The XML special interest group makes recommendations on use of XML standards for all of HL7’s platform- and
vendor-independent healthcare specifications (HL7, n.d., para. 21). XML began as a simplified subset of the standard generalized markup language;
its major purpose is to facilitate the exchange of structured data across different information systems, especially via the Internet. It is considered an
extensible language because it permits users to define their own elements, thereby supporting customization to enable purpose-specific development.
The structured documents technical committee supports the HL7 mission through development of structured document standards for health care
(para. 21). HL7 also organizes, maintains, and sustains a repository for the vocabulary terms used in its messages to provide a shared, well-defined,
and unambiguous knowledge base of the meaning of the data transferred.
ISO (2008a) is a network of the national standards institutes of 157 countries. It includes one member per country, and a central secretariat in
Geneva, Switzerland, coordinates the system (para. 1). ISO is a nongovernmental organization; its members are not delegations of national
governments (unlike the case in the United Nations system). Nevertheless, ISO occupies a special position between the public and private sectors. On
the one hand, many of its member institutes are part of the governmental structure of their countries or are mandated by their government. On the
other hand, other members have their roots uniquely in the private sector, having been set up by national partnerships of industry associations (ISO,
2008a, para. 2).
This placement enables ISO to become a bridging organization where members can reach agreement on solutions that meet both the requirements
of business and the broader needs of society, consumers, and users. These international agreements become standards that use the prefix ISO
followed by the number of the standard. An example is the health informatics, health cards, numbering system, and registration procedure for issuer
identifiers, ISO 20302:2006; it is designed to confirm, via a numbering system and registration procedure, the identities of both the healthcare
application provider and the health card holder so that information may be exchanged by using cards issued for healthcare service (ISO, 2008b, para.
12). ISO provides standards for interoperability that improve care delivery, optimize workflow, reduce ambiguity, and enhance knowledge transfer
among all of its stakeholders, including healthcare providers, government agencies, the vendor community, fellow SDOs, and patients. The standards
are used on a voluntary basis because ISO has no power to force their enactment.
All of the organizations described here have guidelines, standards, and rules to help healthcare entities collect, store, manipulate, dispose of, and
exchange secure PHI. Many SDOs work to help develop standards. HIPAA guarantees the security and privacy of health information and curtails
healthcare fraud and abuse while enforcing standards for health information.
UNITED STATES AND BEYOND
Health care was not the only focus of U.S. legislative acts. One often sees “GLBA” and “SOX” when searching for information on HIPAA. The
Gramm-Leach-Bliley Act (GLBA) is federal legislation in the United States to control how financial institutions handle the private information they
collect from individuals. The Sarbanes-Oxley (SOX) Act is legislation put in place to protect shareholders and the public from deceptive accounting
practices in organizations.
Privacy and data regulations are also being established around the world, such as the Data Protection Act 1998 in the United Kingdom (Ministry
of Justice, 2008); the Dutch Data Protection Authority (2007), which released privacy legislation guidelines on publishing personal data on the
Internet; and Finland’s Personal Data File Act 1988 and Personal Data Act 1999 (Data Protection Board, n.d.). New Zealand’s Health Information
Privacy Code 1994 had amendment number six come into effect in November 2007; this amendment defined entities such as the ethics committee;
hospital, health, or disability services; health professional body; registered health professional; and health practitioner (Privacy Commissioner, 2007).
Argentina’s Privacy and Data Protection (2007) states that it is the first Latin American country to be awarded the status of “adequate country” from
the point of view of European Data Protection authorities—a breakthrough that is expected to encourage other countries in the region to work toward
improving data protection rights for individuals (Privacy and Data Protection, 2007, para. 7). In Canada, the Personal Information Protection and
Electronic Documents Act received royal assent in 2000 (Office of the Privacy Commissioner of Canada, 2004). Safe Harbor deals with the transfer
of personal data from the European Union to the United States; the regulations in Article 25 and 26 of the European Data Protection Directive serve
as the basis for governing this transfer. According to these regulations, transferring data to third countries is in principle possible only if these
countries guarantee an adequate level of protection as required by the directive (Federal Commissioner for Data Protection and Freedom of
Information, n.d., para. 1). It is quite evident that privacy and security have become global concerns.
Avoiding security incidents has become a paramount concern for healthcare organizations and providers. Providers must protect
their information and prevent unauthorized persons from accessing, using, disclosing, changing, or destroying a patient’s health
information, or otherwise interfering with the operations of a health information system, such as an EHR. To facilitate a provider’s
ability to do this, the HITECH Act requires HHS to provide annual guidance to secure health information. PHI can be secured or
unsecured. PHI is considered unsecured if the provider does not follow the guidance provided by HHS for implementing technologies
and methodologies that make PHI “unusable, unreadable, or indecipherable to unauthorized individuals” (U.S. Department of Health
and Human Services, 2009). PHI can be secured through encryption, shredding and other forms of complete destruction, or electronic
media sanitation.
The distinction between secured and unsecured PHI is important because providers that experience a breach in the privacy or
security of their PHI must adhere to certain notification requirements depending on the type of PHI affected by the breach. The
HITECH Act enhanced the breach notification requirements of HIPAA. If the PHI is unsecured, the provider must take certain steps to
notify those individuals who have been affected. Providers can avoid these onerous breach notification requirements if the PHI is
secured in accordance with the specifications of HHS.
A breach is considered discovered as soon as an employee other than the individual who committed the breach knows or should
have known of the breach, such as unauthorized access or even an unsuccessful attempt to access information. For example, if a nurse
knows that a colleague has accessed or attempted to access the record of a patient for whom the colleague is not providing care (e.g.,
the nurse practitioner who accessed her ex-husband’s EHR, as discussed previously), the nurse’s employer is deemed to have
discovered the breach as soon as the nurse learned of it. The discovery of a breach triggers the beginning of the time frame during
which the provider must fulfill the notification requirements. A provider must fulfill these requirements within a reasonable period of
time; under no circumstances may a provider take more than 60 days from discovery of the breach. It is easy to understand why
providers require their employees to report knowledge of such breaches immediately to the privacy or information security officer. A
provider’s failure to adhere to the breach notification requirements could result in OCR sanctions, including monetary penalties.
Whenever a breach involves unsecured PHI, covered entities are responsible for alerting each individual affect by mail, or by e-
mail if preferred by the individual. If there is insufficient contact information for 10 or more patients, the provider is required to place
conspicuous postings on the home page of its website or in major print or broadcast media (without identifying patients). A toll-free
telephone number must be provided so that affected individuals can call for information about the breach. For breaches involving
unsecured PHI of more than 500 individuals, a prominent media outlet must also be notified. Notice must be given to HHS as well,
and HHS will post the information on its public website (U.S. Department of Health and Human Services, 2009). It is easy to see why
providers would want to avoid these requirements by making sure their PHI is secured. Having to post such notices undermines the
trust that exists between healthcare providers and the patients and communities they serve.
The HITECH Act has improved the privacy and security of patient health information by applying the requirements of HIPAA
directly to the business associates of covered entities. In the past, it was up to the covered entity to enter into contracts with its business
associates to ensure compliance with HIPAA. Now business associates are responsible for their own compliance. An example of such
a business associate is a HIT company hired by a hospital to implement or upgrade an EHR system. The technology company has
access to the hospital’s EHR system and must comply with the HIPAA Privacy and Security Rules, just as covered entities must
comply with these rules. This includes being subject to enforcement by the OCR for any violations.
Existing accounting rules are enhanced under the HITECH Act, giving patients the right to access their EHR and receive an
accounting of all disclosures. Before the HITECH Act, HIPAA regulations provided an exception to the accounting requirements.
Providers and other covered entities were not required to include in the accounting any disclosures that were made to facilitate the
treatment of patients, the payment for services, or the operations of the entity—a provision commonly known as the “TPO
exception.” This exception ended in January 2011 for providers that recently implemented new EHR systems. For those providers with
EHR systems that were implemented before the HITECH Act, the TPO exception ends in January 2014. It is easy to understand why
this exception is ending. As all providers implement comprehensive EHR systems, it will be very easy to generate an electronic record
with an accounting of anyone who accessed a patient’s record.
Finally, the HITECH Act strengthens the enforcement of HIPAA. HHS can conduct audits, which will be even easier to
accomplish once a nationwide HIT infrastructure is in place. In addition, stiffer civil monetary penalties (CMP) for violations of
HIPAA became effective as soon as the HITECH Act became law in February 2009. CMPs are divided into three tiers. A Tier 1 CMP,
in which the covered entity had no reason to know of a violation, is $100 per incident, up to a cap of $25,000 per year. A Tier 2 CMP,
in which the covered entity had reasonable cause to know of a violation, is $1,000 per incident, up to a cap of $100,000 per year. A
Tier 3 CMP, in which the covered entity engaged in willful neglect that resulted in a breach, is $10,000 per incident, up to a cap of
$250,000 per year. In addition, the HITECH Act gives authority to impose an additional CMP of $50,000 to $1.5 million if the covered
entity does not properly correct a violation. Criminal penalties also can be imposed when warranted. It is imperative that providers
avoid these penalties.
Before enactment of the HITECH Act, the federal government alone enforced HIPAA. Now, state attorneys general can play a
significant role in the enforcement and prosecution of HIPAA violations. Once the HITECH Act became law, state attorneys general
were authorized to pursue civil claims for HIPAA violations and collect up to $25,000 plus attorneys’ fees. As of 2012, individuals
who are damaged by such violations became eligible to share in any monetary awards obtained by these state officials.
Implications for Nursing Practice
Being Involved and Staying Informed
The development and implementation of a nationwide EHR system holds great promise for nursing practice and nursing informatics.
The profession of nursing will benefit from the many enhancements such an infrastructure has to offer, including the ability to improve
the delivery of nursing care and the quality of that care, the ability to make more efficient and timely nursing care decisions for
patients, the ability to avoid errors that may harm patients, and the ability to promote health and wellness for the patients whom nurses
serve. On a broader scale, nurse researchers will have the ability to more readily access data that can be used to continue to foster
evidence-based practice. The possibilities seem endless. For those who devote their professional careers to nursing informatics or plan
to do so, the opportunities abound. Much work remains to be done as this country transitions to a nationwide HIT infrastructure,
however, and there are monetary incentives available from the government for adopting systems that comply with the meaningful use
requirement.
All nurses need to be engaged in this process, whether they treat patients, are managers within healthcare organizations, teach,
develop computer programs, or help create institutional or governmental policies. Nurses, as the end users of developing technologies,
cannot afford to be left behind in these exciting times. Their voices must be heard, whether it is within the facility where they work as
changes to the EHR system are contemplated, or whether it is in the public policy arena. How often are nurses the last to know that a
new EHR system has been adopted by their hospital? How many times have nurses been trained to use a system that would have
benefited from their input before it was implemented or even purchased? Nurses often are not invited to the table when entities make
decisions about informatics, so they should not be afraid to ask to be included, whether it is to be heard within the workplace or within
the governmental agencies that are overseeing the changes that are taking place.
Even nurses who do not get involved in this process need to stay current with the rapid changes that are taking place. Information
about federal initiatives is available from the ONC and the OCR. Both offices are housed within HHS and are excellent resources for
additional information about the HITECH Act and HIPAA. Regulations to implement the HITECH Act and enhance the HIPAA
protections required by it are being proposed and adopted at a rapid pace. The ONC can be accessed at
http://www.ahrq.gov/healthcare-information/topics/topic-hit.html. The OCR can be accessed at
http://www.hhs.gov/ocr/privacy/hipaa/modelnotices.html. State resources also are available.
Protecting Yourself
Nurses who strive to protect the privacy and security of patient information are protecting themselves from ethical lapses and
violations of law. The American Nurses Association’s Code of Ethics for Nurses with Interpretive Statements mandates that nurses
protect a patient’s rights to privacy and confidentiality.
Associated with the right to privacy, the nurse has a duty to maintain confidentiality of all patient information. Nurses who engage
with social media need to be especially cognizant of the potential for breaching the confidentiality of patient information. Box 9-3
provides more information related to nurses’ use of social media. The patient’s well-being could be jeopardized and the fundamental
trust between patient and nurse destroyed by unnecessary access to data or by the inappropriate disclosure of identifiable patient
information. The rights, well-being, and safety of the individual patient should be the primary factors in arriving at any professional
judgment concerning the disposition of confidential information received from or about the patient, whether oral, written, or electronic.
The standard of nursing practice and the nurse’s responsibility to provide quality care require that relevant data be shared with only
those members of the healthcare team who have a need to know that information. Only information pertinent to a patient’s treatment
and welfare should be disclosed, and only to those directly involved with the patient’s care. When using electronic communications,
special effort should be made to maintain data security (American Nurses Association, 2010, p. 6).
BOX 9-3 USE OF SOCIAL NETWORKS BY NURSES
Glenn Johnson and Jeff Swain
New opportunities to share information via social networks have grabbed the headlines. Since their inception in 2004, the growth in popularity of
social networking tools, such as Facebook (http://www.facebook.com) and Twitter (http://twitter.com), has been phenomenal. What makes these
sites so attractive? Web-based applications, such as Facebook, allow users to connect and share information in ways that were not previously
possible. Users develop online profiles that contain information they select to share with others. Using simple online utilities, users can easily
connect and share their profiles, communicating with friends over the Internet. Virtual groups of users with similar profiles may be created,
connecting users with others who have similar interests. Twitter, a micro-blogging platform, allows users to create interpersonal networks for
socializing, support, and information sharing. The power of such tools as Twitter lies in their being lightweight, their limiting of updates to 140 or
fewer characters, and their convenience—users can update their status from any device that has an Internet connection or text messaging capabilities.
The popularity of social and mobile networking applications is one indication of how new Web-based technologies are changing communication
preferences. The Web is no longer a destination place, but instead has become a vehicle of communication where individuals use application
software (“apps”), which are installed or downloaded, to connect with others. Individuals act as their own portal and can connect from anywhere with
their various communities. This makes it difficult to separate out various communities and social networks. Where once it was relatively easy to
separate work relationships from friends and family, networked communities tend to overlap, blurring the boundaries between them. The
phenomenon of overlapping networks means that the unintended audience is almost always greater than the intended one. A status update that may
be construed as harmless and funny to one’s friends could be taken an entirely different way by family or colleagues. This is not to say networked
communities are harmful or bad. Indeed, the benefits of such communities far exceed their negatives. However, the immediacy and the permanence
of the updates shared mean that the user must think about the impact beyond the intended audience in ways never before required (Johnson & Swain,
2011).
Nurses and other healthcare workers who use social media must be aware that the overlapping of networks may unintentionally create privacy
and confidentiality breaches. Even when patients are not identified by name, general sharing of information or venting about a difficult day may
constitute a privacy breach. The National Council of State Boards of Nursing (NCSBN, 2011) has collaborated with the ANA to develop specific
guidelines for the use of social media by nurses. See https://www.ncsbn.org/Social_Media to read a white paper discussing common
misconceptions about social media, consequences for breaching confidentiality using social media, guidelines for appropriate use of social media,
and case scenarios with discussion.
REFERENCES
Johnson, G., & Swain, J. (2011). Professional development and collaboration tools. In McGonigle, D. & Mastrian, K. eds. Nursing informatics and
the foundation of knowledge (2nd ed., pp. 185–195).
National Council of State Boards of Nursing. (2011). White paper: a nurses guide to the use of social media. www.ncsbn.org
The similarities between these ethical obligations and the legal requirements of HIPAA and other federal and state privacy and
confidentiality laws are readily apparent to nurses. By complying with their ethical code, nurses were complying with the Privacy and
Security Rules before they were required to do so. Since the adoption of the HIPAA Privacy and Security Rules, and now with the
passage of the HITECH Act, it is more important than ever for nurses to understand their obligations in this area and avoid the pitfalls
of violations.
In addition to the sanctions imposed by the OCR, violations can lead to disciplinary actions by employers and professional
licensing boards, as well as litigation. Such actions can have a serious negative impact on the nurse’s reputation and financial
wellbeing. If a nurse is terminated for invading a patient’s privacy or breaching the confidentiality of a patient’s information, some
state laws require reporting the information to prospective employers of the nurse; other laws require reporting to the State Board of
Nursing. State Boards of Nursing have the authority to publicly discipline a nurse who has engaged in professional misconduct by
invading a patient’s privacy, which includes inappropriately accessing a patient’s EHR, and breaching confidentiality of patient
information, such as allowing or tolerating unauthorized access to a patient’s EHR. These types of situations can also cause patients to
file complaints with the OCR and lawsuits against the offenders. Nurses must be ever mindful of their obligations to report a breach in
the privacy or security of PHI to their employers, even if it entails reporting a colleague.
Finally, some view the EHR as a convenient method for employers to monitor the performance of its nurses. Clearly, an EHR
system provides a wealth of information that can be, and often is required to be, monitored. Audits are required to make sure that no
breaches in the system’s security occur. Audits are not necessarily required to determine, for example, which nurses are failing to
complete the hospital’s documentation requirements in a timely fashion, which nurses are improperly altering (attempting to correct)
the record, or which nurses are dispensing more pain medication than the average. Nurses have been challenged by employers who
allege failure to document, improper or false documentation, and suspected diversion of narcotics. These types of situations are
unsettling and may be on the rise as more providers adopt or augment EHR systems. Thus it behooves every nurse who works with
such a system to obtain proper training and to know the policies and procedures that pertain to its use.
Summary
The HITECH Act and the HIPAA Privacy and Security Rules are intended to enhance the rights of individuals. These laws provide
patients with greater access and control over their PHI. They can control its uses, dissemination, and disclosures. Covered entities must
establish not only a required level of security for PHI, but also sanctions for employees who violate the organization’s privacy policies
and administrative processes for responding to patient requests regarding their information. Therefore, they must be able to track the
PHI, note access from the perspective of which information was accessed and by whom, and identify any disclosures. Finally, readers
should recognize that there is global awareness of the need for privacy protections for personal information or PHI. Over the next few
years, international efforts will accelerate, enhancing international data exchange.
THOUGHT-PROVOKING
QUESTIONS
1. Why is it important to establish patient ownership of the healthcare record?
2. What are the potential negative consequences of the right of amendment and correction of healthcare records by patients?
3. One of the largest problems with healthcare information security has always been inappropriate use by authorized users. How do HIPAA and the
HITECH Act help to curb this problem?
4. How do you envision Health Level 7, HIPAA, and the HITECH Act evolving in the next decade?
5. Imagine that you are the designated privacy officer in a healthcare institution. Which types of monitoring procedures would you develop?
6. If you were the privacy officer, what would you include in your sanctions for violations policy?
7. As privacy officer, how would you address the following:
a. Tracking each point of access of the patient’s database, including who entered the data.
b. Nurses in your hospital have an access code that gives them access to only their unit’s patients. A visitor accidently comes to the wrong unit
looking for a patient and asks the nurse to find out which unit the patient is on.
c. Encouraging nurses to report privacy and security breaches.
References
Agency for Healthcare Research and Quality (AHRQ). (2009). National healthcare quality report (NHQR) 2009: Crossing the quality chasm: A new
healthcare system for the 21st century. Washington, DC: National Academies Press.
Agency for Healthcare Research and Quality (AHRQ). (2013). 2012 national healthcare disparities report.
http://www.ahrq.gov/research/findings/nhqrdr/nhdr12/nhdr12_prov
American National Standards Institute (ANSI). (n.d.). ANSI: A historical overview. http://ansi.org/about_ansi/introduction/history.aspx?menuid=1
American Nurses Association. (2010). Code of ethics for nurses with interpretive statements.
http://www.nursingworld.org/MainMenuCategories/EthicsStandards/CodeofEthicsforNurses/Code-of-Ethics.aspx
Ashish, J. (2009). Use of electronic health records in U.S. hospitals. New England Journal of Medicine, 360(16), 1628–1638.
Centers for Medicare and Medicaid Services (CMS). (2010). Meaningful use. https://www.cms.gov/EHRIncentivePrograms/30_Meaningful_Use.asp
Data Protection Board. (n.d.). Legislation for the protection of privacy. http://www.tietosuoja.fi/27305.htm
Dutch Data Protection Authority (DPA). (2007, December). Dutch DPA publication of personal data on the Internet.
http://www.dutchdpa.nl/downloads_overig/en_20071108_richtsnoeren_internet ?refer=true&theme=purple
Egger, E. (2000). HIPAA offers hospitals the good, the bad, and the ugly. Health Care Strategic Management, 18(4), 1, 21–23.
Federal Commissioner for Data Protection and Freedom of Information. (n.d.). Safe harbor.
http://www.bfdi.bund.de/cln_007/nn_671558/EN/EuropeanInternationalAffaires/Artikel/SafeHarbor.html
Guidance on compliance with HIPAA transactions and code sets. (2003). http://www.cms.gov/Regulations-and-Guidance/HIPAA-Administrative-
Simplification/TransactionCodeSetsStands/index.html?redirect=/transactioncodesetsstands/02_transactionsandcodesetsregulations.asp
Health Level Seven (HL7). (n.d.). What is HL7? http://www.hl7.org/
Hellerstein, D. (1999). HIPAA’s impact on healthcare. Health Management Technology. Retrieved January 2008 from
http://findarticles.com/p/articles/mi_m0DUD/is_3_20/ai_54396227
Hellerstein, D. (2000). HIPAA and health information privacy rules: Almost there. Health Management Technology. Retrieved January 2008 from
http://findarticles.com/p/articles/mi_m0DUD/is_4_21/ai_61523494
HIT Consultant. (2013). 3 Do’s and don’ts of effective HIPAA compliance for BYOD & mHealth. http://www.hitconsultant.net/2013/06/11/3-dos-and-
donts-of-effective-hipaacompliance-for-byod-mhealth/
Institute of Medicine. (2000). To err is human: Building a safer health system. Washington, DC: National Academies Press.
International Standards Organization (ISO). (2008a). About ISO. http://www.iso.org/iso/about.htm
International Standards Organization (ISO). (2008b). Health informatics. http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?
csnumber=35376
Keehan, S., Sisko, A.Q., & Truffler, C. (2008). Health spending projections through 2017: The baby-boom generation is coming to Medicare. Health
Affairs, 27(2), 145–155.
Leyva, C., & Leyva, D. (2011). HITECH Act. http://www.hipaasurvivalguide.com/hitech-act-text.php
Ministry of Justice. (2008). Data sharing and protection. http://www.justice.gov.uk/information-access-rights/data-protection
Office of the Privacy Commissioner of Canada. (2004). Privacy legislation. http://www.privcom.gc.ca/legislation/02_06_07_e.asp
Privacy and Data Protection. (2007). Data protection in Argentina. http://www.protecciondedatos.com.ar
Privacy Commissioner. (2007). Health information privacy code. http://www.privacy.org.nz/health-information-privacy-code
Readthestimulus.org. (2009). Committee print, January 16, 2009. http://govinfo.sla.org/2009/02/08/readthestimulusorg/
Savage, M. (2006). Security news: Perfect HIPAA security impossible, experts say.
http://searchsecurity.techtarget.com/originalContent/0,289142,sid14_gci1268986,00.html
Spencer, J., & Bushman, M. (2006). HIPAAdvisory: The next HIPAA frontier: Claims attachments. Retrieved January 2008 from
http://www.hipaadvisory.com/action/tcs/nextfrontier.htm
U.S. Department of Commerce. (2011). National Institute of Standards and Technology (NIST). http://www.nist.gov/index.html
U.S. Department of Health and Human Services. (2002). Federal Register, Part V, Department of Health and Human Services: Standards for privacy of
individually identifiable health information; Final rule. http://www.hhs.gov/ocr/privacy/hipaa/administrative/privacyrule/privrulepd
U.S. Department of Health and Human Services. (2006). Medical privacy: National standards to protect the privacy of personal health information.
http://www.ihs.gov/hipaa/
U.S. Department of Health and Human Services. (2007). Administrative simplification in the health care industry. Retrieved January 2008 from
http://aspe.os.dhhs.gov/admnsimp
U.S. Department of Health and Human Services. (2009). Federal Register: 45 CFR Parts 160 and 164: Breach Notification for Unsecured Protected
Health Information; Interim.
U.S. Department of Health and Human Services, CMS. (2008). CMS information security incident handling and breach analysis/notification process.
Retrieved January 2008 from https://www.cms.gov/informationsecurity/downloads/incident_handling_procedure
U.S. Department of Health and Human Services, Office of Civil Rights. (n.d.). Health information privacy: All case examples.
http://www.hhs.gov/ocr/privacy/hipaa/enforcement/examples/allcases.html#case1
V|lex. (2011). 45 CFR 160.103—Definitions. http://cfr.vlex.com/vid/160-103-definitions-19933565
Webopedia. (2008). The 7 layers of the OSI model. http://www.webopedia.com/quick_ref/OSI_Layers.asp
Section III
Nursing Informatics Administrative Applications: Precare and Care
Support
Chapter 10 Systems Development Life Cycle: Nursing Informatics and Organizational Decision Making
Chapter 11 Administrative Information Systems
Chapter 12 The Human–Technology Interface
Chapter 13 Electronic Security
Chapter 14 Nursing Informatics: Improving Workflow and Meaningful Use
Nursing informatics (NI) and information technology (IT) have invaded nursing, and some nurses are happy with the capabilities
afforded by this specialty. Others, however, remain convinced that the changes wrought by IT are nothing more than a nuisance. In the
past, nursing administrators have found the implementation of technology tools to be an expensive venture with minimal rewards. This
disappointment is likely related to their lack of knowledge about NI, which caused nursing administrators to listen to vendors or other
colleagues; in essence, it was decision making based on limited and biased information. There were at least two reasons for the
experience of limited rewards. First, nurses were rarely included in the testing and implementation of products designed for nurses and
nursing tasks. Second, the new products they purchased had to interface with old, legacy systems that were not at all compatible or
seemed compatible until the glitches arose. These glitches caused frustration for clinicians and administrators alike. They purchased
tools that should have made the nurses happy, but instead all they did was grumble.
The good news is that approaches have changed as a result of the difficult lessons learned from the early forays into technology
tools. Nursing personnel are involved both at the agency level and at the vendor level, in the decision-making process and development
of new systems and products charged with enhancing the practice of nursing. Older legacy systems are being replaced with newer
systems that have more capacity to interface with other systems. Nurses and administrators have become more astute in the realm of
NI, but there is still a long way to go. The Systems Development Life Cycle: Nursing Informatics and Organizational Decision Making
chapter introduces the system development life cycle, which is used to make important and appropriate organizational decisions for
technology adoption.
Administrators need information systems that facilitate their administrative role, and they particularly need systems that provide
financial, risk management, quality assurance, human resources, payroll, patient registration, acuity, communication, and scheduling
functions. The administrator must be open to learning about all of the tools available. One of the most important tasks that an
administrator can oversee and engage in is data mining, or the extraction of data and information from sizeable data sets that have been
collected and warehoused. Data mining helps to identify patterns in aggregate data, gain insights, and ultimately discover and generate
knowledge applicable to nursing science. To take advantage of these benefits, nursing administrators must become astute
informaticists—knowledge workers who harness the information and knowledge at their fingertips to facilitate the practice of their
clinicians, improve patient care, and advance the science of nursing.
Clinical information systems (CIS) have traditionally been designed for use by one unit or department within an institution.
However, because clinicians working in other areas of the organization need access to this information, these data and information are
generally used by more than one area. The new initiatives arising with the development of the electronic health record place
institutions in the position of striving to manage their CIS through the electronic health record. Currently, there are many CISs,
including nursing, laboratory, pharmacy, monitoring, and order entry, plus additional ancillary systems to meet the individual
institutions’ needs. The Administrative Information Systems chapter provides an overview of administrative information systems and
helps the reader to understand the powerful data aggregation and data mining tools afforded by these systems.
The Human–Technology Interface chapter discusses the need to improve quality and safety outcomes significantly in the United
States. Through the use of IT, the designs for human–technology interfaces can be radically improved so that the technology better fits
both human and task requirements. A number of useful tools are currently available for the analysis, design, and evaluation phases of
development life cycles and should be used routinely by informatics professionals to ensure that technology better fits both task and
user requirements. In this chapter, the author stresses that the focus on interface improvement using these tools has dramatically
improved patient safety in a specific area of health care: anesthesiology. With increased attention from informatics professionals and
engineers, the same kinds of improvements should be possible in other areas. This human–technology interface is a crucial area if the
theories, architectures, and tools provided by the building block sciences are to be implemented.
Each organization must determine who can access and use its information systems and provide robust tools for securing
information in a networked environment. The Electronic Security chapter addresses the important safeguards for protecting
information. As new technologies designed to enhance patient care are adopted, barriers to implementation and resistance by
practitioners to change are frequently encountered. The Nursing Informatics: Improving Workflow and Meaningful Use chapter
provides insights into clinical workflow analysis and provides advice on improving efficiency and effectiveness to achieve meaningful
use of caring technologies.
Pause to reflect on the Foundation of Knowledge model (Figure III-1) and its relationship to both personal and organizational
knowledge management. Consider that organizational decision making must be driven by appropriate information and knowledge
developed in the organization and applied with wisdom. Equally important to adopting technology within an organization is the
consideration of the knowledge base and knowledge capabilities of the individuals within that organization. Administrators must use
the system development life cycle wisely and carefully consider organizational workflow as they adopt NI technology for meaningful
use.
The reader of this section is challenged to ask the following questions: (1) How can I apply the knowledge gained from my practice
setting to benefit my patients and enhance my practice; (2) How can I help my colleagues and patients understand and use the current
technology that is available; and (3) How can I use my wisdom to create the theories, tools, and knowledge of the future?
Figure III-1 Foundation of Knowledge Model
Source: Designed by Alicia Mastrian
Chapter 10
Systems Development Life cycle: nursing informatics and
organizational Decision Making
Dee McGonigle and Kathleen Mastrian
OBJECTIVES
1. Describe the systems development life cycle (SDLC).
2. Explore selected approaches to SDLC.
3. Assess interoperability and its importance in addressing and meeting the challenges of implementing the HITECH Act in health care.
4. Reflect on the past to move forward into the future to determine how new systems will be developed, integrated, and made interoperable in
health care.
Key Terms
Chief information officer
Computer-aided software engineering
Dynamic system development method
End users
Health management information system
Hospital information system
Information technology
Integration
Interoperability
Iteration
Milestones
MoSCoW
Object-oriented
systems
development
Open source software
Prototype
Rapid application development
Rapid prototyping
Repository
Systems development life cycle
TELOS strategy
Waterfall model
Introduction
The following case scenario demonstrates the need to have all of the stakeholders involved from the beginning to the end of the
systems development life cycle (SDLC). Creating the right team to manage the development is a key. Various methodologies have
been developed to guide this process. This chapter reviews the following approaches to SDLC: waterfall, rapid prototyping or rapid
application development (RAD), object-oriented system development (OOSD), and dynamic system development method (DSDM).
When reading about each approach, think about the case scenario and how important it is to understand the specific situational needs
and the various methodologies for bringing a system to life. As in this case, it is generally necessary or beneficial to use a hybrid
approach that blends two or more models for a robust development process.
As the case demonstrates, the process of developing systems or SDLC is an ongoing development with a life cycle. The first step
in developing a system is to understand the problem or business needs. It is followed by understanding the solution or how to address
those needs; developing a plan; implementing the plan; evaluating the implementation; and finally, maintenance, review, and
destruction. If the system needs major upgrading outside of the scope of the maintenance phase, if it needs to be replaced because of
technological advances, or if the business needs change, a new project is launched, the old system is destroyed, and the life cycle
begins anew.
SDLC is a way to deliver efficient and effective information systems that fit with the strategic business plan of an organization.
The business plan stems from the mission of the organization. In the world of health care, its development includes a needs assessment
for the entire organization, which should include outreach linkages (as seen in the case scenario) and partnerships and merged or
shared functions. The organization’s participating physicians and other ancillary professionals and their offices are included in
thorough needs assessments. When developing a strategic plan, the design must take into account the existence of the organization
within the larger healthcare delivery system and assess the various factors outside of the organization itself, including technological,
legislative, and environmental issues that impact the organization. The plan must identify the needs of the organization as a whole and
propose solutions to meet those needs or a way to address the issues.
SDLC can occur within an organization, be outsourced, or be a blend of the two approaches. With outsourcing, the team hires an
outside organization to carry out all or some of the development. Developing systems that truly meet business needs is not an easy task
and is quite complex. Therefore, it is common to run over budget and miss milestones. When reading this chapter, reflect on the case
scenario and in general the challenges teams face when developing systems.
CASE SCENARIO
Envision two large healthcare facilities that merge resources to better serve their community. This merger is called the Wellness Alliance, and its
mission is to establish and manage community health programming that addresses the health needs of the rural, underserved populations in the area.
The Wellness Alliance would like to establish pilot clinical sites in five rural areas to promote access and provide health care to these underserved
consumers. Each clinical site will have a full-time program manager and three part-time employees (a secretary, a nurse, and a doctor). Each program
manager will report to the wellness program coordinator, a newly created position within the Wellness Alliance.
Because you are a community health nurse with extensive experience, you have been appointed as the wellness program coordinator. Your
directive is to establish these clinical sites within 3 months and report back in 6 months as to the following: (1) community health programs offered,
(2) level of community involvement in outreach health programs and clinical site–based programming, (3) consumer visits made to the clinical site,
and (4) personnel performance.
You are excited and challenged, but soon reality sets in: You know that you have five different sites with five different program managers. You
need some way to gather the vital information from each of them in a similar manner so that the data are meaningful and useful to you as you
develop your reports and evaluate the strengths and weaknesses of the pilot project. You know that you need a system that will handle all of the pilot
project’s information needs.
Your first stop is the chief information officer of the health system, a nurse informaticist. You know her from the health management
information system miniseminar that she led. After explaining your needs, you share with her the constraint that this system must be in place in 3
months when the sites are up and running before you make your report. When she begins to ask questions, you realize that you do not know the
answers. All you know is that you must be able to report on which community health programs were offered, track the level of community
involvement in outreach health programs and clinical site–based programming, monitor consumer visits made to the clinical site, and monitor the
performance of site personnel. You know that you want accessible, real-time tracking, but as far as programming and clinical site–related activities
are concerned, you do not have a precise description of either the process and procedures that will be involved in implementing the pilot or the means
by which they will gather and enter data.
The chief information officer requires that you and each program manager remain involved in the development process. She assigns an
information technology (IT) analyst to work with you and your team in the development of a system that will meet your current needs. After the
first meeting, your head is spinning: The IT analyst has challenged your team not only to work out the process for your immediate needs, but also to
envision what your needs will be in the future. At the next meeting, you tell the analyst that your team does not feel comfortable trying to map
everything out at this point. He states that there are several ways to go about building the system and software by using the systems development life
cycle (SDLC). Noticing the blank look on everyone’s faces, he explains that the SDLC is a series of actions used to develop an information system.
The SDLC is similar to the nursing process, in which the nurse must assess, diagnose, plan, implement, evaluate, and revise. If the plan developed in
this way does not meet the patient’s need or if a new problem arises, the nurse either revises and updates the plan or starts anew. Likewise, you will
plan, analyze, design, implement, operate, support, and secure the proposed community health system.
The SDLC is an iterative process—a conceptual model that is used in project management describing the phases involved in building or
developing an information system. It moves from assessing feasibility or project initiation, to design analysis, to system specification, to
programming, to testing, to implementation, to maintenance, and to destruction—literally from beginning to end. As the IT analyst describes this
process, once again he sees puzzled looks. He quickly states that even the destruction of the system is planned—that is, how it will be retired, broken
down, and replaced with a new system. Even during upgrades, destruction tactics can be invoked to secure the data and even decide if servers are to
be disposed of or repurposed. The security people will tell you that this is their phase, where they make sure that any sensitive information is
properly handled and decide whether the data are to be securely and safely archived or destroyed.
After reviewing all of the possible methods and helping you to conduct your feasibility and business study, the analyst chooses the dynamic
system development method (DSDM). This SDLC model was chosen because it works well when the time span is short and the requirements are
fluctuating and mainly unknown at the outset. The IT analyst explains that this model works well on tight schedules and is a highly iterative and
incremental approach stressing continuous user input and involvement. As part of this highly iterative process, the team will revisit and loop through
the same development activities numerous times; this repetitive
examination provides ever-increasing levels of detail, thereby improving accuracy. The analyst explains that you will use a mockup of the
hospital information system (HIS) and design for what is known; you will then create your own minisystem that will interface with the HIS.
Because time is short, the analysis, design, and development phases will occur simultaneously while you are formulating and revising your specific
requirements through the iterative process so that they can be integrated into the system.
The functional model iteration phase will be completed in 2 weeks based on the information that you have given to the analyst. At that time, the
prototype will be reviewed by the team. The IT analyst tells you to expect at least two or more iterations of the prototype based on your input. You
should end with software that provides some key capabilities. Design and testing will occur in the design and build iteration phase and continue until
the system is ready for implementation, the final phase. This DSDM should work well because any previous phase can be revisited and reworked
through its iterative process.
One month into the SDLC process, the IT analyst tells the team that he will be leaving his position at Wellness Alliance. He introduces his
replacement. She is new to Wellness Alliance and is eager to work with the team. The initial IT analyst will be there 1 more week to help the new
analyst with the transition. When he explains that you are working through DSDM, she looks a bit panicky and states that she has never used this
approach. She has used the waterfall, prototyping, iterative enhancement, spiral, and object-oriented methodologies—but never the DSDM. From
what she heard, DSDM is new and often runs amok because of the lack of understanding as to how to implement it appropriately. After 1 week on
the project, the new IT analyst believes that this approach was not the best choice. As the leader of this SDLC, she is growing concerned about
having a product ready at the point when the clinical sites open. She might combine another method to create a hybrid approach with which she
would be more comfortable; she is thinking out loud and has everyone very nervous.
The IT analyst reviews the equipment that has arrived for the sites and is excited to learn that the Mac computers were ordered from Apple. They
will be powerful and versatile enough for your needs.
Two months after the opening of the clinical sites, you as the wellness program coordinator are still tweaking the system with the help of the IT
analyst. It is hard to believe how quickly the team was able to get a robust system in place. As you think back on the process, it seems so long ago
that you reviewed the HIS for deficiencies and screen shots. You reexamined your requirements and watched them come to life through five
prototype iterations and constant security updates. You trained your personnel on its use, tested its performance, and made final adjustments before
implementation. Your own stand-alone system that met your needs was installed and fully operational on the Friday before you opened the clinic
doors on Monday, 1 day ahead of schedule. You are continuing to evaluate and modify the system, but that is how the SDLC works: It is never
finished, but rather constantly evolving.
Waterfall Model
The waterfall model is one of the oldest methods and literally depicts a waterfall effect—that is, the output from each previous phase
flows into or becomes the initial input for the next phase. This model is a sequential development process in that there is one pass
through each component activity from conception or feasibility through implementation in a linear order. The deliverables for each
phase result from the inputs and any additional information that is gathered. There is minimal or no iterative development where one
takes advantage of what was learned during the development of earlier deliverables. Many projects are broken down into six phases
(Figure 10-1), especially small- to medium-size projects.
Figure 10-1 Waterfall phases
Feasibility
As the term implies, the feasibility study is used to determine whether the project should be initiated and supported. This study should
generate a project plan and estimated budget for the SDLC phases. Often, the TELOS strategy—technological and systems,
economic, legal, operational, and schedule feasibility—is followed. Technological and systems feasibility addresses the issues of
technological capabilities, including the expertise and infrastructure to complete the project. Economic feasibility is the cost–benefit
analysis, weighing the benefits versus the costs to determine whether the project is fiscally possible to do and worth undertaking.
Formal assessments should include return on investment. Legal feasibility assesses the legal ramifications of the project, including
current contractual obligations, legislation, regulatory bodies, and liabilities that could affect the project. Operational feasibility
determines how effective the project will be in meeting the needs and expectations of the organization and actually achieving the goals
of the project or addressing and solving the business problem. Schedule feasibility assesses the viability of the time frame, making sure
it is a reasonable estimation of the time and resources necessary for the project to be developed in time to attain the benefits and meet
constraints. TELOS helps to provide a clear picture of the feasibility of the project.
Analysis
During the analysis phase, the requirements for the system are teased out from a detailed study of the business needs of the
organization. As part of this analysis, work flows and business practices are examined. It may be necessary to consider options for
changing the business process.
Design
The design phase focuses on high- and low-level design and interface and data design. At the high-level phase, the team establishes
which programs are needed and ascertains how they will interact. At the low-level phase, team members explore how the individual
programs will actually work. The interface design determines what the look and feel will be or what the interfaces will look like.
During data design, the team critically thinks about and verifies which data are required or essential.
The analysis and design phases are vital in the development cycle, and great care is taken during these phases to ensure that the
software’s overall configuration is defined properly. Mockups or prototypes of screen shots, reports, and processes may be generated
to clarify the requirements and get the team or stakeholders on the same page, limiting the occurrence of glitches that might result in
costly software development revisions later in the project.
Implement
During this phase, the designs are brought to life through programming code. The right programming language, such as C++, Pascal,
Java, and so forth, is chosen based on the application requirements.
Test
The testing is generally broken down into five layers: (1) the individual programming modules, (2) integration, (3) volume, (4) the
system as a whole, and (5) beta testing. Typically, the programs are developed in a modular fashion, and these individual modules are
then subjected to detailed testing. The separate modules are subsequently synthesized, and the interfaces between the modules are
tested. The system is evaluated with respect to its platform and the expected amount or volume of data. It is then tested as a complete
system by the team. Finally, to determine if the system performs appropriately for the user, it is beta tested. During beta testing, users
put the new system through its paces to make sure that it does what they need it to do to perform their jobs.
Maintain
Once the system has been finalized from the testing phase, it must be maintained. This could include user support through actual
software changes necessitated through use or time.
The waterfall approach is linear and progresses sequentially. The main lack of iterative development is seen as a major weakness,
according to Purcell (2007). No projects are static, and typically changes occur during the SDLC. As requirements change, there is no
way to address them formally using the waterfall method after project requirements are developed. The waterfall model should be used
for simple projects when the requirements are well known and stable from the outset.
Rapid Prototyping or Rapid Application Development
As technology advances and faster development is expected, rapid prototyping, also known as rapid application development
(RAD), provides a fast way to add functionality through prototyping and user testing. It is easier for users to examine actual
prototypes rather than documentation. A rapid requirements-gathering phase relies on workshops and focus groups to build a
prototype application using real data. This prototype is then beta tested with users, and their feedback is used to perfect or add
functionality and capabilities to the system. According to Alexandrou (2010), “RAD (rapid application development) proposes that
products can be developed faster and of higher quality” (para. 1). The RAD approach uses informal communication, repurposes
components, and typically follows a fast-paced schedule. Object-oriented programming using such languages as C++ and Java
promotes software repurposing and reuse.
The major advantage is the speed with which the system can be deployed; a working, usable system can be built within 3 months.
The use of prototyping allows the developers to skip steps in the SDLC process in favor of getting a mockup in front of the user. At
times, the system may be deemed acceptable if it meets a predefined minimum set of requirements rather than all of the identified
requirements. This rapid deployment also limits the project’s exposure to change elements. Unfortunately, the fast pace can be its
biggest disadvantage in some cases. Once one is locked into a tight development schedule, the process may be too fast for adequate
testing to be put in place and completed. The most dangerous lack of testing is in the realm of security.
The RAD approach is chosen because it builds systems quickly through user-driven prototyping and adherence to quick, strict
delivery milestones. This approach continues to be refined and honed, and other contemporary manifestations of RAD continue to
emerge in the agile software development realm.
Object-Oriented Systems Development
The object-oriented systems development (OOSD) model “combines the logic of the systems development life cycle with the power
of object-oriented modeling and programming” (Stair & Reynolds, 2008, p. 501). Object-oriented modeling makes an effort to
represent real-world objects, by modeling the real-world entities or things (e.g., hospital, patient, account, nurse) into abstract
computer software objects. Once the system is object oriented, all of the interactions or exchanges take place between or among the
objects. The objects are derived from classes, and “an object consists of both data and the actions that can be performed on the data”
(p. 501).
Class hierarchy allows objects to inherit characteristics or attributes from parent classes, which fosters object reuse resulting in less
coding. The object-oriented programming languages, such as C++ and Java, promote software repurposing and reuse. Therefore, the
class hierarchy must be clearly and appropriately designed to reap the benefits of this SDLC approach, which uses object-oriented
programming to support the interactions of objects.
For example, in the case scenario, a system could be developed for the Wellness Alliance to manage the community health
programming for the clinic system being set up for outreach. There could be a class of programs, and well-baby care could be an
object in the class of programs; programs is a relationship between Wellness Alliance and well-baby care. The program class has
attributes, such as clinic site, location address, or attendees or patients. The relationship itself may be considered an object having
attributes, such as pediatric programs. The class hierarchy from which all of the system objects are created with resultant object
interactions must be clearly defined.
The OOSD model is a highly iterative approach. The process begins by investigating where object-oriented solutions can address
business problems or needs, determining user requirements, designing the system, programming or modifying object modeling (class
hierarchy and objects), implementing, user testing, modifying, and implementing the system, and ends with the new system being
reviewed regularly at established intervals and modifications being made as needed throughout its life.
Dynamic System Development Method
The dynamic system development method (DSDM) is a highly iterative and incremental approach with a high level of user input and
involvement. The iterative process requires repetitive examination that enhances detail and improves accuracy. The DSDM has three
phases: (1) preproject; (2) project life cycle (feasibility and business studies, functional model iteration, design and build iteration, and
implementation); and (3) postproject.
In the preproject phase, buy-in or commitment is established and funding is secured. This helps to identify the stakeholders
(administration and end users) and gain support for the project.
In the second phase, the project’s life cycle begins. This phase includes five steps: (1) feasibility, (2) business studies, (3)
functional model iteration, (4) design and build iteration, and (5) implementation.
In steps 1 and 2, the feasibility and business studies are completed. The team ascertains if this project meets the required business
needs while identifying the potential risks during the feasibility study. In step 1, the deliverables are a feasibility report, project plan,
and a risk log. Once the project is deemed feasible, step 2, the business study, is begun. The business study extends the feasibility
report by examining the processes, stakeholders, and their needs. It is important to align the stakeholders with the project and secure
their buy-in because it is necessary to have user input and involvement throughout the entire DSDM process. Therefore, bringing them
in at the beginning of the project is imperative.
Using the MoSCoW approach, the team works with the stakeholders to develop a prioritized requirements list and a development
plan. MoSCoW stands for “Must have, Should have, Could have, and Would have.” The “must have” requirements are needed to meet
the business needs and are critical to the success of the project. “Should have” requirements are those that would be great to have if
possible, but the success of the project does not depend on them being addressed. The “could have” requirements are those that would
be nice to have met, and the “would have” requirements can be put off until later; these may be undertaken during future
developmental iterations. Timeboxing is generally used to develop the project plan. In timeboxing, the project is divided into sections,
each having its own fixed budget and dates or milestones for deliverables. The MoSCoW approach is then used to prioritize the
requirements within each section; the requirements are the only variables because the schedule and budget are set. If a project is
running out of time or money, the team can easily omit the requirements that have been identified as the lowest priority to meet their
schedule and budget obligations. This does not mean that the final deliverable, the actual system, would be flawed or incomplete.
Instead, it meets the business needs. According to Haughey (2010), the 80/20 rule or Pareto principle can be applied to nearly
everything. The Pareto principle states that 80% of the project comes from 20% of the system requirements; therefore, the 20% of
requirements must be the crucial requirements or those with the highest priority. One also must consider the pancake principle: The
first pancake is not as good as the rest, and one should know that the first development will not be perfect. This is why it is extremely
important to clearly identify the “must have” and “should have” requirements.
In the third step of the project life cycle phase, known as functional model iteration, the deliverables are a functional model and
prototype ready for user testing. Once the requirements are identified, the next step is to translate them into a functional model with a
functioning prototype that can be evaluated by users. This could take several iterations to develop the desired functionality and
incorporate the users’ input. At this stage, the team should examine the quality of the product and revise the list requirements and risk
log. The requirements are adjusted, the ones that have been realized are deleted, and the remaining requirements are prioritized. The
risk log is revised based on the risk analysis completed during and after prototype development.
The design and build iteration step focuses on integrating functional components and identifying the nonfunctional requirements
that need to be in the tested system. Testing is crucial; the team will develop a system that the end users can safely use on a daily basis.
The team will garner user feedback and generate user documentation. These efforts provide this step’s deliverable, a tested system
with documentation for the next and final phase of the development process.
In the final step of the project life cycle phase, known as implementation, deliverables are the system (ready to use),
documentation, and trained users. The requirements list should be satisfied, along with the users’ needs. Training users and
implementing the approved system is the first part of this phase, and the final part consists of a full review. It is important to review the
impact of the system on the business processes and to determine if it addressed the goals or requirements established at the beginning
of the project. This final review determines if the project is completed or if further development is necessary. If further development is
needed, preceding phases are revisited. If the project is complete and satisfies the users, then it moves into maintenance and ongoing
development.
The final phase is labeled “postproject.” In this phase, the team verifies that the system is functioning properly. Once verified, the
maintenance schedule is begun. Because the DSDM is iterative, this postproject phase is seen as ongoing development and any of the
deliverables can be refined. This is what makes the DSDM such an iterative development process.
DSDM is one of an increasing number of agile methodologies being introduced, such as Scrum and Extreme Programming. These
new approaches address the organizational, managerial, and interpersonal communication issues that often bog down SDLC projects.
Empowerment of teams and user involvement enhance the iterative and programming strengths provided in these SDLC models.
Computer-Aided Software Engineering Tools
When reviewing SDLC, the computer-aided software engineering (CASE) tools that will be used must be described. “CASE tools
automate many of the tasks required in a systems development effort and encourage adherence to the SDLC, thus instilling a high
degree of rigor and standardization [in] the entire systems development process” (Stair & Reynolds, 2008, p. 500). These tools help to
reduce cost and development time while enriching the quality of the product. CASE tools contain a repository with information about
the system: models, data definitions, and references linking models together. They are valuable in their ability to make sure the models
follow diagramming rules and are consistent and complete.
The various types of tools can be referred to as upper CASE tools or lower CASE tools. The upper CASE tools support the
analysis and design phases, whereas the lower CASE tools support implementation. The tools can also be general or specific in nature,
with the specific tools being designed for a particular methodology.
Two examples of CASE tools are Visible Analyst and Rational Rose. According to Andoh-Baidoo, Kunene, and Walker (n.d.),
Visible Analyst “supports structured and object-oriented design (UML),” whereas Rational Rose “supports solely object-oriented
design (UML)” (p. 372). Both tools can “build and reverse database schemas for SQL and Oracle” and “support code generation for
pre.NET versions of Visual Basic” (p. 372). Visible Analyst can also support shell code generation for pre.NET versions of C and
COBOL, whereas Rational Rose can support complete code for C++ and Java. In addition, Andoh-Baidoo et al. found that Rational
Rose “[p]rovides good integration with Java, and incorporates common packages into class diagrams and decompositions through
classes” (p. 372).
CASE tools have many advantages, including decreasing development time and producing more flexible systems. On the down
side, they can be difficult to tailor or customize and use with existing systems.
Open Source Software and Free/Open Source Software
Another area that must be discussed with SDLC is open source software (OSS). An examination of job descriptions or advertisements
for candidates shows that many IS and IT professionals need a thorough understanding of SDLC and OSS development tools (e.g.,
PHP, MySQL, and HTML). With OSS, any programmer can implement, modify, apply, reconstruct, and restructure the rich libraries
of source codes available from proven, well-tested products. As Vauldes (2008) notes, “Examples of FOSS successes are the Internet,
Google, Web 2.0, the GNU/Linux operating system, courseware such as Moodle, and the Veterans Affairs VistA hospital system” (p.
7).
To transform health care, it is necessary for clinicians to use information systems that can share patient data (Goulde & Brown,
2006). This all sounds terrific and many people wonder why it has not happened yet, but the challenges are many. How does one
establish the networks necessary to share data between and among all healthcare facilities easily and securely? “Healthcare IT is
beginning to adopt open source software to address these challenges” (p. 4). Early attempts at OSS ventures in the healthcare realm
failed because of a lack of support or buy-in for sustained effort, technologic lags, authority and credibility, and other such issues.
“Spurred by a greater sense of urgency to adopt IT, health industry leaders are showing renewed interest in open source solutions” (p.
5). Health care is realizing the benefits of OSS. According to Goulde and Brown, “other benefits of open source software—low cost,
flexibility, opportunities to innovate—are important but independence from vendors is the most relevant for health care” (p. 10).
Interoperability
Interoperability, the ability to share information across organizations, will remain paramount under the HITECH Act (see the
Legislative Aspects of Nursing Informatics: HITECH and HIPAA chapter). The ability to share patient data is extremely important,
both within an organization and across organizational boundaries. According to the Health Information and Management Systems
Society (HIMSS, 2010), few healthcare systems take advantage of the full potential of the current state of the art in computer science
and health informatics. The consequences of this situation include a drain on financial resources from the economy, the inability to
truly mitigate the occurrence of medical errors, and a lack of national preparedness to respond to natural and manmade epidemics and
disasters. HIMSS has created the Integration and Interoperability Steering Committee to guide the industry on allocating resources to
develop and implement standards and technology needed to achieve interoperability (para. 2).
As we enter into SDLCs, we must be aware of how this type of development will affect both our own healthcare organization and
the healthcare delivery system as a whole. In an ideal world, we would all work together to create systems that are integrated within
our own organization while having the interoperability to cross organizational boundaries and unite the healthcare delivery system to
realize the common goal of improving the quality of care provided to consumers.
Summary
At times during the SDLC, new information affects the outputs from earlier phases; the development effort may be reexamined or
halted until these modifications can be reconciled with the current design and scope of the project. At other times, teams are
overwhelmed with new ideas from the iterative SDLC process that result in new capabilities or features that exceed the initial scope of
the project. Astute team leaders will preserve these ideas or initiatives so they can be considered at a later time. The team should
develop a list of recommendations to improve the current software when the project is complete. This iterative and dynamic exchange
makes the SDLC robust.
As technology and research continue to advance, new SDLC models are being pioneered and revised to enhance development
techniques. The interpretation and implementation of any model selected reflect the knowledge and skill of the team applying the
model. The success of the project is often directly related to the quality of the organizational decision making throughout the project—
that is, how well the plan was followed and documented. United efforts to create systems that are integrated and interoperable will
define the future of health care.
THOUGHT-PROVOKING
QUESTIONS
1. How would you describe cognitive informatics? Reflect on a plan of care that you have developed for a patient. How could cognitive informatics
be used to create tools to help with this important work?
2. Think of a clinical setting you are familiar with and envision artificial intelligence tools. Are there any current tools in use? Which tools would
enhance practice in this setting and why?
3. Reflect on the SDLC in relation to the quality of the organizational decision making throughout the project. What are some of the major
stumbling blocks faced by healthcare organizations?
References
Alexandrou, M. (2010). Rapid application development (RAD) methodology. http://www.mariosalexandrou.com/methodologies/rapid-
application-development.asp
Andoh-Baidoo, F., Kunene, K., & Walker, R. (n.d.). An evaluation of CASE tools as pedagogical aids in software development
courses. http://www.swdsi.org/swdsi2009/Papers/9K10
Goulde, M., & Brown, E. (2006). Open source software: A primer for health care leaders. http://www.protecode.com/an-open-
source-world-a-primer-on-licenses-obligations-and-your-company/
Haughey, D. (2010). Pareto analysis step by step. http://www.projectsmart.co.uk/pareto-analysis-step-by-step.html
Health Information and Management Systems Society (HIMSS). (2010). Integration and interoperability.
http://www.himss.org/library/interoperability-standards?navItemNumber=13323
Purcell, J. (2007). Comparison of software development lifecycle methodologies. Retrieved June 2010 from
http://www2.giac.org/resources/whitepaper/application/217
Stair, R., & Reynolds, G. (2008). Principles of information systems (8th ed.). Boston, MA: Thomson Course Technology.
Vauldes, I. (2008). Free and open source software in healthcare 1.0: American Medical Informatics Association. Open Source
Working Group white paper. Retrieved July 2010 from https://www.amia.org/files/final-os-wg%20white%20paper_11_19_08
Chapter 11
Administrative Information Systems
Marianela Zytkowski, Susan Paschke, Dee McGonigle, and Kathleen Mastrian
OBJECTIVES
1. Explore agency-based health information systems.
2. Evaluate how administrators use core business systems in their practice.
3. Assess the function and information output from selected information systems used in healthcare organizations.
Key Terms
Acuity system
Admission, discharge, and transfer system
American National Standards Institute (ANSI)
Attribute
Care plan
Case management information system
Clinical documentation system
Clinical information system
Collaboration
Column
Communication system
Computerized physician order entry system
Core business system
Data dictionary
Data file
Data mart
Data mining
Data warehouse
Database
Database management system
Decision support
Drill-down
Electronic health record
Entity
Entity–relationship diagram
Field
Financial system
Information system
Information technology
International Organization for Standardization (ISO)
Key field
Knowledge exchange
Laboratory information system
Managed care information system
Master patient index
Order entry system
Patient care information system
Patient care support system
Patient centered
Pharmacy information system
Picture and archiving
communication system
Primary key
Query
Radiology information system
Record
Relational database management system (RDMS)
Repository
Row
Scheduling system
Stakeholder
Standardized plan of care
Structured Query Language (SQL)
Table
Tuple
Introduction
To compete in the ever-changing healthcare arena, organizations require quick and immediate access to a variety of types of
information, data, and bodies of knowledge for daily clinical, operational, financial, and human resource activities. Information is
continuously shared between units and departments within healthcare organizations and is also required or requested from other
healthcare organizations, regulatory and government agencies, educational and philanthropic institutions, and consumers.
Healthcare organizations integrate a variety of clinical and administrative types of information systems. These systems collect,
process, and distribute patient-centered data to aid in managing and providing care. Together, they create a comprehensive record of
the patient’s medical history and support organizational processes. Each of these systems is unique in the way it functions and provides
information to clinicians and administrators. An understanding of how each of these types of systems works within healthcare
organizations is fundamental in the study of informatics.
Types of Healthcare Organization Information Systems
Case Management Information Systems
Case management information systems identify resources, patterns, and variances in care to prevent costly complications related to
chronic conditions and to enhance the overall outcomes for patients with chronic illness. These systems span past episodes of treatment
and search for trends among the records. Once a trend is identified, case management systems provide decision support promoting
preventive care. Care plans are a common tool found in case management systems. A care plan is a set of care guidelines that outline
the course of treatment and the recommended interventions that should be implemented to achieve optimal results. By using a
standardized plan of care, these systems present clinicians with treatment protocols to maximize patient outcomes and support best
practices.
Case management information systems are especially beneficial for patient populations with a high cost of care and complex health
needs, such as the elderly or patients with chronic disease conditions. For example, such systems may be used in treating patients with
AIDS. The case management system applies a care plan to treat the patient and manage care better from outpatient to inpatient visits,
where opportunistic infections, such as Pneumocystis jiroveci pneumonia, are common complications (DiJerome, 1992). Avoiding
these types of complications requires identifying the right resources for care and implementing preventive treatments across all
medical visits. Ultimately, this preventive care decreases the costs of care for patients with AIDS and supports a better quality of life.
Such systems increase the value of individual care while controlling the costs and risks associated with long-term health care.
Case management systems compile massive amounts of information obtained over a patient’s lifetime by reaching far beyond the
walls of the hospital and track care from one medical visit to the next (Simpson & Falk, 1996). Information collected by these systems
is processed in a way that helps to reduce risks, ensure quality, and decrease costs.
Communication Systems
Communication systems promote interaction among healthcare providers and between providers and patients. Such systems have
historically been kept separate from other types of health information systems and from one another. Healthcare professionals
overwhelmingly recognize the value of these systems, however, so they are now more commonly integrated into the design of other
types of systems as a newly developing standard within the industry. Examples of communication systems include call light systems,
wireless telephones, pagers, e-mail, and instant messaging, which have traditionally been forms of communication targeted at
clinicians. Other communication systems target patients and their families. Some patients are now able to access their electronic chart
from home via an Internet connection. They can update their own medical record to inform their physician of changes to their health or
personal practices that impact their physical condition. Inpatients in hospital settings also receive communication directly to their
room. Patients and their families may, for example, review individualized messages with scheduled tests and procedures for the day
and confirm menu choices for their meals. These types of systems may also communicate educational messages, such as smoking
cessation advice.
As health care begins to introduce more of this technology into practice, the value of having communication tools integrated with
other types of systems is being widely recognized. Integrating communication systems with clinical applications provides a real-time
approach that facilitates inter-actions among the entire healthcare team, patients, and their families to enhance care. These systems
enhance the flow of communication within an organization and promote an exchange of information to care better for patients. The
next generation of communication systems will be integrated with other types of healthcare systems and guaranteed to work together
smoothly. The Research Brief discusses the economic impact of communication inefficiencies in U.S. hospitals.
Core Business Systems
Core business systems enhance administrative tasks within healthcare organizations. Unlike clinical information systems (CISs),
whose aim is to provide direct patient care, these systems support the management of health care within an organization. Core business
systems provide the framework for reimbursement, support of best practices, quality control, and resource allocation. There are four
common core business systems: (1) admission, discharge, and transfer (ADT) systems; (2) financial systems; (3) acuity systems; and
(4) scheduling systems.
Admission, discharge, and transfer systems provide the backbone structure for the other types of clinical and business systems
(Hassett & Thede, 2003). Admitting, billing, and bed management departments most commonly use ADT systems. These systems hold
key information on which all other systems rely. For example, ADT systems maintain the patient’s name; medical record number; visit
or account number; and demographic information, such as age, gender, home address, and contact information. Such systems are
considered the central source for collecting this type of patient information and communicating it to other types of healthcare
information systems.
Research Brief
Researchers attempted to quantify the costs of poor communication, termed “communication inefficiencies,” in hospitals. This qualitative study was
conducted in seven acute care hospitals of varying sizes via structured interviews with key informants at each facility. The interview questions
focused on four broad categories: (1) communication bottlenecks, (2) negative outcomes as a result of those bottlenecks, (3) subjective perceptions of
the potential effectiveness of communication improvements on the negative outcomes, and (4) ideas for specific communication improvements. The
researchers independently coded the interview data and then compared results to extract themes.
All of the interviewees indicated that communication was an issue. Inefficiencies revolved around time spent tracking people down to
communicate with them, with various estimates provided: 3 hours per nursing shift wasted tracking people down, 20% of productive time wasted on
communication bottlenecks, and a reported average of five to six telephone calls to locate a physician. Several respondents pointed to costly medical
errors that were the direct result of communication issues. Communication lapses also resulted in inefficient use of clinician resources and increased
length of stay for patients.
The researchers developed a conceptual model of communication quality with four primary dimensions: (1) efficiency of resource use, (2)
effectiveness of resource use, (3) quality of work life, and (4) service quality. They concluded that the total cost of communication inefficiencies in
U.S. hospitals is more than $12 billion annually and estimated that a 500-bed hospital could lose as much as $4 million annually because of such
problems. They urge the adoption of information technologies to redesign workflow processes and promote better communication.
Source: The full article appears in Agarwal, R., Sands, D., Schneider, J., & Smaltz, D. (2010). Quantifying the economic impact of communication
inefficiencies in U.S. hospitals. Journal of Healthcare Management, 55(4), 265–281.
Financial systems manage the expenses and revenue for providing health care. The finance, auditing, and accounting departments
within an organization most commonly use financial systems. These systems determine the direction for maintenance and growth for a
given facility. They often interface to share information with materials management, staffing, and billing systems to balance the
financial impact of these resources within an organization. Financial systems report fiscal outcomes, which can then be tracked and
related to the organizational goals of an institution. These systems are key components in the decision-making process as healthcare
institutions prepare their fiscal budgets. They often play a pivotal role in determining the strategic direction for an organization.
Acuity systems monitor the range of patient types within a healthcare organization using specific indicators. They track these
indicators based on the current patient population within a facility. By monitoring the patient acuity, these systems provide feedback
about how intensive the care requirement is for an individual patient or group of patients. Identifying and classifying a patient’s acuity
can promote better organizational management of the expenses and resources necessary to provide care. Acuity systems help predict
the ability and capacity of an organization to care for its current population. They also forecast future trends to allow an organization to
successfully strategize on how to meet upcoming market demands.
Scheduling systems coordinate staff, services, equipment, and allocation of patient beds. They are frequently integrated with the
other types of core business systems. By closely monitoring staff and physical resources, these systems provide data to the financial
systems. For example, resource-scheduling systems may provide information about operating room use or availability of intensive care
unit beds and regular nursing unit beds. These systems also provide great assistance to financial systems when they are used to track
medical equipment within a facility. Procedures and care are planned when the tools and resources are available. Scheduling systems
help to track resources within a facility while managing the frequency and distribution of those resources.
Order Entry Systems
Order entry systems are one of the most important systems in use today. They automate the way that orders have traditionally been
initiated for patients—that is, clinicians place orders using these systems instead of creating traditional handwritten transcriptions onto
paper. Order entry systems provide major safeguards by ensuring that physician orders are legible and complete, thereby providing a
level of patient safety that was historically missing with paper-based orders. Computerized physician order entry systems provide
decision support and automated alert functionality that was unavailable with paper-based orders.
The Institute of Medicine estimates that medical errors cost the United States approximately $37.6 billion each year; nearly $17
billion of those costs are associated with preventable errors. Consequently, the federal agency recommends eliminating reliance on
handwriting for ordering medications and other treatment needs (Agency for Healthcare Research and Quality, 2000).
Because of the global concern for patient safety as a result of incorrect and misinterpreted orders, healthcare organizations are
incorporating order entry systems into their operations as a standard tool for practice. Such systems allow for clear and legible orders,
thereby both promoting patient safety and streamlining care. Although much of the health information technology literature suggests
that physicians are resistant to adopting health information technology, a recent study by Elder, Wiltshire, Rooks, BeLue, and Gary
(2010) found that physicians who use information technology were more satisfied overall with their careers. The Informatics Tools to
Promote Patient Safety and Clinical Outcomes chapter provides more information about the use of computerized physician order entry
systems in clinical care.
Patient Care Support Systems
Most specialty disciplines within health care have an associated patient care information system. These patient-centered systems
focus on collecting data and disseminating information related to direct care. Several of these systems have become mainstream types
of systems used in health care. The four systems most commonly encountered in health care include (1) clinical documentation
systems, (2) pharmacy information systems, (3) laboratory information systems, and (4) radiology information systems.
Clinical documentation systems, also known as “clinical information systems,” are the most commonly used type of patient care
support system within healthcare organizations. CISs are designed to collect patient data in real time. They enhance care by putting
data at the clinician’s fingertips and enabling decision making where it needs to occur—that is, at the bedside. For that reason, these
systems often are easily accessible at the point of care for caregivers interacting with the patient. CISs are patient centered, meaning
they contain the observations, interventions, and outcomes noted by the care team. Team members enter information, such as the plan
of care, hemodynamic data, laboratory results, clinical notes, allergies, and medications. All members of the treatment team use
clinical documentation systems; for example, pharmacists, allied health workers, nurses, physicians, support staff, and many others
access the clinical record for the patient using these systems. Frequently these types of systems are also referred to as the electronic
patient record or electronic health record. The Electronic Health Record and Clinical Informatics chapter provides a comprehensive
overview of CISs and the electronic health record.
Pharmacy information systems also have become mainstream patient care support systems. They typically allow pharmacists to
order, manage, and dispense medications for a facility. They also commonly incorporate information regarding allergies and height
and weight to ensure effective medication management. Pharmacy information systems streamline the order entry, dispensing,
verification, and authorization process for medication administration. They often interface with clinical documentation and order entry
systems so that clinicians can order and document the administration of medications and prescriptions to patients while having the
benefits of decision support alerting and interaction checking.
Laboratory information systems were perhaps some of the first systems ever used in health care. Because of their long history of
use within medicine, laboratory systems have been models for the design and implementation of other types of patient care support
systems. Laboratory information systems report on blood, body fluid, and tissue samples, along with biological specimens collected at
the bedside and received in a central laboratory. They provide clinicians with reference ranges for tests indicating high, low, or normal
values to make care decisions. Often, the laboratory system provides result information directing clinicians toward the next course of
action within a treatment regimen.
The final type of patient care support system commonly found within health care is the radiology information system (RIS) found
in radiology departments. These systems schedule, result, and store information related to diagnostic radiology procedures. One feature
found in most radiology systems is a picture archiving and communication system (PACS). The PACS may be a stand-alone
system, kept separate from the main radiology system, or it can be integrated with the RIS and CIS. These systems collect, store, and
distribute medical images, such as computed tomography scans, magnetic resonance images, and X-rays. PACS replace traditional
hard-copy films with digital media that are easy to store, retrieve, and present to clinicians. The benefit of RIS and PACS is their
ability to assist in diagnosing and storing vital patient care support data. Beird (2000) identified the main benefits of PACS as
streamlined workflow, enhanced productivity, and better patient care. Imaging studies can be available in minutes as opposed to 2–6
hours for images in a film-based system. The digital workstations provide enhanced imaging capabilities and on-screen measurement
tools to improve diagnostic accuracy. Finally, the archive system stores images in a database that is readily accessible, so that images
can be easily retrieved and compared to subsequent testing or shared instantly with consultants.
The mobility of patients both geographically and within a single healthcare delivery system challenges information systems
because data must be captured wherever and whenever the patient receives care. In the past, managed care information systems were
implemented to address these issues. According to Ciotti and Zodda (1996), a managed care information system “can nimbly cross
organizational boundaries, includes an enterprise-wide master patient index (MPI), and offers access across provider, geographic,
and departmental lines” (para. 10). Consequently, data can be obtained at any and all of the areas where a patient interacts with the
healthcare system. Patient tracking mechanisms continue to be honed, but the financial impact of health care also has changed these
systems to some extent. The information systems currently in use enable nurses and physicians to make clinical decisions while being
mindful of their financial ramifications. In the future, vast improvements in information systems and systems that support health
information exchange are likely to continue to emerge.
Many healthcare organizations now aggregate data in a data warehouse (DW) for the purpose of mining the data to discover new
relationships and to build organizational knowledge. Mekhjian, Vasila, and Jones (2008) provide insights into how their healthcare
system integrated data from 30 different silos containing physician information into a single comprehensive database. None of the
disparate information systems was able to communicate with any of the others, resulting in poor communication, billing errors, and
issues with continuity of care. By developing a single comprehensive database, the healthcare system was able to facilitate
communications among physicians, particularly consulting physicians from outside the system, and maintain compliance with privacy
regulations.
The most basic element of a database system is the data. Data refers to raw facts that can consist of unorganized text, graphics,
sound, or video. Information is data that has been processed—it has meaning; information is organized in a way that people find
meaningful and useful. Even useful information can be lost if one is mired in unorganized information. Computers can come to the
rescue by helping to create order out of chaos. Computer science and information science are designed to help cut down the amount of
information to a more manageable size and organize it so that users can cope with it more efficiently through the use of databases and
database programs technology. Learning about basic databases and database management programs is paramount so that users can
apply data and information management principles in health care.
Databases are structured or organized collections of data that are typically the main component of an information system. Databases
and database management software allow the user to input, sort, arrange structure, organize, and store data and turn those data into
useful information. An individual can set up a personal database to organize recipes, music, names and addresses, notes, bills, and
other data. In health care, databases and information systems make key information available to healthcare providers and ancillary
personnel to promote the provision of quality patient care. Box 11-1 provides a detailed description of a database.
BOX 11-1 OVERVIEW OF DATABASE CONSTRUCTION
Databases consist of fields (columns) and records (rows). Within each record, one of the fields is identified as the primary key or key field. This
primary key contains a code, name, number, or other information that acts as a unique identifier for that record. In the healthcare system, for
example, a patient is assigned a patient number or ID that is unique for that patient. As you compile related records, you create data files or tables. A
data file is a collection of related records. Therefore, databases consist of one or more related data files or tables.
An entity represents a table, and each field within the table becomes an attribute of that entity. The database developer must critically think
about the attributes for each specific entity. For example, the entity “disease” might have the attributes of “chronic disease,” “acute disease,” or
“communicable disease.” The name of the entity, “disease,” implies that the entity is about diseases. The fields or attributes are “chronic,” “acute,” or
“communicable.”
The entity–relationship diagram specifies the relationship among the entities in the database. Sometimes the implied relationships are readily
apparent based on the entities’ definitions; however, all relationships should be specified as to how they relate to one another. Typically, three
relationships are possible: (1) one to one, (2) one to many, and (3) many to many. A one-to-one relationship exists between the entities of the table
about a patient and the table about the patient’s birth. A one-to-many relationship could exist when one entity is repeatedly used by another entity.
Such a one-to-many relationship could then be a table query for age that could be used numerous times for one patient entity. The many-to-many
relationship reflects entities that are all used repeatedly by other entities. This is easily explained by the entities of patient and nurse. The patient
could have several nurses caring for him or her, and the nurse could have many patients assigned to him or her (see Figure 11-1).
The relational model is a database model that describes data in which all data elements are placed in relation in two-dimensional tables; the
relations or tables are analogous to files. A relational database management system (RDMS) is a system that manages data using this kind of
relational model. A relational database could link a patient’s table to a treatment table (e.g., by a common field, such as the patient ID number). To
keep track of the tables that constitute a database, the database management system uses software called a data dictionary. The data dictionary
contains a listing of the tables and their details, including field names, validation settings, and data types. The data type refers to the type of
information, such as a name, a date, or a time.
Figure 11-1 Example of an entity relationship diagram (ERD)
The database management system is an important program because before it was available, many health systems and businesses had dozens of
database files with incompatible formats. Because patient data come from a variety of sources, these separated, isolated data files required duplicate
entry of the same information, thereby increasing the risk of data entry error. The design of the relational databases eliminates data duplication. Some
examples of popular database management system software include Microsoft’s Access or Visual FoxPro, Corel’s Paradox, Oracle’s Oracle
Database 10g, and IBM’s DB2.
On a large scale, a data warehouse is an extremely large database or repository that stores all of an organization’s or institution’s
data and makes these data available for data mining. The DW can combine an institution’s many different databases to provide
management personnel with flexible access to the data. On the smaller scale, a data mart represents a large database where the data
used by one of the units or a division of a healthcare system are stored and maintained. For example, a university hospital system
might store clinical information from its many affiliate hospitals in a DW, and each separate hospital might have a data mart housing
its data.
There are many ways to access and retrieve information in databases. Searching information in databases can be done through the
use of a query, as is used in Microsoft’s Access database. A query asks questions of the database to retrieve specific data and
information. Box 11-2 provides a detailed description of the Structured Query Language (SQL).
Data mining software sorts thorough data to discover patterns and ascertain or establish relationships. This software discovers or
uncovers previously unidentified relationships among the data in a database by conducting an exploratory analysis looking for hidden
patterns in data. Using such software, the user searches for previously undiscovered or undiagnosed patterns by analyzing the data
stored in a DW. Drill-down is a term that means the user can view DW information by drilling down to lower levels of the database to
focus on information that is pertinent to his or her needs at the moment.
As users move through databases within the healthcare system, they can access anything from enterprise-wide DWs to data marts.
For example, an infection-control nurse might notice a pattern of methicillin-resistant Staphylococcus aureus infections in the local
data mart (a single hospital within a larger system). The nurse might want to find out if the outbreak is local (data mart) or more
widespread in the system (DW). The nurse might also query the database to determine if certain patient attributes (e.g., age or medical
diagnosis) are associated with the incidence of infection.
These kinds of data mining capabilities are also quite useful for healthcare practitioners who wish to conduct clinical research
studies. For example, one might query a database to tease out attributes (patient characteristics) associated with asthma-related
hospitalizations. For a more detailed description and review of data mining, refer to the Data Mining as a Research Tool chapter.
BOX 11-2 SQL
SQL was originally called SEQUEL or Structured English Query Language. SQL, still pronounced “sequel,” now stands for Structured Query
Language; it is a database querying language, rather than a programming language. It is a standard language for accessing and manipulating
databases. SQL is “used with relational databases; it allows users to define the structure and organization of stored data, verify and maintain data
integrity, control access to the data, and define relationships among the stored data items” (University of California at San Diego, 2010, para. 8). In
this way, it simplifies the process of retrieving information from a database in a functional or usable form while facilitating the reorganization of data
within the databases.
The relational database management system is the foundation or basis for SQL. An RDMS stores data in “database objects called tables”
(W3Schools.com, 2010, para. 6). A table is a collection of related data that consists of columns and rows; as noted earlier, columns are also referred
to as fields, and rows are also referred to as records or tuples. Databases can have many tables, and each table is identified by a name (see the
Database Example: School of Nursing Faculty).
SQL statements handle most of the actions users need to perform on a database. SQL is an International Organization for Standardization
(ISO) standard and American National Standards Institute (ANSI) standard, but many different versions of the SQL language exist (Indiana
University, 2010). To remain compliant with the ISO and ANSI standards, SQL must handle or support the major commands of SELECT, UPDATE,
DELETE, INSERT and WHERE in a similar manner (W3Schools.com, 2010). The SELECT command allows you to extract data from a database.
UPDATE updates the data, DELETE deletes the data, and INSERT inserts new data. WHERE is used to specify selection criteria, thereby restricting
the results of the SQL query. Thus SQL allows you to create databases and manipulate them by storing, retrieving, updating, and deleting data.
The database example provided here reflects the faculty listing for a school of nursing. The table that contains the data is identified by the name
“Faculty.” The faculty members are each categorized by the following fields (columns): Last Name, First Name, Department Affiliation, Office
Phone Number, Office Location, and UserID. Each individual faculty member’s information is a record (tuple or row).
Using the SQL command SELECT, all of the records in the “Faculty” table can be selected:
SELECT * FROM Faculty
This command would SELECT all (*) of the records FROM the table known as FACULTY. The asterisk (*) is used to select all of the columns.
According to Mishra, Sharma, and Pandey (2013), there is a new set of challenges and opportunities for managing data, data
mining, and establishing algorithms in the clouds. Data mining in the clouds is emerging and evolving. This frontier is becoming a
potent way to take advantage of the power of cloud computing and combine it with SQL. The world as we know it is changing:
“Clouds” are leading us to develop revolutionary data mining technologies. There are five typical clinical applications for databases:
(1) hospitals, (2) clinical research, (3) clinical trials, (4) ambulatory care, and (5) public health. Some healthcare systems are
connecting their hospitals together by choosing a single CIS to capture data on a system-wide basis. In such healthcare organizations,
multiple application programs share a pool of related data. Think about how potent such databases might potentially be in managing
organizations and providing insights into new relationships that may ultimately transform the way work is done.
Department Collaboration and Exchange of Knowledge and Information
The implementation of systems within health care is the responsibility of many people and departments. All systems require a
partnership of collaboration and knowledge sharing to implement and maintain successful standards of care. Collaboration is the
sharing of ideas and experiences for the purposes of mutual understanding and learning. Knowledge exchange is the product of
collaboration when sharing an understanding of information promotes learning from past experiences to make better future decisions.
Depending on the type of project, collaboration may occur at many different levels within an organization. At an administrative
level, collaboration among key stakeholders is critical to the success of any project. Stakeholders have the most responsibility for
completing the project. They have the greatest influence in the overall design of the system, and ultimately they are the people who are
most impacted by a system implementation. Together with the organizational executive team, stakeholders collaborate on the overall
budget and time frame for a system implementation.
Collaboration may also occur among the various departments impacted by the system. These groups frequently include
http://W3Schools.com
http://W3Schools.com
representatives from information technology, clinical specialty areas, support services, and software vendors. Once a team is
assembled, it defines the objectives and goals of the system. The team members work strategically to align their goals with the goals of
the organization where the system is to be used. The focus for these groups is on planning, resource management, transitioning, and
ongoing support of the system. Their collaboration determines the way in which the project is managed, the deliverables for the
project, the individuals held accountable for the project, the time frame for the project, opportunities for process improvement using
the system, and the means by which resources are allocated to support the system.
From collaboration comes the exchange of information and ideas through knowledge sharing. Specialists exchange knowledge
within their respective areas of expertise to ensure that the system works for an entire organization. From one another, they learn
requirements that make the system successful. This exchange of ideas is what makes healthcare information systems so valuable. A
multidisciplinary approach ensures that systems work in the complex environment of healthcare organizations that have diverse and
complex patient populations.
Summary
The integration of technology within healthcare organizations offers limitless possibilities. As new types of systems emerge, clinicians
will become smarter and more adept at incorporating these tools into their daily practice. Success will be achieved when health care
incorporates technology systems in a way that they are not viewed as separate tools to support healthcare practices, but rather as
necessary instruments to provide health care. Patients, too, will become savvier at using healthcare information systems as a means of
communication and managing their personal and preventive care. In the future, these two mindsets will become expectations for health
care and not simply a high-tech benefit, as they are often viewed today.
Ultimately, it is not the type of systems that are adopted that is important, but rather the method in which they are put into practice.
In an ideal world, robust and transparent information technologies will support clinical and administrative functions and promote safe,
quality, and cost-effective care.
THOUGHT-PROVOKING
QUESTIONS
1. Which type of technology exists today that could be converted into new types of information systems to be used in health care?
2. How could collaboration and knowledge sharing at a single organization be used to help individuals preparing for information technology at a
different facility?
3. Discuss the administrative information systems and their applications.
References
Agency for Healthcare Research and Quality. (2000). Medical errors: The scope of the problem. An epidemic of errors. Retrieved August 2007 from
http://www.ahrq.gov/qual/errback.htm
Beird, L. (2000). How to satisfy both clinical and information technology goals in designing a successful picture archiving and communication system.
Journal of Digital Imaging, 13 (suppl), 10–12. Retrieved from ProQuest Nursing & Allied Health Source (Document ID: 1951921891).
Ciotti, V., & Zodda, F. (1996). Selecting managed care information systems. Journal of the Healthcare Financial Management Ass ociation, 50(6), 35–
36. http://findarticles.com/p/articles/mi_m3257/is_n6_v50/ai_18515376
DiJerome, L. (1992). The nursing case management computerized system: Meeting the challenge of health care delivery through technology. Computers
in Nursing, 10(6), 250–258.
Elder, K., Wiltshire, J., Rooks, R., BeLue, R., & Gary, L. (2010, Summer). Health information technology and physician career satisfaction.
Perspectives in Health Information Management, 1–18. Retrieved from ProQuest Nursing & Allied Health Source (Document ID: 2118694921).
Hassett, M., & Thede, L. (2003). Information in practice: Clinical information systems. In B. Cunningham (Ed.), Informatics and nursing opportunities
and challenges (2nd ed., rev., pp. 222–239). Philadelphia, PA: Lippincott Williams & Wilkins.
Indiana University. (2010). University information technology services knowledge base: What is SQL? http://kb.iu.edu/data/ahux.html
Mekhjian, H., Vasila, M., & Jones, K. (2008). Combine and conquer: Computing from a single database. Physician Executive, 34(5), 30–32, 34–35.
Retrieved from ABI/INFORM Global (Document ID: 1651800501).
Mishra, N., Sharma, S. & Pandey, A. (2013). High performance cloud data mining algorithm and data mining in clouds.
http://www.iosrjournals.org/iosr-jce/papers/Vol8-Issue4/I0845461
Simpson, R. L., & Falk, C. (1996). Technology and case management. In J. Hodgson (Ed.), Information management in nursing and healthcare (pp.
144–152). Springhouse, PA: Springhouse Corporation.
University of California at San Diego. (2010). Data warehouse terms. http://blink.ucsd.edu/technology/help-desk/queries/warehouse/terms.html#s
W3Schools.com. (2010). Introduction to SQL. http://www.w3schools.com/SQL/sql_intro.asp
Chapter 12
The Human–Technology Interface
Judith A. Effken, Dee McGonigle, and Kathleen Mastrian
OBJECTIVES
1. Describe the human–technology interface.
2. Explore human–technology interface problems.
3. Reflect on the future of the human–technology interface.
Key Terms
Cognitive task analysis
Cognitive walkthrough
Cognitive work analysis
Earcons
Ergonomics
Field study
Gulf of evaluation
Gulf of execution
Heuristic evaluation
Human–computer interaction
Human factors
Human–technology interaction
Human–technology interface
Mapping
Situational awareness
Task analysis
Usability
Workaround
Introduction
Several years ago, one of this chapter’s authors stayed in a new hotel on the outskirts of London. When she entered her room, she
encountered three wall-mounted light switches in a row, but with no indication of which lights they operated. In fact, the mapping of
switches to lights was so peculiar that she was more often than not surprised by the light that came on when she pressed a particular
switch. One might conclude that the author had a serious problem, but she prefers to attribute her difficulty to poor design.
When these kinds of technology design issues surface in health care, they are more than just an annoyance. Poorly designed
technology can lead to errors, lower productivity, or even the removal of the system (Alexander & Staggers, 2009). Unfortunately, as
more and more kinds of increasingly complex health information technology applications are integrated, the problem becomes even
worse (Johnson, 2006). However, nurses are very creative and, if at all possible, will design workarounds that allow them to
circumvent troublesome technology. However, workarounds are only a Band-Aid; they are not a long-term solution.
In his classic book The Psychology of Everyday Things, Norman (1988) argued that life would be a lot simpler if people who built
the things that others encounter (such as light switches) paid more attention to how they would be used. At least one everyday thing
meets Norman’s criteria for good design: the scythe. Even people who have never encountered one will pick up a scythe in the manner
needed to use it because the design makes only one way feasible. The scythe’s design fits perfectly with its intended use and a human
user. Would it not be great if all technology were so well fit to human use? In fact, this is not such a far-fetched idea. Scientists and
engineers are making excellent strides in understanding human–technology interface problems and proposing solutions to them.
By the end of this chapter, the reader will be able to (1) define what is meant by the “human–technology interface”; (2) describe
problems with human–technology interfaces currently available in health care; and (3) describe models, strategies, and exemplars for
improving interfaces during the analysis, design, and evaluation phases of the development life cycle.
The Human–Technology Interface
What is the human–technology interface? Broadly speaking, anytime a human uses technology, some type of hardware or software
enables and supports the interaction. It is this hardware and software that defines the interface. The array of light switches described
previously was actually an interface (although not a great one) between the lighting technology in the room and the human user.
In today’s healthcare settings, one encounters a wide variety of human–technology interfaces. Those who work in hospitals may
use bar-coded identification cards to log their arrival time into a human resources management system. Using the same cards, they
might log into their patients’ electronic health record (EHR), access their drugs from a drug administration system, and even
administer their drugs using bar-coding technology. Other examples of human–technology interfaces one might encounter include a
defibrillator, a patient-controlled analgesia (PCA) pump, any number of physiologic monitoring systems, electronic thermometers, and
telephones and pagers.
The human interfaces for each of these technologies are different and can even differ among different brands or versions of the
same device. For example, to enter data into an EHR, one might use a keyboard, a light pen, a touch screen, or voice. Healthcare
technologies may present information via computer screen, printer, or a personal digital assistant (PDA). Patient data might be
displayed in the form of text, pictures (e.g., the results of a brain scan), or even sound (an echocardiogram); and the information may
be arrayed or presented differently, based on roles and preferences. Some human–technology interfaces mimic face-to-face human
encounters. For example, faculty members are increasingly using videoconferencing technology to communicate with their students.
Similarly, telehealth allows nurses to use telecommunication and videoconferencing software to communicate more effectively and
more frequently with patients at home by using the technology to monitor patients’ vital signs, supervise their wound care, or
demonstrate a procedure. Telehealth technology has fostered other virtual interfaces, such as system-wide intensive care units in which
intensivists and specially trained nurses monitor critically ill patients in intensive care units, some of whom may be in rural locations.
Sometimes telehealth interfaces allow patients to interact with a virtual clinician (actually a computer program) that asks questions,
provides social support, and tailors education to identify patient needs based on the answers to screening questions. These human–
technology interfaces have been remarkably successful; sometimes patients even prefer them to live clinicians.
Human–technology interfaces may present information using text, numbers, pictures, icons, or sound. Auditory, visual, or even
tactile alarms may alert users to important information. Users may interact with (or control) the technology via keyboards, digital pens,
voice activation, or even touch.
A small, but growing number of clinical and educational interfaces rely heavily on tactile input. For example, many students learn
to access an intravenous site using virtual technology. Other, more sophisticated virtual reality applications help physicians learn to do
endoscopies or practice complex surgical procedures in a safe environment. Still others allow drug researchers to design new
medications by combining virtual molecules (here, the tactile response is quite different for molecules that can be joined from those
that cannot). In each of these training environments, accurately depicting tactile sensations is critical. For example, feeling the kind
and amount of pressure required to penetrate the desired tissues, but not others, is essential to a realistic and effective learning
experience.
The growing use of large databases for research has led to the design of novel human–technology interfaces that help researchers
visualize and understand patterns in the data that generate new knowledge or lead to new questions. Many of these interfaces now
incorporate multidimensional visualizations, in addition to scatter plots, histograms, or cluster representations (Vincent, Hastings-
Tolsma, & Effken, 2010). Some designers, such as Quinn (the founder of the Design Rhythmics Sonification Research Laboratory at
the University of New Hampshire) and Meeker (2000), use variations in sound to help researchers hear the patterns in large data sets.
In Quinn’s (2000) “climate symphony,” different musical instruments, tones, pitches, and phrases are mapped onto variables, such as
the amounts and relative concentrations of minerals to help researchers detect patterns in ice core data covering more than 110,000
years. Climate patterns take centuries to emerge and can be difficult to detect. The music allows the entire 110,000 years to be
condensed into just a few minutes, making detection of patterns and changes much easier.
The human–technology interface is ubiquitous in health care and takes many forms. A look at the quality of these interfaces
follows. Be warned: It is not always a pretty picture.
The Human–Technology Interface Problem
In The Human Factor, Vicente (2004) cited the many safety problems in health care identified by the Institute of Medicine’s (1999)
report and noted how the technology (defined broadly) used often does not fit well with human characteristics. As a case in point,
Vicente described his own studies of nurses’ PCA pump errors. Nurses made the errors, in large part, because of the complexity of the
user interface, which required as many as 27 steps to program the device. Vicente and his colleagues developed a PCA in which
programming required no more than 12 steps. Nurses who used it in laboratory experiments made fewer errors, programmed drug
delivery faster, and reported lower cognitive workloads compared to the commercial device. Further evidence that human–technology
interfaces do not work as well as they might is evident in the following events.
Doyle (2005) reported that when a bar-coding medication system interfered with their workflow, nurses devised workarounds,
such as removing the armband from the patient and attaching it to the bed, because the bar-code reader failed to interpret bar codes
when the bracelet curved tightly around a small arm. Koppel et al. (2005) reported that a widely used computer-based provider order
entry (CPOE) system meant to decrease medication errors actually facilitated 22 types of errors because the information needed to
order medications was fragmented across as many as 20 screens, available medication dosages differed from those the physicians
expected, and allergy alerts were triggered only after an order was written.
Han et al. (2005) reported increased mortality among children admitted to Children’s Hospital in Pittsburgh after CPOE
implementation. Three reasons were cited for this unexpected outcome. First, CPOE changed the workflow in the emergency room.
Before CPOE, orders were written for critical time-sensitive treatment based on radio communication with the incoming transport
team before the child arrived. After CPOE implementation, orders could not be written until the patient arrived and was registered in
the system (a policy that was later changed). Second, entering an order required as many as 10 clicks and took as long as 2 minutes;
moreover, computer screens sometimes froze or response time was slow. Third, when the team changed its workflow to accommodate
CPOE, face-to-face contact among team members diminished. Despite the problems with study methods identified by some of the
informatics community, there certainly were serious human–technology interface problems.
In 2005, a Washington Post article reported that Cedars-Sinai Medical Center in Los Angeles had shut down a $34 million system
after 3 months because of the medical staff’s rebellion. Reasons for the rebellion included the additional time it took to complete the
structured information forms, failure of the system to recognize misspellings (as nurses had previously done), and intrusive and
interruptive automated alerts (Connolly, 2005). Even though physicians actually responded appropriately to the alerts, modifying or
canceling 35% of the orders that triggered them, designers had not found the right balance of helpful-to-interruptive alerts. The system
simply did not fit the clinicians’ workflow.
Such unintended consequences (Ash, Berg, & Coiera, 2004) or unpredictable outcomes (Aarts, Doorewaard, & Berg, 2004) of
healthcare information systems may be attributed, in part, to a flawed implementation process, but there were clearly also human–
technology interaction issues. That is, the technology was not well matched to the users and the context of care. In the pediatric case,
a system developed for medical–surgical units was implemented in a critical care unit.
Human–technology interface problems are the major cause of as many as 87% of all patient monitoring incidents (Walsh & Beatty,
2002). It is not always that the technology itself is faulty. In fact, the technology may perform flawlessly, but the interface design may
lead the human user to make errors (Vicente, 2004).
Improving the Human–Technology Interface
Much can be learned from the related fields of cognitive engineering, human factors, and ergonomics about how to make interfaces
more compatible with their human users and the context of care. Each of these areas of study is multidisciplinary and integrates
knowledge from multiple disciplines (e.g., computer science, engineering, cognitive engineering, psychology, and sociology). Over the
years, three axioms have evolved for developing effective human–computer interactions (Staggers, 2003): (1) Users must be an
early and continuous focus during interface design; (2) the design process should be iterative, allowing for evaluation and correction of
identified problems; and (3) formal evaluation should take place using rigorous experimental or qualitative methods.
Axiom 1: Users Must Be an Early and Continuous Focus During Interface Design
Rubin (1994) uses the term user-centered design to describe the process of designing products (e.g., human–technology interfaces) so
that users can carry out the tasks needed to achieve their goals with “minimal effort and maximal efficiency” (p. 10). Thus, in user-
centered design, the end user is emphasized.
Vicente (2004) argued that technology should fit human requirements at five levels of analysis (physical, psychological, team,
organizational, and political). Physical characteristics of the technology (e.g., size, shape, or location) should conform to the user’s
size, grasp, and available space). Information should be presented in ways that are consistent with known human psychological
capabilities (e.g., the number of items that can be remembered is seven plus or minus two). In addition, systems should conform to the
communication, workflow, and authority structures of work teams; to organizational factors, such as culture and staffing levels; and
even to political factors, such as budget constraints, laws, or regulations.
A number of analysis tools and techniques have been developed to help designers better understand the task and user environment
for which they are designing. Discussed next are task analysis, cognitive task analysis, and cognitive work analysis (CWA).
Task analysis examines how a task must be accomplished. Generally, analysts describe the task in terms of inputs needed for the
task, outputs (what is achieved by the task), and any constraints on actors’ choices on carrying out the task. Analysts then lay out the
sequence of temporally ordered actions that must be carried out to complete the task in flowcharts (Vicente, 1999). A worker’s tasks
must be analyzed. Task analysis is very useful in defining what users must do and which functions might be distributed between the
user and technology (U.S. Department of Health and Human Services, 2013). Cognitive task analysis usually starts by identifying,
through interviews or questionnaires, the particular task and its typicality and frequency. Analysts then may review the written
materials that describe the job or are used for training and determine, through structured interviews or by observing experts perform
the task, which knowledge is involved and how that knowledge might be represented.
Cognitive work analysis was developed specifically for the analysis of complex, high-technology work domains, such as nuclear
power plants, intensive care units, and emergency departments where workers need considerable flexibility in responding to external
demands (Burns & Hajdukiewicz, 2004; Vicente, 1999). A complete CWA includes five types of analysis: (1) work domain, (2)
control tasks, (3) strategies, (4) social–organizational, and (5) worker competencies. The work domain analysis describes the functions
of the system and identifies the information that users need to accomplish their task goals. The control task analysis investigates the
control structures through which the user interacts with or controls the system. It also identifies which variables and relations among
variables discovered in the work domain analysis are relevant for particular situations so that context-sensitive interfaces can present
the right information (e.g., prompts or alerts) at the right time. The strategies analysis looks at how work is actually done by users to
facilitate the design of appropriate human–computer dialogues. The social–organizational analysis identifies the responsibilities of
various users (e.g., doctors, nurses, clerks, or therapists) so that the system can support collaboration, communication, and a viable
organizational structure. Finally, the worker competencies analysis identifies design constraints related to the users themselves
(Effken, 2002).
Specialized tools are available for the first three types of CWA (Vicente, 1999). Analysts typically borrow tools (e.g., ethnography)
from the social sciences for the two remaining types. Hajdukiewicz, Vicente, Doyle, Milgram, and Burns (2001) used CWA to model
an operating room environment. Effken (2002) and Effken, Loeb, Johnson, Johnson, and Reyna (2001) used CWA to analyze the
information needs for an oxygenation management display for an ICU. Other examples of the application of CWA in health care are
described by Burns and Hajdukiewicz (2004) in their chapter on medical systems (pp. 201–238).
Axiom 2: The Design Process Should Be Iterative, Allowing for Evaluation and Correction of Identified
Problems
Today both principles and techniques for developing human–technology interfaces that people can use with minimal stress and
maximal efficiency are available. An excellent place to start is with Norman’s (1988, pp. 188–189) principles:
1. Use both knowledge in the world and knowledge in the head. In other words, pay attention not only to the environment or to
the user, but to both, and to how they relate. By using both, the problem actually may be simplified.
2. Simplify the structure of tasks. For example, reduce the number of steps or even computer screens needed to accomplish the
goal.
3. Make things visible: Bridge the gulfs of execution and evaluation. Users need to be able to see how to use the technology to
accomplish a goal (e.g., which buttons does one press and in which order to program this PCA?); if they do, then designers
have bridged the gulf of execution. They also need to be able to see the effects of their actions on the technology (e.g., if a
nurse practitioner prescribes a drug to treat a certain condition, the actual patient response may not be perfectly clear). This
bridges the gulf of evaluation.
4. Get the mappings right. Here, the term mapping is used to describe how environmental facts (e.g., the order of light switches
or variables in a physiologic monitoring display) are accurately depicted by the information presentation.
5. Exploit the power of constraints, both natural and artificial. Because of where the eyes are located in the head, humans have to
turn their heads to see what is happening behind them; however, that is not true of all animals. As the location of one’s eyes
constrains what one can see, so also do physical elements, social factors, and even organizational policy constrain the way tasks
are accomplished. By taking these constraints into account when designing technology, it can be made easier for humans use.
6. Design for error. Mistakes happen. Technology should eliminate predictable errors and be sufficiently flexible to allow humans
to identify and recover from unpredictable errors.
7. When all else fails, standardize. To get a feel for this principle, think how difficult it is to change from a Macintosh to a
Windows environment or from the Windows operating system to Vista.
Kirlik and Maruyama (2004) described a real-world human–technology interface that follows Norman’s principles. The authors
observed how a busy expert short-order cook strategically managed to grill many hamburgers at the same time, but each to the
customer’s desired level of doneness. The cook put those burgers that were to be well-done on the back and far right portion of the
grill, those to be medium well-done in the center of the grill, and those to be rare at the front of the grill, but farther to the left. The
cook moved all burgers to the left as grilling proceeded and turned them over during their travel across the grill. Everything the cook
needed to know was available in this simple interface. As a human–technology interface, the grill layout was elegant. The interface
used knowledge housed both in the environment and in the expert cook’s head; also, things were clearly visible, both in the position of
the burgers and in the way they were moved. The process was clearly and effectively standardized, with built-in constraints. What
might it take to create such an intuitive human–technology interface in health care?
Several useful books have been written about effective interface design (e.g., Burns & Hajdukiewicz, 2004; Cooper, 1995; Mandel,
1997). In addition, a growing body of research is exploring new ways to present clinical data that might facilitate clinicians’ problem
identification and accurate treatment (AHRQ, 2010). Just as in other industries, health care is learning that big data can provide big
insights if it can be visualized, accessed, and meaningful (Intel IT Center, 2013). Often designers use graphical objects to show how
variables relate. The first to do so were likely Cole and Stewart (1993), who used changes in the lengths of the sides and area of a four-
sided object to show the relationship of respiratory rate to tidal volume. Other researchers have demonstrated that histograms and
polygon displays are better than numeric displays for detecting changes in patients’ physiologic variables (Gurushanthaiah, Weinger,
& Englund, 1995). When Horn, Popow, and Unterasinger (2001) presented physiologic data via a single circular object with 12 sectors
(where each sector represented a different variable), nurses reported that it was easy to recognize abnormal conditions, but difficult to
comprehend the patient’s overall status. This kind of graphical object approach has been most widely used in anesthesiology, where a
number of researchers have shown improved clinician situational awareness or problem detection time by mapping physiologic
variables onto display objects that have meaningful shapes, such as using a bellows-like object to represent ventilation (Agutter et al.,
2003; Blike, Surgenor, Whallen, & Jensen, 2000; Michels, Gravenstein, & Westenskow, 1997; Zhang et al., 2002).
Effken (2006) compared a prototype display that represented physiologic data in a structured pictorial format with two bar graph
displays. The first bar graph display and the prototype both presented data in the order that experts were observed to use them. The
second bar graph display presented the data in the way that nurses collected them. In an experiment in which resident physicians and
novice nurses used simulated drugs to treat observed oxygenation management problems using each display, residents’ performance
was improved with the displays ordered as experts used them, but nurses’ performance was not improved. Instead, nurses performed
better when the variables were ordered as they were used to collecting them, demonstrating the importance of understanding user roles
and the tasks they need to accomplish.
Data also need to be represented in ways other than visually. Gaver (1993) proposed that because ordinary sounds map onto
familiar events, they could be used as icons to facilitate easier technology navigation and use and to provide continuous background
information about how a system is functioning. In health care, auditory displays have been used to provide clinicians with information
about patients’ vital signs (e.g., in pulse oximetry), such as by altering volume or tone when a significant change occurs (Sanderson,
2006).
Admittedly, auditory displays are probably more useful for quieter areas of the hospital, such as the operating room. Perhaps that is
why researchers have most frequently applied the approach in anesthesiology. For example, Loeb and Fitch (2002) reported that
anesthesiologists detected critical events more quickly when auditory information about heart rate, blood pressure, and respiratory
parameters was added to a visual display. Auditory tones also have been combined as earcons to represent relationships among data
elements, such as the relationship of systolic to diastolic blood pressure (Watson & Gill, 2004).
Axiom 3: Formal Evaluation Should Take Place Using Rigorous Experimental or Qualitative Methods
Perhaps one of the highest accolades that any interface can achieve is to say that it is transparent. An interface becomes transparent
when it is so easy to use that users no longer think about it, but only about the task at hand. For example, a transparent clinical
interface would enable clinicians to focus on patient decisions rather than on how to access or combine patient data from multiple
sources. In Figure 12-1, instead of the nurse interacting with the computer, the nurse and the patient interact through the technology
interface. The more transparent the interface, the easier the interaction should be.
Figure 12-1 Nurse–patient interaction framework in which the technology supports the interaction
Source: Adapted from Staggers, N., & Parks, P. L. (1993). Description and initial applications of the Staggers & Parks nurse–computer interaction
framework. Computers in Nursing, 11, 282–290. Reprinted by permission of AMIA.
Usability is a term that denotes the ease with which people can use an interface to achieve a particular goal. Usability of a new
human–technology interface needs to be evaluated early and often throughout its development. Typical usability indicators include
ease of use, ease of learning, satisfaction with using, efficiency of use, error tolerance, and fit of the system to the task (Staggers,
2003). Some of the more commonly used approaches to usability evaluation are discussed next.
Surveys of Potential or Actual Users
Chernecky, Macklin, and Waller (2006) assessed cancer patients’ preferences for website design. Participants were asked their
preferences for a number of design characteristics, such as display color, menu buttons, text, photo size, icon metaphor, and layout, by
selecting on a computer screen their preferences for each item from two or three options.
Focus Group
Typically used at the very start of the design process, focus groups can help the designer better understand users’ responses to potential
interface designs and to content that might be included in the interface.
Cognitive Walkthrough
In a cognitive walkthrough, evaluators assess a paper mockup, working prototype, or completed interface by observing the steps
users are likely to take to use the interface to accomplish typical tasks. This analysis helps designers determine how understandable
and easy to learn the interface is likely to be for these users and the typical tasks (Wharton, Rieman, Lewis, & Polson, 1994).
Heuristic Evaluation
A heuristic evaluation has become the most popular of what are called “discount usability evaluation” methods. The objective of a
heuristic evaluation is to detect problems early in the design process, when they can be most easily and economically corrected. The
methods are termed “discount” because they typically are easy to do, involve fewer than 10 experts (often experts in relevant fields
such as human–computer technology or cognitive engineering), and are much less expensive than other methods. They are called
“heuristic” because evaluators assess the degree to which the design complies with recognized usability rules of thumb or principles
(the heuristics), such as those proposed by Nielsen (1994) and available on his website
(http://www.useit.com/papers/heuristic/heuristic_list.html).
For example, McDaniel and colleagues (2002) conducted a usability test of an interactive computer-based program to encourage
smoking cessation by low-income women. As part of the initial evaluation, healthcare professionals familiar with the intended users
reviewed the design and layout of the program. The usability test revealed several problems with the decision rules used to tailor
content to users that were corrected before implementation.
Formal Usability Test
Formal usability tests typically use either experimental or observational studies of actual users using the interface to accomplish real-
world tasks. A number of researchers use these methods. For example, Staggers, Kobus, and Brown (2007) conducted a usability study
of a prototype electronic medication administration record. Participants were asked to add, modify, or discontinue medications using
the system. The time they needed to complete the task, their accuracy in the task, and their satisfaction with the prototype were
assessed (the last criterion through a questionnaire). Although satisfaction was high, the evaluation also revealed design flaws that
could be corrected before implementation.
Field Study
In a field study, end users evaluate a prototype in the actual work setting just before its general release. For example, Thompson,
Lozano, and Christakis (2007) evaluated the use of touch-screen computer kiosks containing child health-promoting information in
several low-income, urban community settings through an online questionnaire that could be completed after the kiosk was used. Most
users found the kiosk easy to use and the information it provided easy to understand. Researchers also gained a better understanding of
the characteristics of the likely users (e.g., 26% had never used the Internet and 48% had less than a high school education) and the
information most often accessed (television and media use, and smoke exposure).
Dykes and her colleagues (2006) used a field test to investigate the feasibility of using digital pen and paper technology to record
vital signs as a way to bridge an organization from a paper to an electronic health record. In general, satisfaction with the tool
increased with use, and the devices conformed well to nurses’ workflow. However, 8% of the vital sign entries were recorded
inaccurately because of inaccurate handwriting recognition, entries outside the recording box, or inaccurate data entry (the data entered
were not valid values). The number of modifications needed in the tool and the time that would be required to make those changes
ruled out using the digital pen and paper as a bridging technology.
Ideally, every healthcare setting would have a usability laboratory of its own to test new software and technology in its own setting
before actual implementation. However, this can be expensive, especially for small organizations. Kushniruk and Borycki (2006)
developed a low-cost rapid usability engineering method for creating a portable usability laboratory consisting of videocameras and
other technology that one can take out of the laboratory into hospitals and other locations to test the technology on site using as close
to a real world environment as possible. This is a much more cost-effective and efficient solution and makes it possible to test all
technologies before their implementation.
A Framework for Evaluation
Ammenwerth, Iller, and Mahler (2006) proposed a fit between individuals, tasks, and technology (FITT) model that suggests that each
of these factors be considered in designing and evaluating human–technology interfaces. It is not enough to consider only the user and
technology characteristics; the tasks that the technology supports must be considered as well. The FITT model builds on DeLone and
McLean’s (1992) information success model, Davis’s (1993) technology acceptance model, and Goodhue and Thompson’s (1995) task
technology fit model. A notable strength of the FITT model is that it encourages the evaluator to examine the fit between the various
pairs of components: user and technology, task and technology, and user and task.
Johnson and Turley (2006) compared how doctors and nurses describe patient information and found that doctors emphasized
diagnosis, treatment, and management, whereas the nurses emphasized functional issues. Although both physicians and nurses share
some patient information, how they thought about patients differed. For that reason, an EHR needs to present information (even the
same information) to the two groups in different ways.
Hyun, Johnson, Stetson, and Bakken (2009) used a combination of two models (technology acceptance model and task–technology
fit model) to design and evaluate an electronic documentation system for nurses. To facilitate the design, they employed multiple
methods, including brainstorming of experts, to identify design requirements. To evaluate how well the prototype design fit both task
and user, nurses were asked to carry out specific tasks using the prototype in a laboratory setting, and then complete a questionnaire on
ease of use, usefulness, and fit of the technology with their documentation tasks. Because the researchers engaged nurses at each step
of the design process, the result was a more useful and usable system.
Future of the Human–Technology Interface
Increased attention to improving the human–technology interface through human factors approaches has already led to significant
improvements in one area of health care: anesthesiology. Anesthesia machines that once had hoses that would fit into any delivery port
now have hoses that can only be plugged into the proper port. Anesthesiologists have also been actively working with engineers to
improve the computer interface through which they monitor their patients’ status and are among the leaders in investigating the use of
audio techniques as an alternative way to help anesthesiologists maintain their situational awareness. As a result of these efforts,
anesthesia-related deaths dropped from 2 in 20,000 to 1 in 200,000 in less than 10 years (Vicente, 2004). It is hoped that continued
emphasis on human factors (Vicente, 2004) and user-centered design (Rubin, 1994) by informatics professionals and human–computer
interactions experts will have equally successful effects on other parts of the healthcare system. The increased amount of informatics
research in this area is encouraging, but there is a long way to go.
A systematic review of clinical technology design evaluation studies (Alexander & Staggers, 2009) found 50 nursing studies. Of
those, nearly half (24) evaluated effectiveness, fewer (16) evaluated satisfaction, and still fewer (10) evaluated efficiency. The
evaluations were not systematic. That is, there was no attempt to evaluate the same system in different environments or with different
users. Most evaluations were done in a laboratory, rather than in the setting where the system would be used. The authors argued for a
broader range of studies that use an expanded set of outcome measures. For example, instead of looking at user satisfaction, evaluators
should dig deeper into the design factors that led to the satisfaction or dissatisfaction. In addition, performance measures, such as
diagnostic accuracy, errors, and correct treatment, should be used.
Rackspace, Brauer, and Barth (2013) reported on a social study of the human cloud formed in part by data collected from wearable
technologies; they focused on assessing attitudes and “exploring how cloud computing is enabling this new generation of smart
devices” (p. 2). Today, smartphones, glasses, clothing, watches, cameras, and monitors for health or patient tracking, to name but a
few devices, are available to this purpose.
As our technologies continue to evolve, we are creating more design issues. The proliferation of smart devices and wearable
technology brings new concerns related to human–technology interfaces. According to Madden (2013), wearable technologies are
adding another wrinkle into the design process—namely, human behavior. How will someone use this technology? How will
individuals behave with it on their person? How will they wear it? How and when will they enable and use it? Will others be able to
detect the technologies? That is, will someone be able to wear Google Glass and take pictures or videos of other people’s actions easily
and seamlessly move among all of the capabilities of his or her wearable technologies? The human–technology interface must address
these issues. There is a long way to go.
Summary
There are at least three messages the reader should take away from the discussion in this chapter. First, if there is to be significant
improvement in quality and safety outcomes in the United States through the use of information technology, the designs for human–
technology interfaces must be radically improved so that the technology better fits human and task requirements. However, that
improvement will be possible only if clinicians identify and report problems, rather than simply creating workarounds. That means that
each clinician has a responsibility to participate in the design process and to report designs that do not work.
Second, a number of useful tools are currently available for the analysis, design, and evaluation phases of development life cycles.
They should be used routinely by informatics professionals to ensure that technology better fits both task and user requirements.
Third, focusing on interface improvement using these tools has had a huge impact on patient safety in the area of anesthesiology
and medication administration. With increased attention from informatics professionals and engineers, the same kind of improvement
should be possible in other areas regardless of the technologies actually employed there. In the ideal world, one can envision that every
human–technology interface will be designed to enhance users’ workflow, will be as easy to use as banking ATMs, and will be fully
tested before its implementation in a setting that mirrors the setting where it will be used.
THOUGHT-PROVOKING
QUESTIONS
1. You are a member of a team that has been asked to evaluate a prototype personal digital assistant–based application for calculating drug dosages.
Based on what you know about usability testing, which kind of test (or tests) might you do and why?
2. Is there a human–technology interface that you have encountered that you think needs improvement? If you were to design a replacement, which
analysis techniques would you choose? Why?
3. Which type of functionality and interoperability would you want from your smartphone, watch, clothing, glasses, camera, and monitor? Provide
a detailed response.
REFERENCES
Aarts, J., Doorewaard, H., & Berg, M. (2004). Understanding implementation: The case of a computerized physician order entry system in a large Dutch
university medical center. Journal of the American Medical Association, 11, 207–216.
Agency for Healthcare Research and Quality (AHRQ). (2010). Improving data collection across the health care system.
http://www.ahrq.gov/research/findings/final-reports/iomracereport/reldata5.html
Agutter, J., Drews, F., Syroid, N., Westneskow, D., Albert, R., Strayer, … Weinger, M. (2003). Evaluation of graphic cardiovascular display in a high-
fidelity simulator. Anesthesia and Analgesia, 97, 1403–1413.
Alexander, G., & Staggers, N. (2009). A systematic review of the designs of clinical technology findings and recommendations for future research.
Advances in Nursing Science, 32(3), 252–279.
Ammenwerth, E., Iller, C., & Mahler, C. (2006). IT-adoption and the interaction of task, technology and individuals: A fit framework and a case study.
BMC Medical Informatics and Decision Making, 6, 3.
Ash, J. S., Berg, M., & Coiera, E. (2004). Some unintended consequences of information technology in health care: The nature of patient care
information system-related errors. Journal of the American Medical Informatics Association, 11, 104–112.
Blike, G. T., Surgenor, S. D., Whallen, K., & Jensen, J. (2000). Specific elements of a new hemodynamics display improves the performance of
anesthesiologists. Journal of Clinical Monitoring & Computing, 16, 485–491.
Burns, C. M., & Hajdukiewicz, J. R. (2004). Ecological interface design. Boca Raton, FL: CRC Press.
Chernecky, C., Macklin, D., & Waller, J. (2006). Internet design preferences of patients with cancer. Oncology Nursing Forum, 33, 787–792.
Cole, W. G., & Stewart, J. G. (1993). Metaphor graphics to support integrated decision making with respiratory data. International Journal of Clinical
Monitoring and Computing, 10, 91–100.
Connolly, C. (2005, March 21). Cedars-Sinai doctors cling to pen and paper. Washington Post, p. A01.
Cooper, A. (1995). About face: Essentials of window interface design. New York, NY: Hungry Minds, Inc.
Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal
of Man–Machine Studies, 38, 475–487.
DeLone, W. H., & McLean, E. (1992). Information systems success: The question for the dependent variable. Information Systems Research, 3(1), 60–
95.
Doyle, M. (2005). Impact of the Bar Code Medication Administration (BCMA) system on medication administration errors. Unpublished doctoral
dissertation, University of Arizona, Tucson, AZ.
Dykes, P. C., Benoit, A., Chang, F., Gallagher, J., Li, Q., Spurr, C., … Prater, M. (2006). The feasibility of digital pen and paper technology for vital
sign data capture in acute care settings. In AMIA 2006 symposium proceedings (pp. 229–233). Washington, DC: American Medical Informatics
Association.
Effken, J. A. (2002). Different lenses, improved outcomes: A new approach to the analysis and design of healthcare information systems. International
Journal of Medical Informatics, 65, 59–74.
Effken, J. A. (2006). Improving clinical decision making through ecological interfaces. Ecological Psychology, 18(4), 283–318.
Effken, J., Loeb, R., Johnson, K., Johnson, S., & Reyna, V. (2001). Using cognitive work analysis to design clinical displays. In V. L. Patel, R. Rogers,
& R. Haux (Eds.), Proceedings of MedInfo-2001 (pp. 27–31). London, UK: IOS Press.
Gaver, W. W. (1993). What in the world do we hear? An ecological approach to auditory event perception. Ecological Psychology, 5, 1–30.
Goodhue, D. L., & Thompson, R. L. (1995). Task–technology fit and individual performance. MIS Quarterly, 19(2), 213–236.
Gurushanthaiah, K. I., Weinger, M. B., & Englund, C. E. (1995). Visual display format affects the ability of anesthesiologists to detect acute physiologic
changes: A laboratory study employing a clinical display simulator. Anesthesiology, 83, 1184–1193.
Hajdukiewicz, J. R., Vicente, K. J., Doyle, D. J., Milgram, P., & Burns, C. M. (2001). Modeling a medical environment: An ontology for integrated
medical informatics design. International Journal of Medical Informatics, 62, 79–99.
Han, Y. Y., Carcillo, J. A., Venkataraman, S. T., Clark, R. S. B., Watson, R. S., Nguyen, T., … Orr, R. (2005). Unexpected increased mortality after
implementation of a commercially sold computerized physician order entry system. Pediatrics, 116, 1506–1512.
Horn, W., Popow, C., & Unterasinger, L. (2001). Support for fast comprehension of ICU data: Visualization using metaphor graphics. Methods in
Informatics Medicine, 40, 421–424.
Hyun, S., Johnson, S. B., Stetson, P. D., & Bakken, S. (2009). Development and evaluation of nursing user interface screens using multiple methods.
Journal of Biomedical Informatics, 42(6), 1004–1012.
Institute of Medicine. (1999). To err is human: Building a safer health system. Washington, DC: Author.
Intel IT Center. (2013). Big data visualization: Turning big data into big insights.
http://www.intel.com/content/dam/www/public/us/en/documents/white-papers/big-data-visualizationturning-big-data-into-big-insights
Johnson, C. M., & Turley, J. P. (2006). The significance of cognitive modeling in building healthcare interfaces. International Journal of Medical
Informatics, 75(2), 163–172.
Johnson, C. W. (2006). Why did that happen? Exploring the proliferation of barely usable software in healthcare systems. Quality & Safety in
Healthcare, 15(suppl 1), 176–181.
Kirlik, A., & Maruyama, S. (2004). Human–technology interaction and music perception and performance: Toward the robust design of sociotechnical
systems. Proceedings of the IEEE, 92(4), 616–631.
Koppel, R., Metlay, J. P., Cohen, A., Abaluck, B., Localio, A. R., Kimmel, S. E., & Stron, B. (2005). Role of computerized physician order entry
systems in facilitating medication errors. Journal of the American Medical Association, 293(10), 1197–1203.
Kushniruk, A. W., & Borycki, E. M. (2006). Low-cost rapid usability engineering: Designing and customizing usable healthcare information systems.
Healthcare Quarterly (Toronto, Ont.), 9(40), 98–100, 102.
Loeb, R. G., & Fitch, W. T. (2002). A laboratory evaluation of an auditory display designed to enhance intraoperative monitoring. Anesthesia and
Analgesia, 94, 362–368.
Madden, S. (2013). With wearable tech like Google Glass, human behavior is now a design problem. http://gigaom.com/2013/06/15/with-wearable-tech-
like-google-glass-humanbehavior-is-now-a-design-problem/
Mandel, T. (1997). The elements of user interface design. New York, NY: John Wiley & Sons.
McDaniel, A., Hutchinson, S., Casper, G. R., Ford, R. T., Stratton, R., & Rembush, M. (2002). Usability testing and outcomes of an interactive computer
program to promote smoking cessation in low income women. In Proceedings AMIA 2002 (pp. 509–513). Washington, DC: American Medical
Informatics Association.
Michels, P., Gravenstein, D., & Westenskow, D. R. (1997). An integrated graphic data display improves detection and identification of critical events
during anesthesia. Journal of Clinical Monitoring & Computing, 13, 249–259.
Nielsen, J.(1994). Heuristic evaluation. In J. Nielsen & R. L. Mack (Eds.), Usability inspection methods (pp. 25–62). New York, NY: John Wiley &
Sons.
Norman, D. A. (1988). The psychology of everyday things. New York, NY: Basic Books.
Quinn, M. (2000). The climate symphony: Rhythmic techniques applied to the sonification of ice core data. http://www.bcca.org/ief/dquin00c.htm
Quinn, M., & Meeker, L. (2000). Research set to music: The climate symphony and other sonifications of ice core, radar, DNA, seismic and solar wind
data. http://www.drsrl.com/climate_paper.html
Rackspace, Brauer, C. & Barth, J. (2013). The human cloud: Wearable technology from novelty to productivity. A social study into the impact of
wearable technology. http://sloanreview.mit.edu/article/managing-the-human-cloud/
Rubin, J. (1994). Handbook of usability testing: How to plan, design, and conduct effective tests. New York, NY: Wiley & Sons.
Sanderson, P. (2006). The multimodal world of medical monitoring displays. Applied Ergonomics, 37, 501–512.
Staggers, N. (2003). Human factors: Imperative concepts for information systems in critical care. AACN Clinical Issues, 14(3), 310–319.
Staggers, N., Kobus, D., & Brown, C. (2007). Nurses’ evaluations of a novel design for an electronic medication administration record. CIN: Computers,
Informatics, Nursing, 25(2), 67–75.
Thompson, D. A., Lozano, P., & Christakis, D. A. (2007). Parent use of touchscreen computer kiosks for child health promotion in community settings.
Pediatrics, 119(3), 427–434.
U.S. Department of Health and Human Services. (2013). Usability.gov: Task analysis. http://www.usability.gov/how-to-and-tools/methods/task-
analysis.html
Vicente, K. J. (1999). Cognitive work analysis: Toward safe, productive, and healthy computer-based work. Mahwah, NJ: Lawrence Erlbaum
Associates.
Vicente, K. (2004). The human factor. New York, NY: Routledge.
Vincent, D., Hastings-Tolsma, M., & Effken, J. (2010). Data visualization and large nursing datasets. Online Journal of Nursing Informatics, 14(2).
http://ojni.org/14_2/Vincent
Walsh, T., & Beatty, P. C. W. (2002). Human factor error and patient monitoring. Physiological Measurement, 23, R111–R132.
Watson, G., & Gill, T. (2004). Earcon for intermittent information in monitoring environments. In Proceedings of the 2004 Conference of the
Computer–Human Interaction Special Interest Group of the Human Factors and Ergonomics Society of Australia (OzCHI2004), Wollonggong, New
South Wales, November 22–24, 1994.
Wharton, C., Rieman, J., Lewis, C., & Polson, P. (1994). The cognitive walkthrough: A practitioner’s guide. In J. Nielsen & R. L. Mack (Eds.), Usability
inspection methods (pp. 105–139). New York, NY: John Wiley & Sons.
Zhang, Y., Drews, F. A., Westenskow, D. R., Foresti, S., Agutter, J., Burmedez, J. C., … Loeb, R. (2002). Effects of integrated graphical displays on
situation awareness in anaesthesiology. Cognition, Technology & Work, 4, 82–90.
Chapter 13
Electronic Security
Lisa Reeves Bertin, Dee McGonigle, and Kathleen Mastrian
OBJECTIVES
1. Describe processes for securing electronic information in a computer network.
2. Identify various methods of user authentication and relate authentication to security of a network.
3. Explain methods to anticipate and prevent typical threats to network security.
Key Terms
Antivirus software
Authentication
Biometrics
Confidentiality
Firewall
Flash drive
Hacker
Integrity
Intrusion detection devices
Intrusion detection system
Jump drive
Malicious code
Malicious insider
Malware
Mask
Network
Network accessibility
Network availability
Network security
Password
Proxy server
Radio frequency identity chip
Secure information
Security breach
Shoulder surfing
Social engineering
Spyware
Thumb drive
Trojan horse
Virus
Worm
Introduction
In addition to complying with federal HIPAA guidelines regarding the privacy of patient information, healthcare systems need to be
vigilant in the way that they secure information and manage network security. Mowry and Oakes (n.d.) discuss the vulnerability of
electronic health records to data breaches. They suggest that as many as 77 persons could view a patient’s record during a hospital
stay. It is critical for information technology (IT) policies and procedures to ensure appropriate access by clinicians and to protect
private information from inappropriate access. However, authentication procedures can be cumbersome and time consuming, thus
reducing clinician performance efficiency.
Physicians spend on average 7 minutes per patient encounter, with nearly 2 minutes of that time being devoted to managing logins
and application navigation. Likewise, an average major healthcare provider must deal with more than 150 applications—most
requiring different user names and passwords—making it difficult for caregivers to navigate and receive contextual information.
Healthcare organizations must strike the right balance, in terms of simplifying access to core clinical data sets while maximizing the
Instead, they simply track which websites are popular and are used to develop advertising and marketing strategies. Spyware that does
steal user IDs and passwords contains malicious code that is normally hidden in a seemingly innocent file download. This threat to
security explains why healthcare organizations typically do not allow employees to download files. The rule of thumb to protect the
network and one’s own computer system is to download only files from a reputable site that provides complete contact information.
Organizations may also use such devices as firewalls (covered in the next section) and intrusion detection devices to protect from
hackers.
Another huge threat to corporate security is social engineering, or the manipulation of a relationship based on one’s position in an
organization. For example, someone attempting to access a network might pretend to be an employee from the corporate IT office,
who simply asks for an employee’s digital ID and password. The outsider can then gain access to the corporate network. Once this
access has been obtained, all corporate information is at risk. A second example of social engineering is a hacker impersonating a
federal government agent. After talking an employee into revealing network information, the hacker has an open door to enter the
corporate network.
The number one security threat to a corporate network is the malicious insider. This person can be a disgruntled employee or a
recently fired employee whose rights of access to the corporate network have not yet been removed. In the case of a recently fired
employee, his or her network access should be suspended immediately upon notice of termination. To avoid the potentially hazardous
issues created by malicious insiders, healthcare organizations need some type of policy to monitor employee activity to ensure that
employees carry out only those duties that are part of their normal job. Separation of privileges is a common security tool; no one
employee should be able to complete a task that could cause a critical event without the knowledge of another employee. For example,
the employee who processes the checks and prints them should not be the same person who signs those checks. Similarly, the
employee who alters pay rates and hours worked should be required to submit a weekly report to a supervisor before the changes take
effect. Software that can track and monitor employee activity is also available. This software can log which files an employee
accesses, whether changes were made to files, and whether the files were copied. Depending on the number of employees,
organizations may also employ a full-time electronic auditor who does nothing but monitor activity logs.
Security Tools
A wide range of tools are available to an organization to protect the organizational network and information. These tools can be either
a software solution, such as antivirus software, or a hardware tool, such as a proxy server. Such tools are effective only if they are
used along with employee awareness training.
For example, e-mail scanning is a commonly used software tool. All incoming e-mail messages are scanned to ensure they do not
contain a virus or some other malicious code. This software can find only viruses that are currently known, so it is important that the
virus software be set to search for and download updates automatically. Organizations can further protect themselves by training
employees to never open an e-mail attachment unless they are expecting the attachment and know the sender. Even IT managers have
fallen victim to e-mail viruses and sent infected e-mails to everyone in their address book. Employees should be taught to protect their
organization from new viruses that may not yet be included in their scanning software by never opening an e-mail attachment unless
the sender is known and the attachment is expected. E-mail scanning software and antivirus software should never be turned off, and
updates should be installed at least weekly—or, ideally, daily. Software is also available to scan instant messages and to delete
automatically any spam e-mail.
Many antivirus and adware software packages are available for fees ranging from free to more than $25 per month. The main
factors to consider when purchasing antivirus software are its effectiveness (i.e., the number of viruses it has missed), the ease of
installation and use, the effectiveness of the updates, and the help and user support available. Numerous websites compare and contrast
the most recent antivirus software packages. Be aware, however, that some of these sites also sell antivirus software, so they may
present biased information.
Firewalls are another tool used by organizations to protect their corporate networks when they are attached to the Internet. A
firewall can be either hardware or software or a combination of both that examines all incoming messages or traffic to the network.
The firewall can be set up to allow only messages from known senders into the corporate network. It can also be set up to look at
outgoing information from the corporate network. If the message contains some type of corporate secret, the firewall may prevent the
message from leaving. In essence, firewalls serve as electronic security guards at the gate of the corporate network. Box 13-1 contains
links to short videos explaining Internet security and firewalls.
Proxy servers also protect the organizational network. Proxy servers prevent users from directly accessing the Internet. Instead,
users must first request passage from the proxy server. The server looks at the request and makes sure the request is from a legitimate
user and that the destination of the request is permissible. For example, organizations can block requests to view a website with the
word “sex” in the title or the actual uniform resource locator of a known pornography site. The proxy server can also lend the
requesting user a mask to use while he or she is surfing the Web. In this way, the corporation protects the identity of its employees.
The proxy server keeps track of which employees are using which masks and directs the traffic appropriately.
With hacking becoming more common, healthcare organizations must have some type of protection to avoid this invasion.
Intrusion detection systems (both hardware and software) allow an organization to monitor who is using the network and which files
that user has accessed. Detection systems can be set up to monitor a single computer or an entire network. Corporations must diligently
monitor for unauthorized access of their networks. Anytime someone uses a secured network, a digital footprint of all of the user’s
travels is left, and this path can be easily tracked by electronic auditing software. The Research Brief provides an overview of the
HIMSS 2012 Security Survey results that report on progress made by institutions in securing private information.
BOX 13-1 SOURCES OF INFORMATION ON INTERNET SECURITY AND FIREWALLS
Check out YouTube for the following videos on the Internet and firewalls:
Warriors on the Net (Full Version)”: http://www.youtube.com/watch?v=RhvKm0RdUY0
“What Is a firewall?”: http://www.youtube.com/watch?v=0_EVfWpL6L4
“Firewalls: An Introduction”: http://www.youtube.com/watch?v=kIAu7mvjBUU&feature=related
Research Brief
Overview of HIMSS 2012 Security Survey
In the 5th Annual Security Survey, participants included 303 persons who had some responsibility for information security in their organization.
Roles and titles varied among respondents, such as chief information officer (CIO), senior IT executive, chief security officer (CSO), or chief
technology officer, but all reported responsibility for privacy and information security. Key survey results from the executive summary are
reproduced here:
Maturity of Environment: Respondents characterized their environment as being at a middle rate of maturity, with an average score of 4.64 on a
scale of 1 to 7, where 1 is “not at all mature” and 7 is “highly mature.”
Security Budget: Approximately half of respondents reported that their organization spends 3% or less of its IT budget on information security.
However, while this was consistent with what was reported in the previous year, many respondents indicated that their budget actually increased
in the past year.
Formal Security Position: Nearly 2/3 of the hospital-based respondents reported that they had either a chief security officer (CSO), chief
information security officer (CSIO) or other full-time person in charge of the security of patient data. Less than 1% of respondents reported that
this function was outsourced.
Employee/Patient Data Access: Healthcare organizations are increasingly making patient data available to patients, surrogates, and designated
others.
Security in a Networked Environment: Respondents working for hospitals were much more likely than their counterparts at physician practices to
report that they shared information with other healthcare organizations. Future use of health information exchanges was expected to grow among
all survey respondents.
Use of Security Technologies: While use of tools like firewalls and user access controls has remained widespread in the past several years, growth
of tools such as biometric technologies and public key infrastructure has remained limited. Growth of electronic signature tools has been
substantial in the past five years.
Medical Identity Theft: The number of organizations reporting a case of medical identity theft in the past five years has decreased, from 20
percent in 2008 to 11 percent in 2012. Hospitals (18 percent) were more likely to report an instance of medical identity theft than were physician
practices (six percent).
Results continue to demonstrate that physician practices are not as advanced in many of the areas for security data, when compared to hospitals.
Respondents working for physician practices are less likely to have formal processes in place to monitor their security environment. This includes
performing regular risk analyses, validating and testing their data breach response plan and auditing their IT security plan. Respondents working for
physician practices were also less likely than their peers to report the use of a full complement of data security tools such as wireless security
protocols and single signon (SSO). Despite this, physician practices were less likely than their hospital counterparts to experience cases of medical
identity theft or security breaches. Fewer incidents may be attributed to the fact that physician practices typically manage fewer patients than do
hospitals and also have fewer staff, which has been identified as a key threat to secure patient information (HIMSS, 2012, p. 23).
Source: Permission for use of the data is granted in the publication. Individuals are encouraged to cite this report and any accompanying graphics in
printed matter, publications, or any other medium, as long as the information is attributed to the 5th Annual HIMSS Security Survey, sponsored by
Experian.
Off-Site Use of Portable Devices
Off-site uses of portable devices, such as laptops, personal data assistants (PDAs), home computing systems, smartphones, smart
devices, and portable data storage devices, can help to streamline the delivery of health care. For example, home health nurses may
need to access electronic protected health information (EPHI) via a wireless laptop connection during a home visit, or a physician
might use a PDA to get specific patient information related to a prescription refill in response to a patient request. These mobile
devices are invaluable to healthcare efficiency and responsiveness to patient need in such cases. At the very least, however, agencies
should require data encryption when EPHI is being transmitted over unsecured networks or transported on a mobile device as a way of
protecting sensitive information. Hotspots provided by companies, such as McDonald’s restaurants, and by airports are not secured
networks. Virtual private networks must be used to ensure that all data transmitted on unsecured networks are encrypted. The user
must log into the virtual private network to reach the organization’s network.
Only data essential for the job should be maintained on the mobile device; other nonclinical information, such as Social Security
numbers, should never be carried outside the secure network. Some institutions make use of thin clients, which are basic interface
portals that do not keep secure information stored on them. Essentially, users must log in to the network to get the data they need.
Use of thin clients may be problematic in patient care situations where the user cannot access the network easily. For example, some
rural areas of the United States do not have wireless coverage. In these instances, private health information may need to be stored in a
clinician’s laptop or PDA. This is comparable to home health nurses carrying paper charts in their cars to make home visits, and it
entails the same responsibilities accompanying such use of private information outside the institution’s walls.
What happens if one of these devices is lost or stolen? The agency is ultimately responsible for the integrity of the data contained
on these devices and is required by HIPAA regulations (U.S. Department of Health and Human Services, 2006) to have policies in
place covering such items as appropriate remote use, removal of devices from their usual physical location, and protection of these
devices from loss or theft. Simple rules, such as covering laptops left in a car and locking car doors during transport of mobile devices
containing EPHI, can help to deter theft. If a device is lost or stolen, the agency must have clear procedures in place to help ensure that
sensitive data are not released or used inappropriately. Software packages that provide for physical tracking of the static and mobile
computer inventory including laptops and PDAs are being used more widely and can assist in the recovery of lost or stolen devices. In
addition, some software that allows for remote data deletion in the event of theft or loss of a mobile device can be invaluable to the
agency in preventing the release of EPHI.
If a member of an agency is caught accessing EPHI inappropriately or steals a mobile device, the sanctions should be swift and
public. Sanctions may range from a warning or suspension with retraining to termination or prosecution depending on the severity of
the security breach. The sanctions must send a clear message to all that protecting EPHI is serious business.
The U.S. Department of Health and Human Services (2006) has identified potential risks and proposed risk management strategies
for accessing, storing, and transmitting EPHI. Visit the following website for detailed tabular information (pp. 4–6) on potential risks
and risk management strategies: http://its.syr.edu/infosec/docs/standards/remoteaccess-standard
To protect our patients and their data, nurses must consider the impact of wireless mobile devices. Data can be stolen by an
employee very easily through the use of e-mail or file transfers. Malware that infiltrates a network can collect easily accessible data.
One of the evolving issues is lost or stolen devices that can provide a gateway into a healthcare organization’s network and records.
When the device is owned by the employee, other issues arise as to how the device is used and secured.
The increase in cloud computing has also challenged our personal and professional security and privacy. According to Jansen and
Grance (2011), cloud computing “promises to have farreaching effects on the systems and networks of federal agencies and other
organizations. Many of the features that make cloud computing attractive, however, can also be at odds with traditional security
models and controls” (p. vi).
Summary
Technology changes so quickly that even the most diligent user will likely encounter a situation that could constitute a threat to his or
her network. Organizations must provide their users with the proper training to help them avoid known threats and—more importantly
—be able to discern a possible new threat. Consider that 10 years ago wireless networks were the exception to the rule, where today
access to wireless networks is almost taken for granted. How will computer networks be accessed 10 years from now? The most
important concept to remember from this chapter is that the only completely safe network is one that is turned off. Network
accessibility and network availability are necessary evils that pose security risks. The information must be available to be accessed,
yet remain secured from hackers, unauthorized users, and any other potential security breaches. As the cloud expands, so do the
concerns over security and privacy. In an ideal world, everyone would understand the potential threats to network security and would
diligently monitor and implement tools to prevent unauthorized access of their networks, data, and information.
THOUGHT-PROVOKING
QUESTIONS
1. Sue is a chronic obstructive pulmonary disorder clinic nurse enrolled in a master’s education program. She is interested in writing a paper on the
factors that are associated with poor compliance with medical regimens and associated repeat hospitalization of chronic obstructive pulmonary
disorder patients. She downloads patient information from the clinic database to a thumb drive that she later accesses on her home computer. Sue
understands rules about privacy of information and believes that because she is a nurse and needs this information for a graduate school
assignment, she is entitled to the information. Is Sue correct in her thinking? Describe why she is or is not correct.
2. The nursing education department of a large hospital system has been centralized; as a consequence, the nurse educators are no longer assigned
to one hospital but must now travel among all of the hospitals. They use their smartphones to interact and share data and information. What are
the first steps you would take to secure these transactions? Describe why each step is necessary.
3. Research cloud computing in relation to health care. What are the major security and privacy challenges? Please choose three and describe them
in detail.
References
Degaspari, J. (2010). Staying ahead of the curve on data security. Healthcare Informatics, 27(10), 32–36. http://www.healthcare-
informatics.com/ME2/dirmod.asp?
sid=9B6FFC446FF7486981EA3C0C3CCE4943&nm=Articles%2FNews&type=Publishing&mod=Publications%3A%3AArticle&mid=8F3A7027421841978F18BE895F87F791&tier=4&id=35F1496AE0B144D3A9716D5D9C2D03CF
Gross, G. (2003). Human error causes most security breaches. InfoWorld.
http://www.infoworld.com/article/03/03/18/HNhumanerror_1.html
HIMSS. (2012). 5th annual HIMSS security survey. http://himss.files.cms-
plus.com/HIMSSorg/content/files/2012_HIMSS_SecuritySurvey
Jansen, W., & Grance, T. (2011). National Institute of Standards and Technology (NIST): Guidelines on security and privacy in
public cloud computing. https://cloudsecurityalliance.org/wp-content/uploads/2011/07/NIST-Draft-SP-800-144_cloud-computing
Mowry, M., & Oakes, R. (n.d.). Not too tight, not too loose. Healthcare Informatics, Healthcare IT Leadership, Vision & Strategy.
http://www.healthcare-informatics.com/ME2/dirmod.asp?
nm=&type=Publishing&mod=Publications%3A%3AArticle&mid=8F3A7027421841978F18BE895F87F791&tier=4&id=B7823E299AC64041AC3F253CE19DF298
Sullivan, T. (2012). Government health IT: DHS lists top 5 mobile medical device security risks.
http://www.govhealthit.com/news/dhs-lists-top-5-mobile-device-security-risks
U.S. Department of Health and Human Services. (2006). HIPAA security guidance. Retrieved from Security Guidance for Remote
Use website: http://its.syr.edu/infosec/docs/standards/remoteaccess-standard
Chapter 14
Nursing Informatics: Improving Workflow and Meaningful
Use
Denise Hammel-Jones, Dee McGonigle, and Kathleen Mastrian
OBJECTIVES
1. Provide an overview of the purpose of conducting workflow analysis and design.
2. Deliver specific instructions on workflow analysis and redesign techniques.
3. Cite measures of efficiency and effectiveness that can be applied to redesign efforts.
4. Explore meaningful use from the nursing perspective.
Key Terms
American Recovery and Reinvestment Act (ARRA)
Bar-code medication administration (BCMA)
Clinical transformation Computerized provider order entry (CPOE)
Electronic health record (EHR)
Events
Health information exchange (HIE)
Health information technology (HIT)
Information systems Interactions
Lean
Meaningful use (MU) Medical home models Metrics
Process analysis
Process owners
Quality
Six Sigma
Tasks
Work process
Workflow
Workflow analysis
Introduction
The healthcare environment has grown more complex and continues to evolve every day. Unfortunately, the complexities that help
clinicians to deliver better care and improve patient outcomes also take a toll on the clinicians themselves. This toll is exemplified
through hours spent learning new technology, loss in productivity as the user adjusts and adapts to new technology, and unintended
workflow consequences from the use of technology.
Despite the perceived negative downstream effects to end users and patients as a result of technology, this very same technology
can improve efficiency and yield a leaner healthcare environment. The intent of this chapter is to outline the driving forces that create
the need to redesign workflow as well as to elucidate what the nurse needs to know about how to conduct workflow redesign, measure
the impact of workflow changes, and assess the impact of meaningful use.
Workflow Analysis Purpose
According to the American Association for Justice (2013), “Preventable medical errors kill and seriously injure hundreds of thousands
of Americans every year” (para. 1). Not only is there an impact on patients from these errors, but there is also a significant financial
impact on healthcare organizations. Clearly, we must minimize these errors, and one of the most important tools for this purpose is the
use of electronic records and information systems to provide point-of-care decision support and automation. The key point is that
many of these errors are preventable and we must find ways to prevent them.
Technology can provide a mechanism to improve care delivery and create a safer patient environment, provided it is implemented
appropriately and considers the surrounding workflow. In an important article by Campbell, Guappone, Sittig, Dykstra, and Ash
(2009), the authors suggested that technology implemented without consideration of workflow can provide greater patient safety
concerns than no technology at all. Computerized provider order entry (CPOE) causes us to focus more specifically on workflow
considerations. These workflow implications are referred to as the unintended consequences of CPOE implementation; they are just
some of the effects of poorly implemented technology. The Healthcare Information Management Systems Society (HIMSS, 2010)
ME-PI Toolkit addresses workflow redesign and considers why it is so critical to successful technology implementations.
Technology is recognized to have a potentially positive effect on patient outcomes. Nevertheless, even with the promise of
improving how care is delivered, adoption of technology has been slow. The cost of technology solutions such as CPOE, bar-code
medication administration (BCMA), and electronic health records (EHR) remain staggeringly high. The cost of technology
coupled with the lengthy timelines required to develop and implement such technology has put this endeavor out of reach for many
healthcare organizations. In addition, upgrades or enhancements to the technology are often necessary either mid-implementation or
shortly after a launch, leaving little time to focus efforts on the optimization of the technology within the current workflow.
Furthermore, the existence of technology does not in itself guarantee that it will be used in a manner that promotes better outcomes for
patients.
Given the sluggish adoption of technology, in 2009 the U.S. government took an unprecedented step when it formally recognized
the importance of health information technology (HIT) for patient care outcomes. As a result of the provisions of American
Recovery and Reinvestment Act (ARRA), healthcare organizations can qualify for financial incentives based on the level of
meaningful use achieved. Meaningful use (MU) refers to the rules and regulations established by the ARRA. The three stages of
meaningful use are part of an EHR incentive program. During stage 1, the focus is on data capturing and sharing (Centers for Medicare
& Medicaid Services [CMS], 2013a, 2013b). Stage 2 focuses on advanced clinical processes, and stage 3 seeks improved outcomes
(CMS, 2013b). Stage 1 was initiated during 2011–2012, stage 2 will begin in 2014, and stage 3 will be launched in 2016 (Sherman,
2013). Follow developments related to meaningful use at this site: http://www.cms.gov/Regulations-and-
Guidance/Legislation/EHRIncentivePrograms/Meaningful_Use.html.
For an organization that seeks to use the 25 meaningful use measures to qualify for the incentives, the data to support these
measures must be gathered and reported on electronically—necessitating the use of technology in all patient care areas. Additionally, a
fundamental aspect of meaningful use is the assurance that a significant number of healthcare providers have adopted technology.
Nurses and healthcare professionals who use EHRs in their practice setting, for example, will collect higher-quality data. Many of the
quality reporting measures rely on nursing documentation. Those healthcare personnel who do not use EHRs should soon see their
organizations moving in that direction. Meaningful use measures will push healthcare organizations to reexamine the use of clinical
technologies within their organization and approach implementations in a new way.
Not only is there a potential for patient safety and quality issues to arise from technology implementations that do not address
workflow, but a financial impact to the organization is possible as well. All organizations, regardless of their industry, must operate
efficiently to maintain profits and continue to provide services to their customers. For hospitals, which normally have significantly
smaller profit margins than other organizations, the need to maintain efficient and effective care is essential for survival. Given that
hospital profit margins are diminishing, never has there been a more crucial time to examine the costs of errors and poorly designed
workflows and the financial burden they present to an organization, than now. Moreover, what are the costs to an organization that
fails to address the integration of technology? This is an area where few supporting data exist to substantiate the claim that technology
without workflow considerations can, in fact, impact the bottom line.
Today, many healthcare organizations are experiencing the effects of poorly implemented clinical technology solutions. These
effects may be manifested in the form of redundant documentation, non–value-added steps, and additional time spent at the computer
rather than in direct care delivery. A recent study by the University of Maryland indicated that nurses spend the equivalent of 1 FTE
time per year at a computer instead of in providing direct patient care. Technology ought not to be implemented for the sake of
automation unless it promises to deliver gains in patient outcomes and proper workflow. In fact, the cost to organizations for
duplicate/redundant documentation by nursing can range from $6,500 to $13,000 per nurse, per year (Clancy, Delaney, Morrison, &
Gunn, 2006).
Examining the workflow surrounding the use of technology enables better use of the technology and more efficient work. It also
promotes safer patient care delivery. The need to focus on workflow and technology is attracting increasing recognition, although there
remains a dearth of literature that addresses the importance of this area. As more organizations work to achieve a level of technology
adoption that will enable them to receive ARRA financial incentives, we will likely see more attention paid to the area of workflow
design and, therefore, a greater body of research and evidence (AHRQ, n.d., Qualis Health, 2011; Yuan, Finley, Long, Mills, &
Johnson, 2013).
Workflow and Technology
Workflow is a term used to describe the action or execution of a series of tasks in a prescribed sequence. Another definition of
workflow is a progression of steps (tasks, events, interactions) that constitute a work process, involve two or more persons, and
create or add value to the organization’s activities. In a sequential workflow, each step depends on the occurrence of the previous step;
in a parallel workflow, two or more steps can occur concurrently. The term workflow is sometimes used interchangeably with process
or process flows, particularly in the context of implementations. Observation and documentation of workflow to better understand
what is happening in the current environment and how it can be altered is referred to as process or workflow analysis. A typical output
of workflow analysis is a visual depiction of the process, called a process map. The process map ranges from simplistic to fairly
complex and provides an excellent tool to identify specific steps. It also can provide a vehicle for communication and a tool upon
which to build educational materials as well as policies and procedures.
One school of thought suggests that technology should be designed to meet the needs of clinical workflow (Yuan et al., 2013). This
model implies that system analysts have a high degree of control over screen layout and data capture. It also implies that technology is
malleable enough to allow for the flexibility to adapt to a variety of workflow scenarios. Lessons learned from more than three decades
of clinical technology implementations suggest that clinical technologies still have a long way to go on the road to maturity to allow
this to be possible. The second and probably most prevalent thought process is that workflow should be adapted to the use of
technology. Today, this is by far the most commonly used model given the progress of clinical technology.
A concept that has gained popularity in recent years relative to workflow redesign is clinical transformation. Clinical
transformation is the complete alteration of the clinical environment and, therefore, this term should be used cautiously to describe
redesign efforts. Earl, Sampler, and Sghort (1995) define transformation as “a radical change approach that produces a more
responsive organization that is more capable of performing in unstable and changing environments that organizations continue to be
faced with” (p. 31). Many workflow redesign efforts are focused on relatively small changes and not the widespread change that
accompanies transformational activities. Moreover, clinical transformation would imply that the manner in which work is carried out
and the outcomes achieved are completely different from the prior state—which is not always true when the change simply involves
implementing technology. Technology can be used to launch or in conjunction with a clinical transformation initiative, although the
implementation of technology alone is not perceived as transformational.
Before undertaking transformative initiatives, the following guidelines should be understood:
Leadership must take the lead and create a case for transformation.
Establish a vision for the end point.
Allow those persons with specific expertise to provide the details.
Think about the most optimal experience for both the patient and the clinician.
Do not replicate the current state.
Focus on those initiatives that offer the greatest value to the organization.
Recognize that small gains have no real impact on transformation.
Optimization
Most of what has been and will be discussed in this chapter is related to workflow analysis in conjunction with technology
implementations. Nevertheless, not all workflow analysis and redesign occurs prior to the implementation of technology. Some
analysis and redesign efforts may occur weeks, months, or even years following the implementation. When workflow analysis occurs
postimplementation, it is often referred to as optimization. Optimization is the process of moving conditions past their current state and
into more efficient and effective method of performing tasks. Merriam-Webster Online Dictionary (2010) considers optimization to be
the act, process, or methodology of making something (as a design, system, or decision) as fully perfect, functional, or effective as
possible. Some organizations will routinely engage in optimization efforts following an implementation, whereas other organizations
may undertake this activity in response to clinician concerns or marked change in operational performance.
Furthermore, workflow analysis can be conducted either as a stand-alone effort or as part of an operational improvement event.
When the process is addressed alone, the effort is termed process improvement. Nursing informatics professionals should always be
included in these activities to represent the needs of clinicians and to serve as a liaison for technological solutions to process problems.
Additionally, informaticists will likely become increasingly operationally focused and will need to transform their role accordingly to
address workflow in an overall capacity as well as respective to technology. As mentioned earlier, hospitals tend to operate with
smaller profit margins than other industries and these profits will likely continue to diminish, forcing organizations to work smarter,
not harder—and to use technology to accomplish this goal.
If optimization efforts are undertaken, the need to revisit workflow design should not be considered a flaw in the implementation
approach. Even a well-designed future-state workflow during a technology implementation must be reexamined postimplementation to
ensure that what was projected about the future state remains valid and to incorporate any additional workflow elements into the
process redesign.
Exploring the topic of workflow analysis with regard to clinical technology implementation will yield considerably fewer literature
results than searching for other topical areas of implementation. More research is needed in the area of the financial implications of
workflow inefficiencies and their impact on patient care. Time studies require an investment of resources and may be subject to patient
privacy issues as well as the challenges of capturing time measurements on processes that are not exactly replicable. Another
confounding factor affecting the quality and quantity of workflow research is the lack of standardized terminology for this area. A
comprehensive literature search was conducted and published through the Agency for Healthcare Quality and Research (AHRQ) in
2008 as an evidence-based handbook for nurses; this literature search yielded findings indicating that a lack of standardized
terminology in the area of workflow and publications on this topic have made it a difficult topic to support through research findings.
What all organizations ultimately strive for is efficient and effective delivery of patient care. The terms efficient and effective are
widely known in quality areas or Six Sigma and Lean departments, but are not necessarily known or used in informatics. Effective
delivery of care or workflow suggests that the process or end product is in the most desirable state. An efficient delivery of care or
workflow would mean that little waste—that is, unnecessary motion, transportation, overprocessing, or defects—was incurred. Health
systems such as Virginia Mason University Medical Center, among others, have experienced significant quality and cost gains from
the widespread implementation of Lean development throughout their organization.
CASE STUDY
In my experience consulting, I have seen several examples of organizations that engage in the printing of paper reports that replicate information that
has been entered and is available with the electronic health record. These reports are often reviewed, signed, and acted on, instead of using the
electronic information. Despite the knowledge that the information contained in these reports was outdated the moment the report was printed and
that the very nature of using the report for workflow is an inefficient practice, this method of clinical workflow remains prevalent in many hospitals
across the United States.
There is an underlying fear that drives the decisions to mold a paper-based workflow around clinical technology. There is also a lack of the
appropriate amount of integration that would otherwise allow this information to be available in an electronic form.
Workflow Analysis and Informatics Practice
The American Nurses Association (ANA), in Nursing Informatics: Scope and Standards of Practice (2008), defines nine functional
areas of practice for the informatics nurse specialist (INS). The functional area of analysis identifies the specific functional qualities
related to workflow analysis. Particularly, Nursing Informatics: Scope and Standards of Practice indicates that the INS should develop
techniques necessary to assess and improve human–computer interaction. Workflow analysis, however, is not relevant solely to
analysis, but rather is part of every functional area the INS engages in. The functional areas covered by consultants, researchers, and
other areas need to understand workflow and appreciate how lack of efficient workflow affects patient care.
A critical aspect of the informatics role is workflow design. Nursing informatics is uniquely positioned to engage in the analysis
and redesign of processes and tasks surrounding the use of technology. The ANA cites workflow redesign as one of the fundamental
skills sets that make up the discipline of this specialty. Moreover, workflow analysis should be part of every technology
implementation, and the role of the informaticist within this team is to direct others in the execution of this task or to perform the task
directly.
Unfortunately, many nurses find themselves in an informatics capacity without sufficient preparation for a process analysis role.
One area of practice that is particularly susceptible to inadequate preparation is the ability to facilitate process analysis. Workflow
analysis requires careful attention to detail and the ability to moderate group discussions, organize concepts, and generate solutions.
These skills can be acquired through a formal academic informatics program or through courses that teach the discipline of Six Sigma
or Lean, by example. Regardless of where these skills are acquired, it is important to understand that they are now and will continue to
remain a vital aspect of the informatics role.
Some organizations have felt strongly enough about the need for workflow analysis that departments have been created to address
this very need. Whether the department carries the name of clinical excellence, organizational effectiveness, or Six Sigma/Lean, it is
critical to recognize the value this group can offer technology implementations and clinicians.
As we examine how workflow analysis is conducted, note that while the nursing informaticist is an essential member of the team to
participate in or enable workflow analysis, a team dedicated to this effort is necessary for its success.
Building the Design Team
The workflow redesign team is an interdisciplinary team consisting of “process owners.” Process owners are those persons who
directly engage in the workflow to be analyzed and redesigned. These individuals can speak about the intricacy of process, including
process variations from the norm. When constructing the team, it is important to include individuals who are able to contribute
information about the exact current-state workflow and offer suggestions for future-state improvement. Members of the workflow
redesign team should also have the authority to make decisions about how the process should be redesigned. This authority is
sometimes issued by managers, or it could come from participation of the managers directly. Such a careful blend of decision makers
and “process owners” can be difficult to assemble but is critical for forming the team and enabling them for success. Often, individuals
at the manager level will want to participate exclusively in the redesign process. While having management participate provides the
advantage of having decision makers and management-level buy-in, these individuals may also make erroneous assumptions about
how the process should be versus how the process is truly occurring. Conversely, including only process owners who do not possess
the authority to make decisions can slow down the work of the team while decisions are made outside the group sessions.
Team focus needs to be addressed at the outset of the team’s assembly. Early on, the team should decide which workflow will be
examined to avoid confusion or spending time unnecessarily on workflow that does not ultimately matter to the outcome. In the early
stages of workflow redesign, the team should define the beginning and end of a process and a few high-level steps of the process.
Avoid focusing on process steps in great detail in the beginning, as the conversation can get sidetracked or team members may get
bogged down by focusing on details and not move along at a good pace. Six Sigma expert George Eckes uses the phrase “Stay as high
as you can as long as you can”—a good catch phrase to remember to keep the team focused and at a high level. The pace at which any
implementation team progresses ultimately affects the overall timeline of a project; therefore, focus and speed are skills that the
informatics expert should develop and use throughout every initiative, but particularly when addressing workflow redesign.
The workflow redesign team will develop a detailed process map after agreement is reached on the current-state process’s
beginning and end points, and a high-level map depicting the major process steps is finalized. Because workflow crosses many
different care providers, it may be useful to construct the process map using a swimlane technique (Figure 14-1). A swim-lane
technique uses categories such as functional workgroups and roles to visually depict groups of work and to indicate who performs the
work. The resulting map shows how workflow and data transition to clinicians and can demonstrate areas of potential process and
information breakdowns.
It may take several sessions of analysis to complete a process map, as details are uncovered and workarounds discussed. There is a
tendency for individuals who participate in process redesign sessions to describe workflow as they believe it to be occurring, rather
than not how it really is. The informatics expert and/or the process team facilitator should determine what is really happening,
however, and capture that information accurately. Regardless of whether a swim-lane or simplistic process map design is used, the
goal is to capture enough details to accurately portray the process as it is happening today.
Other techniques (aside from process mapping) may be used to help the team understand the workflow as it exists in the current
state. The future-state workflow planning will be only as good as the reliability of the current state; thus it is crucial to undertake
whatever other actions are needed to better understand what is happening in the current state. Observation, interviews, and process or
waste walks are also helpful in understanding the current state.
Value Added Versus Non–Value Added
Beyond analysis of tasks, current-state mapping provides the opportunity for the process redesign team to distinguish between value-
added and non–value-added activities. A value-added activity or step is one that ultimately brings the process closer to completion or
changes the product or service for the better. An example of a value-added step would be placing a name tag on a specimen sample.
The name tag is necessary for the laboratory personnel to identify the specimen and, therefore, its placement is an essential or value-
added step in the process. Some steps in a process do not necessarily add value but are necessary for regulatory or compliance reasons.
These steps are still considered necessary and need to be included in the future process. A non–value-added step, in contrast, does not
alter the outcome of a process or product. Activities such as handling, moving, and holding are not considered value-added steps and
should be evaluated during workflow analysis. Manipulating papers, moving through computer screens, and walking or transporting
items are all considered non–value-added activities.
The five whys represent one technique to drive the team toward identifying value-added versus non–value-added steps. The
process redesign facilitator will query the group about why a specific task is done or done in a particular way through a series of
questions asking “why?” The goal is to uncover tasks that came about due to workarounds or for other unsubstantiated reasons. Tasks
that are considered non–value added and are not necessary for the purpose of compliance or regulatory reasons should be eliminated
from the future-state process. The team’s purpose in redesigning workflow is to eliminate steps in a process that do not add value to
the end state or that create waste by their very nature.
Figure 14-1
Source: Greencastle Associates Consulting and Atlantic Health. Reprinted by permission.
Waste
A key underpinning of the Lean philosophy is the removal of waste activities from workflow. Waste is classified as unnecessary
activities or an excess of products to perform tasks. The seven categories listed here are the mostly widely recognized forms of waste:
Overproduction: pace is faster than necessary to support the process
Waiting
Transport
Inappropriate processing: overprocessing
Unnecessary inventory: excess stock
Unnecessary motion: bending, lifting, moving, and so on
Defects: reproduction
Variation
The nature of the work situation for the nurse is one of frequent interruptions causing the workflow to be disrupted and increasing the
chance of error (Yuan et al., 2013). Variation in workflow is considered the enemy of all good processes and, therefore, should be
eliminated when possible. Variation occurs when workers perform the same function in different ways. It usually arises because of
flaws in the way a process was originally designed, lack of knowledge about the process, or inability to execute a process as originally
designed due to disruption or disturbances in the workflow. Examining the process as it exists today will help with identifying
variation. A brief statement about variation that cannot be eliminated: Processes that involve highly customized products or services
are generally not conducive to standardization and the elimination of variation inherent to the process.
Some argue that delivery of care is subject to variation owing to its very nature and the individual needs of patients. There is little
doubt that each patient’s care should be tailored to meet his or her specific needs. Nevertheless, delivery of care involves some
common processes that can be standardized and improved upon without jeopardizing care.
Transitioning to the Future State
Following redesign efforts, regardless of whether they occurred during or after an implementation or as a stand-alone process
improvement event, steps must be taken to ensure that change takes hold and the new workflow continues after the support team has
disbanded. Management support and involvement during the transition phase is essential, as management will be necessary to enforce
new workflow procedures and further define/refine roles and responsibilities. Documentation of the future-state workflow should have
occurred during the redesign effort but is not completely finished until after the redesign is complete and the workflow has become
operational. Policies and procedures are addressed and rewritten to encompass the changes to workflows and role assignments. Help
desk, system analyst, nursing education, and other support personnel need to be educated about the workflow specifics as part of the
postimprovement effort. It is considered good practice to involve the operational staff in the future process discussions and planning so
as to incorporate specifics of these areas and ensure the buy-in of the staff.
When workflow changes begin to fail and workarounds develop, they signal that something is flawed about the way in which the
new process was constructed and needs to be evaluated further. The workflow redesign team is then brought together to review and, if
necessary, redesign the process.
The future state is constructed with the best possible knowledge of how the process will ideally work. To move from the current
state to the future state, gap analysis is necessary. Gap analysis zeros in on the major areas most affected by the change—namely,
technology. What often happens in redesign efforts is an exact or near-exact replication of the current state using automation. The gap
analysis discussion should generate ideas from the group about how best to utilize the technology to transform practice. A prudent step
is to consider having legal and risk representatives at the table when initiating future-state discussions to identify the parameters within
which the group should work; nevertheless, the group should not assume the existing parameters are its only boundaries.
Future-state process maps become the basis of educational materials for end users, communication tools for the project team, and
the foundation of new policies and procedures. Simplified process maps provide an excellent schematic for communicating change to
others.
Informatics as a Change Agent
Technology implementations represent a significant change for clinicians, as does the workflow redesign that accompanies adoption of
technology. Often the degree of change and its impact are underappreciated and unaccounted for by leadership and staff alike. A
typical response to change is anger, frustration, and a refusal to accept the proposed change. All of these responses should be expected
and need to be accounted for; thus a plan to address the emotional side of change is developed early on. Every workflow redesign
effort should begin with a change management plan. Engagement of the end user is a critical aspect of change management and,
therefore, technology adoption. Without end-user involvement, change is resisted and efforts are subject to failure. Users may be
engaged and brought into the prospective change through question-and-answer forums, technology demonstrations, and frequent
communications regarding change, and as department-specific representatives in working meetings.
Many change theories have been developed. No matter which change theory is adopted by the informatics specialist, however,
communication, planning, and support are key factors in any change management strategy. Informaticists should become
knowledgeable about at least one change theory and use this knowledge as the basis for change management planning as part of every
effort. John Kotter (1996), one of the most widely recognized change theorists, suggested the following conditions must be addressed
to deal with change in an organization:
Education and communication
Participation and involvement
Facilitation and support
Negotiation and agreement
Manipulation and co-optation
Explicit and implicit coercion
In the HIMSS (2009) Nursing Informatics Impact survey, nursing informaticists were identified as the most significant resource in
a project team that influences adoption and change management. Nurses bring to such teams their ability to interact with various
clinicians, their knowledge of clinical practice, and their ability to empathize with the clinicians as they experience the impact of
workflow change. These innate skills differentiate the nursing informaticist from other members of the implementation team and are
highly desirable in the informatics community.
Nevertheless, no matter which change management techniques are employed by the informatics specialist and the project team,
adoption of technology and workflow may be slow to evolve. Change is often a slow process that requires continual positive
reinforcement and involvement of supporting resources. Failure to achieve strong adoption results early on is not necessarily a failure
of the methods utilized, but rather may be due to other factors not entirely within the control of the informaticist.
Perhaps a complete alteration in behavior is not possible, but modifications to behaviors needed to support a desired outcome can
be realized. This situation is analogous to the individual who stops smoking; the desire for the cigarette remains, but the behavior has
been modified to no longer sustain smoking. To manage change in an organization, nurses must modify behavior to produce the
intended outcome.
Change takes hold when strong leadership support exists. This support manifests itself as a visible presence to staff, clear and
concise communications, an unwavering position, and an open door policy to field concerns about change. Too often, leadership gives
verbal endorsement of change and then fails to follow through with these actions or withdraws support when the going gets tough.
Inevitability, if leadership wavers, so too will staff.
Measuring the Results
Metrics provide understanding about the performance of a process or function. Typically within clinical technology projects, we
identify and collect specific metrics about the performance of the technology or metrics that capture the level of participation or
adoption. Equally important is the need for process performance metrics. Process metrics are collected at the initial stage of project or
problem identification. Current-state metrics are then benchmarked against internal indicators. When there are no internal indicators to
benchmark against, a suitable course of action is to benchmark against an external source such as a similar business practice within a
different industry. Consider examining the hotel room change-over strategy or the customer service approach of Walt Disney
Company or Ritz Carlton hotels, for example, to determine suitable metrics for a particular project or focus area.
TABLE 14-1 EXAMPLES OF METRICS
Turnaround times
Change-over time
Patient satisfaction
Cycle times
Set-up time
Employee satisfaction
Throughput
System availability
The right workflow complement will provide the organization with the data it needs to understand operational and clinical
performance. This area is highlighted through the need for healthcare organizations to capture meaningful use measures. Good metrics
should tell the story of accomplishment. The presence of technology alone does not guarantee an organization’s ability to capture and
report on these measures without also addressing the surrounding workflow. Metrics should focus on the variables of time, quality, and
costs. Table 14-1 provides examples of relevant metrics.
The ARRA highlights the need for healthcare organizations to collect information that represents the impact of technology on
patient outcomes. Furthermore, data are necessary to demonstrate how a process is performing in its current state. In spite of the
ARRA mandates, the need to collect data to demonstrate improvement in workflow—though it remains strong—is all too often absent
in implementation or redesign efforts. A team cannot demonstrate improvements to an existing process without collecting information
about how the process is performing today. Current-state measures also help the process team validate that the correct area for
improvement was identified. Once a process improvement effort is over and the new solution has been implemented, postimprovement
measures should be gathered to demonstrate progress.
In some organizations, the informatics professional reports to the director of operations, the chief information officer, or the chief
operations officer. In this relationship, the need to demonstrate operational measures is even stronger. Operational measures such as
turnaround times, throughput, and equipment or technology availability are some of the measures captured.
Future Directions
Workflow analysis is not an optional part of clinical implementations, but rather a necessity for safe patient care supported by
technology. The ultimate goal of workflow analysis is not to “pave the cow path,” but rather to create a future-state solution that
maximizes the use of technology and eliminates non–value-added activities. Although many tools to accomplish workflow redesign
are available, the best method is the one that complements the organization and supports the work of clinicians. Redesigning how
people do work will evidentially create change; thus the nursing informaticist will need to apply change management principles for the
new way of doing things to take hold.
Workflow analysis has been described in this chapter within the context of the most widely accepted tools that are fundamentally
linked to the concepts of Six Sigma/Lean. Other methods of workflow analysis exist and may become commonly used to assess
clinical workflow. An example of an alternative workflow analysis tool is the use of radio frequency badges to detect movement
within a defined clinical area. Clinician and patient movements may be tracked using these devices, and corresponding actions may be
documented, painting a picture of the workflow for a specific area (Vankipuram, 2010).
Another example of a workflow analysis tool involves the use of modeling software. An application such as ProModel provides
images of the clinical work area where clinician workflows can be plotted out and reconfigured to best suit the needs of a specific area.
Simulation applications enable decision makers to visualize realistic scenarios and draw conclusions about how to leverage resources,
implement technology, and improve performance. Other vendors that offer simulation applications include Maya and Autodesk.
Healthcare organizations need to consider how other industries have analyzed and addressed workflow to their streamline business
practices and improve quality outputs to glean best practices that might be incorporated into the healthcare industry’s own clinical and
business approaches. First, however, each healthcare organization must step outside itself and recognize that not all aspects of patient
care are unique; consequently, many aspects of care can be subjected to standardization. Many models of workflow redesign from
manufacturing and the service sector can be extrapolated to health care. The healthcare industry is facing difficult economic times and
can benefit from performance improvement strategies used in other industries.
Although workflow analysis principles have been described within the context of acute and ambulatory care in this chapter, the
need to perform process analysis on a macro level will expand as more organizations move forward with health information
exchanges (HIE) and medical home models. Health information exchanges require the nursing informaticist to visualize how patients
move through the entire continuum of care and not just a specific patient care area.
Technology initiatives will become increasingly complex in the future. In turn, nursing informaticists will need greater preparation
in the area of process analysis and improvement techniques to meet the growing challenges that technology brings and the operational
performance demands of fiscally impaired healthcare organizations.
Summary
Meaningful use (MU) reflects the rules and regulations arising from ARRA. Nursing must lead the charge in this area, because nurses
play an important part in organizations’ ability to meet the MU criteria based on nursing documentation. EHR adoptions “represent a
small step rather than a giant leap forward” (Murphy, 2013, para. 1). Workflows integrating technology provide the healthcare
professional with the data necessary to make informed decisions. This quality data must be collected and captured to meet MU criteria.
Nurses must be involved in “meaningful data collection and reporting. Documentation by nurses can tell what’s going on with the
patient beyond physical exams, test results, and procedures” (Daley, 2013, para. 5).
Workflow redesign is a critical aspect of technology implementation. When done well, it yields technology that is more likely to
achieve the intended patient outcomes and safety benefits. Nursing informatics professionals are taking on a greater role with respect
to workflow design, and this aspect of practice will grow in light of MU-driven objectives. Other initiatives that impact hospital
performance will also drive informatics professionals to influence how technology is used in the context of workflow to improve the
bottom line for their organizations. In an ideal world, nurse informaticists who are experts at workflow analysis will be core members
of every technology implementation team.
THOUGHT-PROVOKING
QUESTIONS
1. What do you perceive as the current obstacles to redesigning workflow within your clinical settings?
2. Thinking about your last implementation, were you able to challenge the policies and practices that constitute today’s workflow or were you able
to create a workflow solution that eliminated non–value-added steps?
3. Is the workflow surrounding technology usage providing the healthcare organization with the data it needs to make decisions and eventually
meet meaningful use criteria?
4. How does the current educational preparation need to change to address the skills necessary to perform workflow analysis and redesign clinical
processes?
References
Agency for Healthcare Research and Quality (AHRQ). (2008). Patient safety and quality: An evidence-based handbook for nurses.
http://www.ahrq.gov/qual/nurseshdbk/
Agency for Healthcare Research and Quality (AHRQ). (n.d.). Workflow assessment for health IT toolkit. http://healthit.ahrq.gov/health-it-tools-and-
resources/workflowassessment-health-it-toolkit
American Association for Justice. (2013). Preventable medical errors: The sixth biggest killer in America.
http://www.justice.org/cps/rde/justice/hs.xsl/8677.htm
American Nurses Association (2008). Nursing informatics: Scope and standards of practice. Silver Springs, MD: Author.
Campbell, E., Guappone, K., Sittig, D., Dykstra, R., & Ash, J. (2009). Computerized provider order entry adoption: Implications for clinical workflow.
Journal of General Internal Medicine, 24(1), 21–26.
Centers for Medicare & Medicaid Services (CMS). (2013a). CMS.gov: Meaningful use. http://www.cms.gov/Regulations-and-
Guidance/Legislation/EHRIncentivePrograms/Meaningful_Use.html
Centers for Medicare & Medicaid Services (CMS). (2013b). What is meaningful use?
http://www.cms.gov/eHealth/downloads/Webinar_eHealth_July2_IntroEHRProgram
Clancy, T., Delaney, C., Morrison, B., & Gunn, J. (2006). The benefits of standardized nursing languages in complex adaptive systems such as hospitals.
Journal of Nursing Administration, 36(9), 426–434.
Daley, K. (2013). Making HIT meaningful for nursing and patients. http://www.theamericannurse.org/index.php/2011/08/01/making-hit-meaningful-for-
nursing-and-patients/
Earl, M., Sampler, J., & Sghort, J. (1995). Strategies for business process reengineering: Evidence from field studies. Journal of Management
Information Systems, 12(1), 31–56.
Healthcare Information Management Systems Society (HIMSS). (2009). Nursing informatics impact study.
http://www.himss.org/files/HIMSSorg/content/files/HIMSS2009NursingInformaticsImpactSurveyFullResults
Healthcare Information Management Systems Society (HIMSS). (2010). ME-PI toolkit: Process management, workflow & mapping: Tools, tips and
case studies to support the understanding, optimizing and monitoring of processes. Retrieved November 2010 from
http://www.himss.org/ASP/topics_FocusDynamic.asp?faid=322
Kotter, J. P. (1996). Leading change (pp. 33–147). Cambridge, MA: Harvard Business Press.
Merriam-Webster Online Dictionary. (2010). Optimization. http://www.merriam-webster.com/dictionary/optimization
Murphy, K. (2013). Nursing approach to meaningful use, EHR adoption: CIO series. http://ehrintelligence.com/2013/02/21/nursing-approach-to-
meaningful-use-ehr-adoption-cioseries/
Qualis Health. (2011). Workflow analysis. http://www.qualishealthmedicare.org/healthcare-providers/improvement-fundamentals/workflow-analysis
Sherman, R. (2013). What nurse leaders need to know about meaningful use. http://www.emergingrnleader.com/nurseleaderdevelopment-2/
Vankipuram, K. (2010). Toward automated workflow analysis and visulization in clinical environment. Journal of Biomedical Informatics.
doi:10.1016/jbi.2010.05.015
Yuan, M., Finley, G., Long, J., Mills, C. & Johnson, R. (2013). Evaluation of user interface and workflow design of a bedside nursing clinical decision
support system. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3628119/
Section IV
Nursing Informatics Practice Applications: Care Delivery
Chapter 15 The Electronic Health Record and Clinical Informatics
Chapter 16 Informatics Tools to Promote Patient Safety and Quality Outcomes
Chapter 17 Supporting Consumer Information and Education Needs
Chapter 18 Using Informatics to Promote Community/Population Health
Chapter 19 Telenursing and Remote Access Telehealth
Nursing information systems must support nurses as they fulfill their roles in delivering quality patient care. Such systems must be
responsive to nurses’ needs, allowing them to manage their data and information as needed and providing access to necessary
references, literature sources, and other networked departments. Nurses have always practiced in a field where they have needed to use
their ingenuity, resourcefulness, creativity, initiative, and skills. To improve patient care and advance the science of nursing, clinicians
as knowledge workers must apply these same abilities and skills to become astute users of available information systems.
In this section, the reader learns about clinical practice tools, electronic health records, and clinical information systems;
informatics tools to enhance patient safety, provide consumer information, and meet education needs; population and community
health tools; and telehealth and telenursing. Information systems, electronic documentation, and electronic health records are changing
the way nurses and physicians practice. Nursing informatics systems are also changing how patients enter and receive data and
information. Some institutions, for example, are permitting patients to access their own records electronically via the Internet or a
dedicated patient portal. Confidentiality and privacy issues loom with these new electronic systems. HIPAA regulations (covered in
the Perspectives on Nursing Informatics section) and professional ethics principles (covered in the Building Blocks of Nursing
Informatics section) must remain at the forefront when clinicians interact electronically with intimate patient data and information.
The material within this book is placed within the context of the Foundation of Knowledge model (Figure IV-1) to meet the needs
of healthcare delivery systems, organizations, patients, and nurses. Readers should continue to assess where they are in this model. The
Foundation of Knowledge model reflects the fact that knowledge is powerful; for that reason, nurses must focus on information as a
key resource. This section addresses the information systems with which clinicians interact in their healthcare environments as affected
by legislation, professional codes of ethics, consumerism, and reconceptualization of practice paradigms, such as in telenursing. All of
the various nursing roles—practice, administration, education, research, and informatics—involve the science of nursing.
Figure IV-1 Foundation of Knowledge Model
Source: Designed by Alicia Mastrian
Chapter 15
The electronic Health Record and clinical informatics
Emily B. Barey, Kathleen Mastrian, and Dee McGonigle
OBJECTIVES
1. Describe the common components of an electronic health record.
2. Assess the benefits of implementing an electronic health record.
3. Explore the ownership of an electronic health record.
4. Evaluate the flexibility of the electronic health record in meeting the needs of clinicians and patients.
Key Terms
Administrative processes
American Recovery and Reinvestment Act of 2009 (ARRA)
Connectivity
Decision support
Electronic communication
Electronic health record
Health information
Health Information Technology for Economic and Clinical Health Act of 2009 (HITECH)
Interoperability
Meaningful use
Order entry management
Patient support
Population health management
Reporting
Results management
Introduction
The significance of electronic health records (EHRs) to nursing cannot be underestimated. Although EHRs on the surface suggest a
simple automation of clinical documentation, in fact their implications are broad, ranging from the ways in which care is delivered, to
the types of interactions nurses have with patients in conjunction with the use of technology, to the research surrounding EHRs that
will inform nursing practice for tomorrow. A basic knowledge of EHRs and nursing informatics is now considered by many to be an
entry-level nursing competency.
At the Technology Informatics Guiding Education Reform (TIGER, 2006) summit on evidence and informatics transforming
nursing, participants stated that “the nation is working full-speed to realize the 10-year goal of Electronic Health Records for its
citizens” (p. 1). Nurses must become active participants in this effort to capture healthcare information, generate knowledge, and
enhance patient care. “This is a critical juncture for nurses, who comprise 55% of the healthcare workforce, number more than 3
million, and who must become more aware and involved at every level of the Informatics Revolution” (p. 1). Although EHR standards
are evolving and barriers to adoption remain, the collective work has a positive momentum that will benefit clinicians and patients
alike.
This drive to adopt EHRs has been underscored with the passage of the Health Information Technology for Economic and
Clinical Health Act of 2009 (HITECH). It is essential that this competency be developed if nurses are to participate fully in the
changing world of healthcare information technology.
This chapter has four goals. First, it describes the common components of an EHR. Second, it reviews the benefits of
implementing an EHR. Third, it provides an overview of successful ownership of an EHR, including nursing’s role in promoting the
safe adoption of EHRs in day-to-day practice. Fourth, it discusses the flexibility of an EHR in meeting the needs of both clinicians and
patients, including an introduction to interoperability.
Setting the Stage
The U.S. healthcare system faces the enormous challenge of improving the quality of care while simultaneously controlling costs.
EHRs have been proposed as one solution to achieve this goal (Institute of Medicine [IOM], 2001). In January 2004, President George
W. Bush raised the profile of EHRs in his State of the Union address by outlining a plan to ensure that most Americans have an EHR
by 2014. He stated that “by computerizing health records we can avoid dangerous medical mistakes, reduce costs, and improve care”
(Bush, 2004). This proclamation generated an increased demand for understanding EHRs and promoting their adoption, but relatively
few healthcare organizations were motivated to pursue more rapid adoption of EHRs. The Healthcare Information and Management
Systems Society (HIMSS) has been tracking EHR adoption since 2005 through its “Stage 7” award and reports that most
U.S. healthcare organizations (77%) are in Stage 3, reflecting only implementation of the basic components of laboratory,
radiology, and pharmacy ancillaries; a clinical data repository, including a controlled medical vocabulary; and simple nursing
documentation and clinical decision support (HIMSS, 2013). Higher stages of the electronic medical record adoption model include
more sophisticated use of decision support tools and medication administration tools, with Stage 7—the highest level—consisting of
EHRs that have data sharing and warehousing capabilities and that are completely interfaced with emergency and outpatient facilities
(HIMSS Analytics, 2013).
In President Barack Obama’s first term in office, Congress passed the American Recovery and Reinvestment Act of 2009
(ARRA). This legislation included the HITECH Act, which specifically sought to incentivize health organizations and providers to
become meaningful users of EHRs. These incentives will come in the form of increased reimbursement rates from the Centers for
Medicare and Medicaid Services (CMS); ultimately, the HITECH Act will result in payment of a penalty by any healthcare
organization that has not adopted EHRs by January 2015. The final rule was published by the Department of Health and Human
Services (HHS) in July 2010 for the first phase of implementation. Stage 1 meaningful use criteria focus on data capture and sharing
(DHHS, 2010). Stage 2 criteria, which are slated for implementation by 2014, advance several clinical processes and promote health
information exchange (HIE) and more patient control over personal data. Stage 3, which has a target implementation date of 2016,
focuses on improved outcomes for individuals and populations, and introduction of patient self-management tools (HealthIT.gov,
2013).
Components of Electronic Health Records
Overview
Before enactment of the ARRA, several variants of EHRs existed, each with its own terminology and each developed with a different
audience in mind. The sources of these records included, for example, the federal government (Certification Commission for
Healthcare Information Technology, 2007), the IOM (2003), the HIMSS (2007), and the National Institutes of Health (2006; Robert
Wood Johnson Foundation [RWJF], 2006). Under ARRA, there is now an explicit requirement for providers and hospitals to use a
certified EHR that meets a set of standard functional definitions to be eligible for the increased reimbursement incentive. HHS has
granted two organizations the authority to accredit EHRs: the Drummond Group and the Certification Commission for Healthcare
Information Technology. These bodies are authorized to test and certify EHR vendors against the standards and test procedures
developed by the National Institute of Standards and Technology (NIST) and endorsed by the Office of the National Coordinator for
Health Information Technology for EHRs.
The NIST test procedure includes 45 certification criteria, ranging from the basic ability to record patient demographics, document
vital signs, and maintain an up-todate problem list, to more complex functions, such as electronic exchange of clinical information and
patient summary records (Jansen & Grance, 2011; NIST, 2010). Box 15-1 lists the 45 certification criteria outlined by NIST.
Despite the points articulated in the ARRA, the IOM definition also remains a valid reference point. This definition is useful
because it has distilled all the possible features of an EHR into eight essential components with an emphasis on functions that promote
patient safety—a universal denominator that everyone in health care can accept. The eight components are (1) health information and
data, (2) results management, (3) order entry management, (4) decision support, (5) electronic communication and connectivity, (6)
patient support, (7) administrative processes, and (8) reporting and population health management (IOM, 2003). Each of these
components is described in more detail here. With the exception of EHR infrastructure functions, such as security and privacy
management, controlled medical vocabularies, and interoperability standards, the 45 NIST standards easily map into the IOM
categories (Jansen & Grance, 2011).
BOX 15-1 EHR CERTIFICATION CRITERIA
Criteria # Certification Criteria
§170.302 (a) Drug–drug, drug–allergy interaction checks
§170.302 (b) Drug formulary checks
§170.302 (c) Maintain up-to-date problem list
§170.302 (d) Maintain active medication list
§170.302 (e) Maintain active medication allergy list
§170.302 (f)(1) Vital signs
§170.302 (f)(2) Calculate body mass index
§170.302 (f)(3) Plot and display growth charts
§170.302 (g) Smoking status
§170.302 (h) Incorporate laboratory test results
§170.302 (i) Generate patient lists
§170.302 (j) Medication reconciliation
§170.302 (k) Submission to immunization registries
§170.302 (l) Public health surveillance
§170.302 (m) Patient-specific education resources
§170.302 (n) Automated measure calculation
§170.302 (o) Access control
§170.302 (p) Emergency access
§170.302 (q) Automatic log-off
§170.302 (r) Audit log
§170.302 (s) Integrity
§170.302 (t) Authentication
§170.302 (u) General encryption
§170.302 (v) Encryption when exchanging electronic health information
§170.302 (w) Accounting of disclosures (optional)
§170.304 (a) Computerized provider order entry
§170.304 (b) Electronic prescribing
§170.304 (c) Record demographics
§170.304 (d) Patient reminders
§170.304 (e) Clinical decision support
§170.304 (f) Electronic copy of health information
§170.304 (g) Timely access
§170.304 (h) Clinical summaries
§170.304 (i) Exchange clinical information and patient summary record
§170.304 (j) Calculate and submit clinical quality measures
§170.306 (a) Computerized provider order entry
§170.306 (b) Record demographics
§170.306 (c) Clinical decision support
§170.306 (d)(1) Electronic copy of health information
§170.306 (d)(2) Electronic copy of health information
Note: For discharge summary
§170.306 (e) Electronic copy of discharge instructions
§170.306 (f) Exchange clinical information and patient summary record
§170.306 (g) Reportable lab results
§170.306 (h) Advance directives
§170.306 (i) Calculate and submit clinical quality measures
Data from National Institute of Standards and Technology (NIST). (2010). Meaningful use test measures: Approved test procedures. Retrieved
October 2010 from http://healthcare.nist.gov/use_testing/finalized_requirements.html
Health Information and Data
Health information and data comprises the patient data required to make sound clinical decisions, including demographics, medical
and nursing diagnoses, medication lists, allergies, and test results (IOM, 2003).
Results Management
Results management is the ability to manage results of all types electronically, including laboratory and radiology procedure reports,
both current and historical (IOM, 2003).
Order Entry Management
Order entry management is the ability of a clinician to enter medication and other care orders, including laboratory, microbiology,
pathology, radiology, nursing, supply orders, ancillary services, and consultations, directly into a computer (IOM, 2003).
Decision Support
Decision support entails the use of computer reminders and alerts to improve the diagnosis and care of a patient, including screening
for correct drug selection and dosing, screening for medication interactions with other medications, preventive health reminders in
such areas as vaccinations, health risk screening and detection, and clinical guidelines for patient disease treatment (IOM, 2003).
Electronic Communication and Connectivity
Electronic communication and connectivity include the online communication among healthcare team members, their care partners,
and patients including e-mail, Web messaging, and an integrated health record within and across settings, institutions, and
telemedicine (IOM, 2003).
Patient Support
Patient support encompasses patient education and self-monitoring tools, including interactive computer-based patient education,
home telemonitoring, and telehealth systems (IOM, 2003).
Administrative Processes
Administrative processes are activities carried out by the electronic scheduling, billing, and claims management systems, including
electronic scheduling for inpatient and outpatient visits and procedures, electronic insurance eligibility validation, claim authorization
and prior approval, identification of possible research study participants, and drug recall support (IOM, 2003).
Reporting and Population Health Management
Reporting and population health management are the data collection tools to support public and private reporting requirements,
including data represented in a standardized terminology and machine-readable format (IOM, 2003).
NIST has not provided an exhaustive list of all possible features and functions of an EHR. Consequently, different vendor EHR
systems combine different components in their offerings, and often a single set of EHR components may not meet the needs of all
clinicians and patient populations. For example, a pediatric setting may demand functions for immunization management, growth
tracking, and more robust order entry features to include weight-based dosing (Spooner & Council on Clinical Information
Technology, 2007). These types of features may not be provided by all EHR systems, and it is important to consider EHR certification
to be a minimum standard.
Advantages of Electronic Health Records
There are mixed reviews of the advantages offered by EHRs. Much has been written about the potential promise of reduced costs,
improved quality, and better outcomes, but very little of this promise’s realization has been substantiated except anecdotally (Sidorov,
2006). Possible methods to estimate EHR benefits include using vendorsupplied data that have been retrieved from their customers’
systems, synthesizing and applying studies of overall EHR value, creating logical engineering models of EHR value, summarizing
focused studies of elements of EHR value, and conducting and applying information from site visits (HealthIT.gov, 2012; Thompson,
Osheroff, Classen, & Sittig, 2007). However, the time and effort involved in completing this work are further increased by the fact that
historically there has been no standard by which to measure adoption or expected benefits (HealthIT.gov, 2012; RWJF, 2006;
Thompson et al., 2007).
With the advent of ARRA, there are now 25 meaningful use objectives for eligible providers and 24 such objectives for eligible
hospitals (CMS, 2010a). The Nursing Informatics: Improving Workflow and Meaningful Use chapter provides an expanded discussion
of meaningful use. In addition, the final rule calls for providers to report on three required clinical quality measures and three
additional quality measures of their choice from a list of 44 possible measures (CMS, 2010b). Eligible hospitals must report on 15
clinical quality measures. Although these objectives and measures will provide a universal benchmark moving forward for EHR
benefits, for most healthcare organizations these outcomes alone will not provide sufficient return on investment to justify the capital
investment made to implement an EHR, and additional benefits will continue to be sought.
The four most common benefits cited for EHRs are (1) increased delivery of guidelines-based care, (2) enhanced capacity to
perform surveillance and monitoring for disease conditions, (3) reduction in medication errors, and (4) decreased use of care
(Chaudhry et al., 2006; HealthIT.gov, 2012). These findings were echoed by two similar literature reviews. The first review focused on
the use of informatics systems for managing patients with chronic illness. It found that the processes of care most positively impacted
were guidelines adherence, visit frequency (i.e., a decrease in emergency department visits), provider documentation, patient treatment
adherence, and screening and testing (Dorr et al., 2007).
The second review was a cost–benefit analysis of health information technology completed by the Agency for Healthcare Research
and Quality (AHRQ) that studied the value of an EHR in the ambulatory care and pediatric settings, including its overall economic
value. The AHRQ study highlighted the common findings already described, but also noted that most of the data available for review
came from six leading healthcare organizations in the United States, underscoring the challenge of generalizing these results to the
broader healthcare industry (Shekelle, Morton, & Keeler, 2006). As noted previously by the HIMSS Stage 7 Awards, the challenge to
generalize results persists in the hospital arena, with fewer than 1% of U.S. hospitals or eight leading organizations providing most of
the experience with comprehensive EHRs (HIMSS, 2010a).
Finally, all three literature reviews cited here indicated that there are a limited number of hypothesis-testing studies of EHRs and
even fewer that have reported cost data.
The descriptive studies do have value, however, and should not be hastily dismissed. Although not as rigorous in their design, they
do describe the advantages of EHRs well and often include useful implementation recommendations learned from practical experience.
As identified in these types of reviews, EHR advantages include simple benefits, such as no longer having to interpret poor
handwriting and handwritten orders, reduced turnaround time for laboratory results in an emergency department, and decreased time to
administration of the first dose of antibiotics in an inpatient nursing unit (HealthIT.gov, 2012; Husk & Waxman, 2004; Smith et al.,
2004). In the ambulatory care setting, improved management of cardiacrelated risk factors in patients with diabetes and effective
patient notification of medication recalls have been demonstrated to be benefits of the EHR (Jain et al., 2005; Reed & Bernard, 2005).
Two other unique advantages that have great potential are the ability to use the EHR and decision support functions to identify patients
who qualify for research studies or who qualify for prescription drug benefits offered by pharmaceutical companies at safety-net
clinics and hospitals (Embi et al., 2005; Poprock, 2005).
The HIMSS Davies Award may be the best resource for combined quantitative and qualitative results of successful EHR
implementation. The Davies Award recognizes healthcare organizations that have achieved both excellence in implementation and
value from health information technology (HIMSS, 2010b). One winner demonstrated a significant avoidance of medication errors
because of bar-code scanning alerts, a $3 million decrease in medical records expenses as a result of going paperless, and a 5%
reduction of duplicate laboratory orders by using computerized provider order entry alerting (HIMSS, 2010c). Another winner noted a
13% decrease in adverse drug reactions through the use of computerized physician order entry; it also achieved a decrease in
methicillin-resistant Staphylococcus aureus (MRSA) nosocomial infections from 9.8 per 10,000 discharges to 6.4 per 10,000
discharges in less than a year using an EHR flagging function, which made clinicians immediately aware that contact precautions were
required for MRSA–positive patients (HIMSS, 2009). At both organizations, there was qualitative and quantitative evidence of high
rates of end user adoption and satisfaction with use of the EHR.
A 2011 study of the effects of EHR adoption on nurse perceptions of quality of care, communication, and patient safety
documented that nurses report better care outcomes and fewer concerns with care coordination and patient safety in hospitals with a
basic EHR (Kutney-Lee & Kelly, 2011). In this study, nurses perceived that in hospitals with a functioning EHR, there was better
communication among staff, especially during patient transfers, and fewer medication errors.
Without an EHR system, any of these benefits would be very difficult and costly to accomplish. Thus, despite limited standards
and published studies, there is enough evidence to warrant pursuing widespread implementation of the EHR (Halamka, 2006;
HealthIt.gov, 2012), and certainly enough evidence to warrant further study of the use and benefits of EHRs. Box 15-2 describes some
of the clinical information system (CIS) functions of an EHR.
BOX 15-2 THE EHR AS A CLINICAL INFORMATION SYSTEM
Denise Tyler
A clinical information system (CIS) is a technology-based system applied at the point of care and designed to support care by providing instant
access to information for clinicians. Early CISs implemented prior to the advent of EHRs were limited in scope and provided such information as
interpretation of laboratory results or a medication formulary and drug interaction information. With the implementation of EHRs, the goal of many
organizations is to expand the scope of the early CISs to become comprehensive systems that provide clinical decision support, an electronic patient
record, and in some instances professional development and training tools. Benefits of such a comprehensive system include easy access to patient
data at the point of care; structured and legible information that can be searched easily and lends itself to data mining and analysis; and improved
patient safety, especially the prevention of adverse drug reactions and the identification of health risk factors, such as falls.
TRACKING CLINICAL OUTCOMES
The ability to measure outcomes can be enhanced or impeded by the way an information system is designed and used. Although many practitioners
can paint a very good picture of the patient by using a narrative (free text), employing this mode of expression in a clinical system without the use of
a coded entry makes it difficult to analyze the care given or the patient’s response. Free-text reporting also leads to inconsistencies of reporting from
clinician to clinician and patient information that is fragmented or disorganized. This can limit the usefulness of patient data to other clinicians and
interfere with the ability to create reports from the data for quality assurance and measurement purposes. Moreover, not all clinicians are equally
skilled at the free-text form of communication, yielding inconsistent quality of documentation. Integrating standardized nursing terminologies into
computerized nursing documentation systems enhances the ability to use the data for reporting and further research.
According to the IOM (2012), “Payers, healthcare delivery organizations and medical product companies should contribute data to research and
analytic consortia to support expanded use of care data to generate new insights” (para. 2). McLaughlin and Halilovic (2006) described the use of
clinical analytics to promote medical care outcomes research. The use of a CIS in conjunction with standardized codes for patient clinical issues
helps to support the rigorous analysis of clinical data. Outcomes data produced as part of these analyses may include length of stay, mortality,
readmissions, and complications. Future goals include the ability to compare data and outcomes across various institutions as a means of developing
clinical guidelines or best practices guidelines. With the implementation of a comprehensive CIS, similar analyses of nursing outcomes could also be
performed and shared. Likewise, such a system could aid nurse administrators in cross-unit comparisons and staffing decisions, especially when
coupled with acuity systems data. In addition, clinical analytics can support required data reporting functions, especially those required by
accreditation bodies.
SUPPORTING EVIDENCE-BASED PRACTICE
Evidence-based practice (EBP) can be thought of as the integration of clinical expertise and best practices based on systematic research to enhance
decision making and improve patient care. References supporting EBP, such as clinical guidelines, are available for review at the click of a mouse or
the press of a few keystrokes. The CIS’s prompting capabilities can also reinforce the practice of looking for evidence to support nursing
interventions rather than relying on how things have been done historically. This approach enhances processing and understanding of the information
and allows the nurse to apply the information to other areas, increasing the knowledge obtained about why certain conditions or responses result in
prompts for additional questions or actions.
To incorporate EBP into the practice of clinical nursing, the information needs to be embedded in the computerized documentation system so that
it is part of the workflow. The most typical way of embedding this timely information is through clinical practice guidelines. The resulting
interventions and clinical outcomes need to be measurable and reportable for further research. The supporting documentation for the EBP needs to be
easily retrievable and meaningful. Links, reminders, and prompts can all be used as vehicles for transmission of this information. The format needs to
allow for rapid scanning, with the ability to expand the amount of information when more detail is required or desired. Balancing a consistency in
formatting with creativity can be difficult but is worth the effort to stimulate an atmosphere for learning.
EBP is supported by translational research, an exciting movement that has enormous potential for the sharing and use of EBP. The use of
translational research to support EBP may help to close the gap between what is known (research) and what is done (practice).
THE CIS AS A STAFF DEVELOPMENT TOOL
Joy Hilty, a registered nurse from Kaweah Delta, came up with a creative way to provide staff development or education without taking staff away
from the bedside to a classroom setting. She created pop-up boxes on the opening charting screens for all staff who chart on the computer. These
pop-ups vary in color and content and include a short piece of clinical information, along with a question. Staff can earn vacations from these pop-
ups for as long as 14 days by e-mailing the correct answer to the question. This medium has provided information, stimulation, and a definite benefit:
the vacation from the pop-up boxes. The pop-up box education format has also encouraged staff to share their answers, thereby creating interaction,
knowledge dissemination, and reinforcement of the education provided.
Embedding EBP into nursing documentation can also increase the compliance with Joint Commission core measures, such as providing
information on influenza and pneumococcal vaccinations to at-risk patients. In the author’s experience at Kaweah Delta, educating staff via classes,
flyers, and storyboards was not successful in improving compliance with the documentation of immunization status or offering education on these
vaccinations to at-risk patients. Embedding the prompts, information, and related questions in the nursing documentation with a link to the protocol
and educational material, however, improved the compliance to 96% for pneumococcal vaccinations and to 95% for influenza vaccinations
(Hettinger, 2007).
As more information is stored electronically, nurse informaticists must translate the technology so that the input and retrieval of information are
developed in a manner that is easy for clinicians to learn and use. A highly usable product should decrease errors and improve information entry and
retrieval. Nurse informaticists must be able to work with staff and expert users to design systems that meet the needs of the staff who will actually
use the systems. The work is not done after the system is installed; the system must continue to be developed and improved, because as staff use the
system, they will be able to suggest changes to improve it. This ongoing revision should result in a system that is mature and meets the needs of the
users.
In an ideal world, all clinical documentation will be shared through a national database, in a standard language, to enable evaluation of nursing
care, increase the body of evidence, and improve patient outcomes. With minimal effort, the information will be translated into new research that can
be analyzed and linked to new evidence that will be intuitively applied to the CIS. Alerts will be meaningful and will be patient and provider specific.
The steps required of the clinician to find current, reliable information will be almost transparent, and the information will be presented in a
personalized manner based on user preferences stored in the CIS.
REFERENCES
Hettinger, M. (2007, March). Core measure reporting: Performance improvement. Visalia, CA: Kaweah Delta Health Care District.
McLaughlin, T., & Halilovic, M. (2006). Clinical analytics, rigorous coding bring objectivity to quality assertions. Medical Staff Update Online,
30(6). http://med.stanford.edu/shs/update/archives/JUNE2006/analytics.htm
Ownership of Electronic Health Records
The implementation of an EHR has the potential to affect every member of a healthcare organization. The process of becoming a
successful owner of an EHR has multiple steps and requires integrating the EHR into the organization’s day-to-day operations and
long-term vision, as well as into the clinician’s day-to-day practice. All members of the healthcare organization—from the executive
level to the clinician at the point of care—must feel a sense of ownership to make the implementation successful for themselves, their
colleagues, and their patients. Successful ownership of an EHR may be defined in part by the level of clinician adoption of the tool,
and this section reviews key steps and strategies for the selection, implementation and evaluation, and optimization of an EHR in
pursuit of that goal.
Historically, many systems were developed locally by the information technology department of a healthcare organization. It was
not unusual for software developers to be employed by the organization to create needed systems and interfaces between them. As
commercial offerings were introduced and matured, it became less and less common to see homegrown or locally developed systems.
As this history suggests, the first step of ownership is typically a vendor selection process for a commercially available EHR.
During this step, it is important to survey the organization’s level of interest, identify possible barriers to participation, document
desired functions of an EHR, and assess the willingness to fund the implementation (Holbrook, Keshavjee, Troyan, Pray, & Ford,
2003). Although clinicians should drive the project, the assessment should also include the needs and readiness of the executive
leadership, information technology, and project management teams. It is essential that leadership understands that this type of project
is as much about redesigning clinical work as it is about technically automating it and that they agree to assume accountability for its
success (Goddard, 2000). In addition, this preacquisition phase should concentrate on understanding the current state of the health
information technology industry to identify appropriate questions and the next steps in the selection process (American Organization
of Nurse Executives, 2006). These first steps begin to identify any organizational risks related to successful implementation and pave
the way for initiating a change management process to educate the organization about the future state of delivering health care with an
EHR system.
The second step of the selection process is to select a system based on the organization’s current and predicted needs. It is common
during this phase to see a demonstration of several vendors’ EHR products. Based on the completed needs assessment, the
organization should establish key evaluation criteria to compare the different vendors and products. These criteria should include both
subjective and objective items that cover such topics as common clinical workflows, decision support, reporting, usability, technical
build, and maintenance of the system. Providing the vendor with these guidelines will ensure that the process meets the organization’s
needs; however, it is also essential to let the vendor demonstrate a proposed future state from its own perspective. This activity is
critical to ensuring that the vendor’s vision and the organization’s vision are well aligned (Konschak & Shiple, n.d.). It also helps spark
dialogue about the possible future state of clinical work at the organization and the change required in obtaining it. Such
demonstrations not only enable the organization to compare and contrast the features and functions of different systems, but also are a
good way to engage the organization’s members in being a part of this strategic decision.
Implementation planning should occur concurrently with the selection process, particularly the assessment of the scope of the
work, initial sequencing of the EHR components to be implemented, and resources required. However, this step begins in earnest once
a vendor and a product have been selected. In addition to further refining the implementation plan, this is the time to identify key
metrics by which to measure the EHR’s success. An organization may realize numerous benefits from implementing an EHR. It should
choose metrics that match its overall strategy and goals in the coming years and may include expected improvements in financial,
quality, and clinical outcomes. Commonly used metrics focus on reductions in the number of duplicate laboratory tests through
duplicate orders alerting, reductions in the number of adverse drug events through the use of bar-code medication administration,
meaningful use objectives and measures, and the EHR advantages mentioned earlier in this chapter. To ensure that the desired benefits
are realized, it is important to avoid choosing so many that they become meaningless or unobtainable, to carefully and practically
define those that are chosen, to measure before and after the implementation, and to assign accountability to a member of the
organization to ensure the work is completed.
End-user adoption of the EHR is also essential to realizing its benefits. Clinicians must be engaged to use the EHR successfully in
their practice and daily workflows so that data may be captured to drive the decision support that underlies so many of the advantages
and metrics described. To promote adoption, a change management plan must be developed in conjunction with the EHR
implementation plan. The most effective change management plans offer end users several exposures to the system and relevant
workflows in advance of its use and continue through the go-live and postlive time periods. Successful prelive strategies include end-
user involvement as subject-matter experts to validate the EHR workflow design and content build, hosting end-user usability testing
sessions, shadowing end users in their current daily work in parallel with the new system, and formal training activities. The goal of
these prelive activities is not only to ensure that the EHR implementation will meet end user needs, but also to assess the impact of the
new EHR on current workflow and process. The larger the impact, the more change management is required above and beyond system
training. For example, simulation laboratory experiences may be offered to more thoroughly dress rehearse a significant workflow
change, executive leadership may need to convey their support and expectations of clinicians about a new way of working, and
generally more anticipatory guidance is required to communicate to those impacted by the changes.
Training may be delivered in a variety of media. Often a combination of approaches works best, including classroom time,
electronic learning, independent exercises, and peer-to-peer, at-the-elbow support. Training must be workflow based and reflect real
clinical processes. It must also be planned and budgeted for through the postlive period to ensure that competency with the system is
assessed at the go-live point and that any necessary retraining or reinforcements are made in the 30 to 60 days postlive. This not only
promotes reliability and safe use of the system as it was designed but also can have a positive impact on end users’ morale: Users will
feel that they are being supported beyond the initial go-live period and have an opportunity to move from basic skills to advanced
proficiency with the system.
Finally, the implementation plan should account for the long-term optimization of the EHR. This step is commonly overlooked and
often results in benefits falling short of expectations because the resources are not available to realize them permanently. It also often
means the difference between end users of EHRs merely surviving the change versus becoming savvy about how to adopt the EHR as
another powerful clinical tool, such as the stethoscope (HealthIT.gov, 2012). Optimization activities of the EHR should be considered
a routine part of the organization’s operations, should be resourced accordingly, and should emphasize the continued involvement of
clinician users to identify ways that the EHR can enable the organization to achieve its overall mission. Many organizations start an
implementation of EHRs with the goal of transforming their care delivery and operations. An endeavor that differs from simply
automating a previously manual or fragmented process, transformation often includes steps to improve the process so as to realize
better patient care outcomes or added efficiency. Although some transformation is experienced with the initial use of the system, most
of this work is done postimplementation and relies on widespread clinician adoption of the EHR. As such, it makes optimization a
critical component to successful ownership of an EHR.
Box 15-3 reviews the barriers to and methods for successful acceptance of EHRs.
BOX 15-3 RESISTANCE TO IMPLEMENTATION
Julie A. Kenney and Ida Androwich
For an implementation to be successful, a few things need to happen. The informatics nurse specialist (INS) will need to understand and use change
management theory to ensure that the implementation of the new EHR system will be successful. It is a well-known fact that nurses can make or
break a system implementation. A nursing staff that is involved early in the implementation process has been found to be a major determinant in a
successful implementation. Assessing nursing attitudes and concerns early in the process can aid the INS in determining the best way to proceed with
staff education and implementation rollout. Nurses may believe that the implementation that should be making their job easier will actually make it
more challenging (Trossman, 2005). Nurses who feel that the system has been forced onto them are very likely to be highly resistant to the change.
This is why it is imperative that nurses be involved in the design, development, and implementation of the EHR. Nurses who have been involved in
the implementation process will ensure that the product meets the needs of the staff, which will result in high end-user satisfaction (McLane, 2005).
Another challenge facing those wishing to implement an EHR is the issue that writing is nearly automatic for most, but using a computer is not.
This potential problem can be overcome by ensuring that data entry and system navigation make for a system that is user friendly (Walsh, 2004).
Voice data entry is an easy way to enter data into the system and may be a way for those who are not comfortable with computers to still use the
system effectively (Walsh, 2004). Another way to encourage staff to accept the new EHR is to ensure that they have received adequate training prior
to the implementation as well as to provide continued support and education after the implementation.
The implementation of a new EHR system requires the staff to make significant changes to how they work and how they handle
patient information. The INS who is familiar with change management and the NI process should have an integral role in the
redesign of workflow processes to ensure a smooth transition from a paper record to an electronic record. Many excellent EHR
systems fail after their installation due to poor implementation planning. It is imperative that nurses are employed in the
information systems (IS) department (Trossman, 2005).
REFERENCES
McLane, S. (2005). Designing an EMR planning process based on staff attitudes toward and opinions about computers in healthcare. CIN:
Computers, Informatics, Nursing, 23(2), 85–92.
Trossman, S. (2005). Bold new world: Technology should ease nurses’ jobs, not create a greater work load. American Journal of Nursing, 105(5),
75–77.
Walsh, S. (2004). The clinician’s perspective on electronic health records and how they can affect patient care. BMJ: British Medical Journal,
328(7449), 1184–1187.
Flexibility and Expandability
Health care is as unique as the patients themselves. It is delivered in a variety of settings, for a variety of reasons, over the course of a
patient’s lifetime. In addition, patients rarely receive all their care from one healthcare organization; indeed, choice is a cornerstone of
the American healthcare system. An EHR must be flexible and expandable to meet the needs of patients and caregivers in all these
settings, despite the challenges.
At a very basic level, there is as yet no EHR system available that can provide all functions for all specialties to such a degree that
all clinicians would successfully adopt it. Consider oncology as an example. Most systems do not yet provide the advanced ordering
features required for the complex treatment planning undertaken in this field. An oncologist could use a general system, but he or she
would not find as many benefits without additional features for chemotherapy ordering, lifetime cumulative dose tracking, or the
ability to adjust a treatment day schedule and recalculate a schedule for the remaining days of the plan.
Further, most healthcare organizations do not yet have the capacity to implement and maintain systems in all care areas. As one
physician stated, “implementing an EMR is a complex and difficult multidisciplinary effort that will stretch an organization’s skills
and capacity for change” (Chin, 2004, p. 47).
These two conditions are improving every day at both vendor and healthcare organizations alike. Improvements in both areas were
recently fueled by ARRA incentives (see Box 15-4).
BOX 15-4 CLOUDY EHRS
A paradigm shift from healthcare facility–owned, machine-based computing to off-site, vendor-owned cloud computing, web browser-based log-in
accessible data, software, and hardware could link systems together and reduce costs. Hospitals with shrinking budgets and extreme IT needs are
exploring the successes in this area achieved in other industries, such as Amazon’s S3. As providers strive to implement potent EHRs, they are
looking for cloud-based models that offer the necessary functionality without having to assume the burden associated with all of the hardware,
software, application, and storage issues. However, in the face of the HITECH Act and its associated penalties, how can we overcome the challenges
to realize the benefits of this approach? Cloud computing has both advantages and disadvantages, and while they explore this new paradigm,
healthcare providers must relinquish control as they continue to strive to maintain security. The vendors that are responsible for developing and
maintaining this new environment are also facing challenges originating from both legislatures and healthcare providers. As the vendors and
healthcare providers work together to improve the implementation and adoption of the cloud-based EHR, the sky is the limit!
ARRA has also set the expectation that despite the large number of settings in which a patient may receive care, a minimum set of
data from those records must flow or “interoperate” between each setting and the unique EHR systems used in those settings. Today,
interoperability exists through what is called a continuity of care document. This data set includes patient demographics, medication,
allergy, and problem lists, among other things, and the formatting and exchange of the continuity of care document is required to be
supported by EHR vendors and healthcare organizations seeking ARRA meaningful use incentives.
Despite this positive step forward, financial and patient privacy hurdles remain to be overcome to achieve an expansive EHR. Most
health care is delivered by small community practices and hospitals, many of which do not have the financial or technical resources to
implement EHRs. HHS recently loosened regulations so that physicians may now be able to receive healthcare information technology
software, hardware, and implementation services from hospitals to alleviate the financial burden placed on individual providers and to
foster more widespread adoption of the EHR.
Finally, patient privacy is a pivotal issue in determining how far and how easy it will be to share data across healthcare
organizations. In addition to the Health Insurance Portability and Accountability Act privacy rules, many states have regulations in
place related to patient confidentiality. The recent experience of the state of Minnesota foreshadows what all states will soon be facing.
In 2007, Governor Tim Pawlenty announced the creation of the Minnesota Health Information Exchange (State of Minnesota, 2007).
Although the intentions of the exchange were to promote patient safety and increase healthcare efficiency across the state, it raised
significant concerns about security and privacy. New questions arose about the definition of when and how patient consent is required
to exchange data electronically, and older paper-based processes needed to be updated to support real-time electronic exchange
(Minnesota Department of Health, 2007). For health exchanges such as these to reach their full potential, members of the public must
be able to trust that their privacy will be protected, or else the healthcare industry risks that patients may not share a full medical
history, or worse yet, may not seek care, effectively making the exchanges useless.
The Future
Despite the challenges, the future of EHRs is an exciting one for patients and clinicians alike. Benefits may be realized by
implementing stand-alone EHRs as described here, but the most significant transformation will come as interoperability is realized
between systems. As the former national information technology coordinator in the HHS, David Brailer, noted about the potential of
interoperability:
For the first time, clinicians everywhere can have a longitudinal medical record with full information about each patient.
Consumers will have better information about their health status since personal health records and similar access strategies
can be feasible in an interoperable world. Consumers can move more easily between and among clinicians without fear of
their information being lost. Payers can benefit from the economic efficiencies, fewer errors, and reduced duplication that
arises from interoperability. Healthcare information exchange and interoperability (HIEI) also underlies meaningful public
health reporting, bioterrorism surveillance, quality monitoring, and advances in clinical trials. In short, there is little that most
people want from health care for which HIEI isn’t a prerequisite. (Brailer, 2005, p. W 5-20)
The future also holds tremendous potential for EHR features and functions that will include not only more sophisticated decision
support and clinical reporting capacity, but also improved biomedical device integration, ease of use and intuitiveness, and access
through more hardware platforms.
Implementations of EHRs will become more commonplace in the near future, with ARRA putting pressure on healthcare
organizations to move more quickly toward adoption of such records. More organizations adopting EHRs will facilitate broader
dissemination of implementation best practices, with the hope of further shortening the time required to take advantage of advanced
EHR features.
Summary
It is an important time for health care and technology. EHRs have come to the forefront and will remain central to shaping the future of
health care. In an ideal world, all nurses, from entry-level personnel to executives, will have a basic competency in nursing informatics
that will enable them to participate fully in shaping the future use of technology in the practice at a national level and wherever care is
delivered. Such initiatives as TIGER are imperative for adoption and ultimately more visibility of nursing in the later phases of the
ARRA meaningful use standards, which are still being defined.
THOUGHT-PROVOKING
QUESTIONS
1. What are the implications for nursing education as the EHR becomes the standard for caring for patients?
2. What are the ethical considerations related to interoperability and a shared EHR?
3. You are asked about a diagnosis with which you are unfamiliar. Where would you start looking for information? How would you determine the
validity of the information?
References
American Organization of Nurse Executives. (2006, September). Defining the role of the nurse executive in technology acquisition and implementation.
Washington, DC: Author. Retrieved October 2007 from
http://www.aone.org/aone/pdf/Guiding%20Principles%20for%20Acquisition%20and%20Implementation%20of%20Information%20Technology
Brailer, D. J. (2005, January). Interoperability: The key to the future healthcare system. Health Affairs—Web Exclusive, W 5-19–W 5-21.
http://content.healthaffairs.org/cgi/reprint/hlthaff.w5.19v1
Bush, G. W. (2004). State of the Union address. http://www.whitehouse.gov/news/releases/2004/01/20040120-7.html
Centers for Medicare and Medicaid Services (CMS). (2010a). EHR incentive programs: Meaningful use.
https://www.cms.gov/EHRIncentivePrograms/35_Meaningful_Use.asp
Centers for Medicare and Medicaid Services (CMS). (2010b). Quality measures: Electronic specifications.
http://www.cms.gov/QualityMeasures/03_ElectronicSpecifications.asp#TopOfPage Certification Commission for Healthcare Information
Technology. (2007).
Certification Commission announces new work group members. Retrieved July 2007 from http://www.cchit.org/about/news/releases/Certification-
Commission-Announces-New-Work-Group-Members.asp Chaudhry, B., Wang, J., Wu, S., Maglione, M., Mojica, W., Roth, E., … Shekelle, P.
(2006).
Systematic review: Impact of health information technology on quality, efficiency, and costs of medical care. Annals of Internal Medicine, 144(10), E-
12–E-22.
Chin, H. L. (2004). The reality of EMR implementation: Lessons from the field. Permanente Journal, 8(4), 43–48.
Department of Health and Human Services (DHHS). (2010). Medicare and Medicaid programs: Electronic health record incentive program. Retrieved
July 2010 from http://www.ofr.gov/OFRUpload/OFRData/2010-17202_PI
Dorr, D., Bonner, L. M., Cohen, A. N., Shoai, R. S., Perrin, R., Chaney, E., & Young, A. (2007). Informatics systems to promote improved care for
chronic illness: A literature review. Journal of the American Medical Informatics Association, 14(2), 156–163.
Embi, P. J., Jain, A., Clark, J., Bizjack, S., Hornung, R., & Harris, C. M. (2005). Effect of a clinical trial alert system on physician participation in trial
recruitment. Archives of Internal Medicine, 165, 2272–2277.
Goddard, B. L. (2000). Termination of a contract to implement an enterprise electronic medical record system. Journal of American Medical Informatics
Association, 7, 564–568.
Halamka, J. D. (2006, May). Health information technology: Shall we wait for the evidence? [Letter to the editor]. Annals of Internal Medicine, 144(10),
775–776.
Healthcare Information and Management Systems Society (HIMSS). (2007). Electronic health record. http://www.himss.org/ASP/topics_ehr.asp
Healthcare Information and Management Systems Society (HIMSS). (2009). HIMSS Davies Organizational Award application: MultiCare. Retrieved
October 2010 from http://www.himss.org/davies/docs/2009_RecipientApplications/MultiCareConnectHIMSSDaviesManuscript
Healthcare Information and Management Systems Society (HIMSS). (2010a). Davies Award. http://www.himss.org/davies/
Healthcare Information and Management Systems Society (HIMSS). (2010b). HIMSS Davies Award. http://apps.himss.org/davies/
Healthcare Information and Management Systems Society (HIMSS). (2010c). Recipient list.
http://www.himssanalytics.org/hc_providers/stage7Hospitals.asp
Healthcare Information and Management Systems Society (HIMSS). (2013). HIMSS 2013 iHIT Study: Executive summary.
http://www.himss.org/library/clinical-informatics/2013-ihitstudy-executive-summary
Healthcare Information and Management Systems Society (HIMSS) Analytics. (2013). Electronic medical record adoption model (EMRAM).
http://www.himssanalytics.org/emram/index.aspx
HealthIT.gov. (2012). Benefits of EHRs: Why adopt EHRs? http://www.healthit.gov/providers-professionals/why-adopt-ehrs
HealthIT.gov. (2013). How to attain meaningful use. http://www.healthit.gov/providers-professionals/how-attain-meaningful-use
Holbrook, A., Keshavjee, K., Troyan, S., Pray, M., & Ford, P. T. (2003). Applying methodology to electronic medical record selection. International
Journal of Medical Informatics, 71, 43–50.
Husk, G., & Waxman, D. A. (2004). Using data from hospital information systems to improve emergency care. Academic Emergency Medicine, 11(11),
1237–1244.
Institute of Medicine (IOM). (2001). Crossing the quality chasm: A new health system for the 21st century. Washington, DC: National Academies Press.
Institute of Medicine (IOM). (2003). Key capabilities of an electronic health record system: Letter report. Washington, DC: National Academies Press.
Institute of Medicine (IOM). (2012). Best care at lower cost. http://www.iom.edu/~/media/Files/Report%20Files/2012/Best-
Care/Best%20Care%20at%20Lower%20Cost_Recs
Jain, A., Atreja, A., Harris, C. M., Lehmann, M., Burns, J., & Young, J. (2005). Responding to the rofecoxib withdrawal crisis: A new model for
notifying patients at risk and their healthcare providers. Annals of Internal Medicine, 142(3), 182–186.
Jansen, W., & Grance, T. (2011). National Institute of Standards and Technology (NIST): Guidelines on security and privacy in public cloud computing.
https://cloudsecurityalliance.org/wp-content/uploads/2011/07/NIST-Draft-SP-800-144_cloud-computing
Konschak, C., & Shiple, D. (n.d.). System selection: Aligning vision and technology.
http://www.divurgent.com/images/White%20Paper.Vendor%20Selection.vfinal
Kutney-Lee, A., & Kelly, D. (2011). The effect of hospital electronic health record adoption on nurse-assessed quality of care and patient safety. Journal
of Nursing Administration, 41(11), 466–472. doi: 10.1097/NNA.0b013e3182346e4b
Minnesota Department of Health. (2007, June). Minnesota Health Records Act—HF 1078 fact sheet. Minneapolis, MN: Author.
http://www.health.state.mn.us/e-health/hras/hras050113facts
National Institute of Standards and Technology (NIST). (2010). Meaningful use test measures: Approved test procedures.
http://healthcare.nist.gov/use_testing/finalized_requirements.html
National Institutes of Health. (2006, April). Electronic health records overview. McLean, VA: Mitre Corporation.
Poprock, B. (2005, September). Using Epic’s alternative medications reminder to reduce prescription costs and encourage assistance programs for
indigent patients. Presented at the Epic Systems Corporation user group meeting, Madison, WI.
Reed, H. L., & Bernard, E. (2005). Reductions in diabetic cardiovascular risk by community primary care providers. International Journal of
Circumpolar Health, 64(1), 26–37.
Robert Wood Johnson Foundation (RWJF). (2006). Health information technology in the United States: The information base for progress.
http://www.rwjf.org/programareas/resources/product.jsp?id=15895&pid=1142&gsa=1
Shekelle, P. G., Morton, S. C., & Keeler, E. B. (2006). Costs and benefits of health information technology. Evidence report/technology assessment, No.
132 [Prepared by the Southern California Evidence-based Practice Center under Contract No. 290-02-0003]. Agency for Healthcare Research and
Quality Publication No. 06-E006. Rockville, MD: Agency for Healthcare Research and Quality.
Sidorov, J. (2006). It ain’t necessarily so: The electronic health record and the unlikely prospect of reducing healthcare costs. Health Affairs, 25(4),
1079–1085.
Smith, T., Semerdjian, N., King, P., DeMartin, B., Levi, S., Reynolds, K., … Dowd, J. (2004). Nicholas E. Davies Award of Excellence: Transforming
healthcare with a patient-centric electronic health record system. Evanston, IL: Evanston Northwestern Healthcare.
http://www.himss.org/content/files/davies2004_evanston
Spooner, S. A., & Council on Clinical Information Technology. (2007). Special requirements of electronic health record systems in pediatrics.
Pediatrics, 119, 631–637.
State of Minnesota, Office of the Governor. (2007). New public–private partnership to improve patient care, safety and efficiency. Retrieved October
2007 from http://www.governor.state.mn.us/mediacenter/pressreleases/2007/PROD008303.html
Technology Informatics Guiding Education Reform (TIGER). (2006). Evidence and informatics transforming nursing.
http://www.amia.org/sites/amia.org/files/Malin-AMIA-Tiger-Team-Testimony
Thompson, D. I., Osheroff, J., Classen, D., & Sittig, D. F. (2007). A review of methods to estimate the benefits of electronic medical records in hospitals
and the need for a national database. Journal of Healthcare Information Management, 21(1), 62–68.
Chapter 16
Informatics Tools to Promote Patient Safety and Clinical
Outcomes
Kathleen Mastrian and Dee McGonigle
OBJECTIVES
1. Explore the characteristics of a safety culture.
2. Examine strategies for developing a safety culture.
3. Recognize how human factors contribute to errors.
4. Appreciate the impact of informatics technology on patient safety.
Key Terms
Alarm fatigue
Bar-code medication administration
Clinical decision support
Computerized physician order entry
Failure modes and effects analysis
High-hazard drug
Human factors engineering
Radiofrequency identifier
Root-cause analysis
Safety culture
Smart pump
Smart room
Wearable devices
Workaround
Introduction
Nursing professionals have an ethical duty to ensure patient safety. Increasing demands on professionals in complex and fast-paced
healthcare environments, however, may lead them to cut corners or develop workarounds that deviate from accepted and expected
practice protocols. These deviations are not carried out deliberately to put patients at risk, but rather are often practiced in the interest
of saving time or because the organizational culture is such that risky behaviors are commonplace. Occasionally, these inappropriate
actions or omissions of appropriate actions result in harm or significant risk of harm to patients. Consider the following case scenario:
A 19-year-old obese woman who had recently undergone C-section delivery of a baby presented in the emergency department
(ED) with dyspnea. Believing the patient had developed a pulmonary embolism, the physician prescribed an IV heparin bolus
dose of 5,000 units followed by a heparin infusion at 1,000 units/hour. After administering the bolus dose, a nurse started the
heparin infusion but misprogrammed the pump to run at 1,000 mL/hour, not 1,000 units/hour (20 mL/hour). By the time the
error was discovered, the patient had received more than 17,000 units (5,000 unit loading dose and about 12,000 units from
the infusion) in less than an hour since arrival in the ED. A smart pump with dosing limits for heparin had been used. Thus, the
programming error should have been recognized before the infusion was started. However, the nurse had elected to bypass the
dose-checking technology and had used the pump in its standard mode. It was quite fortunate that the patient did not
experience adverse bleeding as her aPTT values were as prolonged as 240 seconds when initially measured and 148 seconds
two hours later. (Institute for Safe Medication Practices, 2007, para. 2)
The smart pump used in this scenario was equipped with dose calculation software that compares the programmed infusion rate to
a drug database to check for dosing within safe limits. This technology is particularly important when high-alert or high-hazard drugs
are being administered. In this case, however, the available dose-checking technology had been turned off and the pump was operated
in standard mode. A subsequent analysis of the error event revealed that many nurses in the institution were bypassing the safety
technology afforded by the smart pump to save time.
This chapter focuses on some of the recommended organizational strategies used to promote a culture of safety and some of the
specific informatics technologies designed to reduce errors and promote patient safety.
What Is a Culture of Safety?
The 2000 Institute of Medicine report, To Err Is Human, is widely credited for launching the current focus on patient safety in health
care. This report was followed in 2001 by the Institute of Medicine’s Crossing the Quality Chasm report, which brought to national
attention healthcare quality and safety. This national attention resulted in a $50 million grant by Congress to the Agency for Healthcare
Research and Quality (AHRQ) to launch initiatives focused on safety research for patients. Other initiatives prompted by these seminal
reports were the Joint Commission’s National Patient Safety Goals (2002); the National Quality Forum’s adverse events and “never
events” list (2002); the creation of the Office of National Coordinator for Health IT to computerize health care (2004); the formation of
the World Health Organization’s Alliance for Patient Safety (2004); the Institute for Healthcare Improvement’s (IHI) 100,000 Lives
campaign (2005) and 5 Million Lives campaign (2008); Congressional authorization of patient safety organizations created by the
Patient Safety and Quality Improvement Act to promote blameless error reporting and shared learning (2005); the “no pay for errors”
initiative launched by Medicare (2008); and the $19 billion Congressional appropriation to support electronic health records (EHRs)
and patient safety (Wachter, 2010).
The AHRQ (2012) safety culture primer suggested that organizations should strive to achieve high reliability by being committed
to improving healthcare quality and preventing medical errors and to demonstrate an overall commitment to patient safety. That is,
everyone and every level in an organization must embrace the safety culture. Key features of a safety culture identified by the AHRQ
are as follows:
Acknowledgment of the high-risk nature of an organization’s activities and the determination to achieve consistently safe
operations
A blame-free environment where individuals are able to report errors or near misses without fear of reprimand or punishment
Encouragement of collaboration across ranks and disciplines to seek solutions to patient safety problems
Organizational commitment of resources to address safety concerns (AHRQ, 2012, para. 1)
An important part of the safety culture is cultivating a blame-free environment. Errors and near misses must always be reported so
that they can be thoroughly analyzed. All organizations can learn from mistakes and change their organizational processes or culture to
ensure patient safety. The Patient Safety and Quality Improvement Act of 2005 mandates the creation of a national database of medical
errors and funded several organizations to analyze these data with the goal of developing shared learning to prevent medical errors.
Organizations themselves can engage in root-cause analysis or failure modes and effects analysis to examine medical errors closely
and to determine the system processes that need to be changed to prevent similar future errors (Harrison & Daly, 2009). A tool for
implementing root-cause analysis developed by the National Center for Patient Safety is detailed at this website:
http://www.patientsafety.gov/CogAids/RCA/index.html#page=page-1. Similarly, the IHI has a website dedicated to failure modes and
effects analysis. “Failure Modes and Effects Analysis (FMEA) is a systematic, proactive method for evaluating a process to identify
where and how it might fail, and to assess the relative impact of different failures in order to identify the parts of the process that are
most in need of change” (IHI, 2011b, para. 1). This powerful tool and shared experiences from other organizations can be viewed at
http://www.ihi.org/knowledge/Pages/Tools/FailureModesandEffectsAnalysisTool.aspx.
If one embraces a blame-free environment to encourage error reporting, then where does individual accountability fit in?
According to the ARHQ, one way to balance these competing cultural values (blameless versus accountability) is to establish a “just
culture” where system or process issues that lead to unsafe behaviors and errors are addressed by changing practices or workflow
processes, and a clear message is communicated that reckless behaviors are not tolerated. The “just culture” approach accounts for
three types of behaviors leading to patient safety compromises: (1) human error (unintentional mistakes); (2) risky behaviors
(workarounds); and (3) reckless behavior (total disregard for established policies and procedures).
Strategies for Developing a Safety Culture
Strategies for achieving a safety culture have been addressed frequently in the literature. The focus here is limited to those strategies
described by two key organizations, the AHRQ and the IHI. The AHRQ (2012) provides access to data from two validated surveys, the
Patient Safety Culture Survey and the Safety Attitudes Questionnaire. The AHRQ suggests that teamwork training, executive walk-
arounds, and unit-based safety teams have improved safety culture perceptions but have not led to a significant reduction in error rates.
The IHI (2011a) stresses that organizational leaders must drive the culture change by making a visible commitment to safety and by
enabling staff to share safety information openly. Some of the strategies suggested by the IHI include appointing a safety champion for
every unit, creating an adverse event response team, and reenacting or simulating adverse events to better understand the
organizational or procedural processes that failed.
A systems engineering approach to patient safety, in which technology manufacturers partner with organizations to identify risks to
patient safety and promote safe technology integration, is advocated by Ebben, Gieras, and Gosbee (2008). They note that human
factors engineering is “The discipline of applying what is known about human capabilities and limitations to the design of products,
processes, systems, and work environments.” Its application to system design improves “ease of use, system performance and
reliability, and user satisfaction, while reducing operational errors, operator stress, training requirements, user fatigue, and product
liability” (p. 327). For example, Ebben et al. describe the feel of an oxygen control knob that rotated smoothly between settings,
suggesting to the user that oxygen flows at all points on the knob, when in fact oxygen flowed only at specifically designated liter flow
settings. Human factors engineering testing would most likely reveal this design flaw, and the setting knob could be improved to
include discrete audio or tactile feedback (click into place) to the user to indicate a point on the dial where oxygen flows. Ebben et al.
emphasize that testing human use factors provides more objective safety data than the subjective responses gained from user
preference testing. “Understanding how the equipment shapes human performance is as important as evaluating reliability or other
technical criteria” (p. 329). Organizations that are purchasing medical technology devices should avail themselves of shared safety
data on equipment maintained by several key organizations, including the Joint Commission, the Food and Drug Administration, and
the Medical Product Safety Network (MedSun;
www.fda.gov/MedicalDevices/Safety/MedSunMedicalProductSafetyNetwork/default.htm).
Once the technology is integrated into the organization, biomedical engineers can become valuable partners in promoting patient
safety through appropriate use of these technologies. For example, in one organization, the biomedical engineers helped to revamp
processes associated with the new technology alarm systems after they discovered several key issues: slow response times to legitimate
alarms and multiple false alarms (promoting alarm fatigue) created by alarm parameters that were too sensitive. Strategies for
addressing these issues included improving the nurse call system by adding Voice over Internet Protocol telephones that wirelessly
receive alarms directly from technology equipment carried by all nurses, thus reducing response times to alarms; feeding alarm data
into a reporting database for further analysis; and encouraging nurses to round with physicians to provide input into alarm parameters
that were too sensitive and were generating multiple false alarms (Williams, 2009). The Research Brief describes a study of intelligent
agent (IA) technology to improve the specificity of physiologic alarms.
Clearly, there is more work to be done to create safety cultures in complex healthcare organizations and to reduce the incidence of
errors. Many organizations are looking to informatics technology to help manage these complex safety issues by using smart
technologies that provide knowledge access to users, provide automated safety checks, and improve communication processes.
Informatics Technologies for Patient Safety
Healthcare technologies are frequently designed to improve patient safety, streamline work processes, and improve the quality and
outcomes of healthcare delivery. Technology is not always the answer to patient safety, as the Joint Commission (2008) cautions: “the
overall safety and effectiveness of technology in health care ultimately depends on its human users, and … any form of technology can
have a negative impact on the quality and safety of care if it is designed or implemented improperly or is misinterpreted” (para. 2).
Although technology may certainly help to prevent or reduce errors, one must always remember that technology is not a substitution
for safety vigilance by the healthcare team in a safety culture.
Bates and Gawande (2003) urged the adoption of information technology (IT) processes to improve safety. They suggest that
information technologies improve communication, reduce errors and adverse events, increase the rapidity of response to adverse
events, make knowledge more accessible to clinicians, assist with decisions, and provide feedback on performance. They also describe
the benefits of technology-based forcing functions that direct or restrict actions or orders implemented by computer technologies. For
example, physicians may be forced to write complete and accurate medication orders, restricted from ordering an inappropriate dosing
route for a medication, or prompted to write corollary orders that should be included in the care regimen as part of the standard of care.
The Wired for Health Care Quality Act of 2005 began a series of funding streams to promote health IT, promote sharing of best
practices in health IT, and help organizations implement health IT (Harrison & Daly, 2009). Many early adopters opted to focus
technology and safety initiatives on medication ordering and administration processes. Medication errors are the most frequent and the
most visible errors because the medication administration cycle has many poorly designed work processes with several opportunities
for human error. Thus computerized physician order entry (CPOE), automated dispensing machines, smart pump technologies for
intravenous drug administration, and bar-code medication administration (BCMA) frequently preceded the adoption of the EHR in
many institutions because of the costs associated with implementing these technologies. In an ideal world, the EHR would be adopted
concurrently as part of an interoperable health IT system. In the early EHR systems, clinicians were prompted by electronic alerts
reminding them of important interventions that should be part of the standard of care, but these alerts tended to be generalized and not
patient specific—for example, “Did you check the allergy profile?” or “Has the patient received a pneumonia immunization?” These
early alert and care reminders are now evolving into more sophisticated clinical decision support (CDS) systems to promote accurate
medical diagnoses and suggest appropriate medical and nursing interventions based on patient data.
Research Brief
The investigators in one study used simple reactive intelligent agent (IA) technology to develop and test decision algorithms for improving the
sensitivity and specificity of physiologic alarms. The IA technology was tested in a 14-bed cardiothoracic unit over 28 days and was implemented in
parallel to the usual physiologic patient monitor that provided measures such as systolic blood pressure, mean arterial pressure, central venous
pressure, and cardiac index. Alarm data generated by both systems were compared and classified as to whether the alarm represented a true medical
event requiring clinician intervention or a false-positive alarm. A total of 293,049 alarms were generated by the usual physiologic monitoring system,
and 1,012 alarms were generated by the IA system after raw physiologic data were filtered using rule-based IA technology. The IA filtering system
shows promise for improving the specificity of physiologic alarms and decreasing the number of false-positive alarms generated by artifacts, thus
reducing the incidence of alert fatigue in clinicians.
Source: The full article appears in Blum, J., Kruger, G., Sanders, K., Gutierrez, J., & Rosenberg, A. (2009). Specificity improvement for network
distributed physiologic alarms based on a simple deterministic reactive intelligent agent in the critical care environment. Journal of Clinical
Monitoring and Computing, 23(1), 21–30. Retrieved from ProQuest Nursing & Allied Health Source (Document ID: 1848695561).
The National Patient Safety Foundation (2013) listed the top patient safety issues as wrong-site surgery, hospital-acquired
infections, falls, hospital readmissions, diagnostic errors, and medication errors. Many of these issues can be prevented or detected in
their early stages using informatics technologies. Other technologies designed to promote patient safety include wireless technologies
for patient monitoring, clinician alerts, point-of-care applications, and radiofrequency identification (RFID) applications. Each of these
is reviewed here, and the chapter concludes with a section discussing future technologies for patient safety.
Technologies to Support the Medication Administration Cycle
The steps in the medication administration cycle (assessment of need, ordering, dispensing, distribution, administration, and
evaluation) have been relatively stable for many years. Each of the steps depends on vigilant humans to ensure patient safety, resulting
in the five rights of medication administration: (1) the right patient, (2) the right time and frequency of administration, (3) the right
dose, (4) the right route, and (5) the right drug. Human error can be related to many aspects of this cycle. Distractions, unclear
thinking, lack of knowledge, short staffing, and fatigue are a few of the factors that cause humans to deviate from accepted safety
practices and commit medication errors. Integration of technology into the medication administration cycle promises to reduce the
potential for human errors in the cycle by performing electronic checks and providing alerts to draw attention to potential errors.
CPOE is an electronic prescribing system designed to support physicians and nurse practitioners in writing complete and
appropriate medication and care orders for patients. When CPOE is part of an EHR with a CDS system, the medication order is
electronically checked against specific data in the patient record to prevent errors, such as ordering a drug that might interact with a
drug the patient is already taking, ordering a dose that is too large for the patient’s weight, or ordering a drug that is contraindicated by
the patient’s allergy profile or renal function. Because it is impossible for, and unreasonable to expect, a clinician to remember each of
the more than 600 drugs that require a dose adjustment in the case of renal dysfunction, for example, safe dosing parameters are
provided by the CPOE (Bates & Gawande, 2003). In a stand-alone CPOE system without a CDS system, the medication orders are
simply checked by the computer against the drug database to ensure that the dose and route specified in the order are appropriate for
the medication chosen. Specific benefits of CPOE include the following:
Prompts that warn against the possibility of drug interaction, allergy, or overdose
Accurate, current information that helps physicians keep up with new drugs as they are introduced into the market
Drug-specific information that eliminates confusion among drug names that look and sound alike
Improved communication between physicians and pharmacists
Reduced healthcare costs caused by improved efficiencies (LeapFrog Group, 2008)
CPOE solves the safety issues associated with poor handwriting and unclear or incomplete medication orders. Orders can be
entered in seconds and from remote sites, eliminating the use of verbal orders that are especially subject to interpretation errors. Orders
are then transmitted electronically to the pharmacy, reducing the potential for the transcription errors commonly encountered in the
paper-based system, such as lost or misplaced orders, delayed dosing, or unreadable faxes. Thus CPOE changes workflows for all
clinical staff and physicians as well as health team communication patterns (Manor, 2010). As with any technology integration,
introduction of CPOE is associated with a resistance to change and a learning curve to gain proficiency, and users must learn to trust
the system. Manor urges careful planning and training during implementation with plenty of staff support. She also reports on the need
for a paper-based backup system in the case of network or electrical outages or system maintenance.
The verification and dispensing functions of the pharmacy can also be assisted by technology. The pharmacist begins by verifying
the allergy status of the patient and the medication reconciliation information to ensure that the new medication is compatible with
other medication in the care regimen. This verification function is computer based, and the medication order is electronically checked
via the knowledge database. If the order is verified as safe and appropriate, the pharmacist proceeds to the dispensing process. Barcode
medication labeling at a unit dose level was mandated by the Food and Drug Administration in 2004, with targeted compliance to be
achieved by 2006. A bar code is a series of alternating bars and spaces that represents a unique code that can be read by a special
barcode reader. Bar-code technology spans both the medication dispensing and administration steps in the medication administration
cycle. In the pharmacy, the bar code helps to ensure that the right drug and the right dose are dispensed by the pharmacy. Medications
that are labeled with bar codes can also be dispensed by robots capable of reading the codes or by automated dispensing machines. In
this way, bar-code technology helps with the processes of procurement, inventory, storage, preparation, and dispensing (Cohen, 2002).
The processes of drug storage, dispensing, controlling, and tracking are easily carried out via automated dispensing machines (also
known as automated dispensing cabinets, unit-based cabinets, automated dispensing devices, and automated distribution cabinets).
These devices have benefits for both the user and the organization, specifically in the areas of access security (especially with narcotics
administration tracking), safety, supply chain, and charge functions (Institute for Safe Medication Practices, 2008).
Radiofrequency identifier (RFID) technology is rapidly gaining a foothold in healthcare technology and may soon be used in the
medication administration cycle. Although more expensive than bar coding for packaging, the RFID tags are reprogrammable (Wicks,
Visich, & Li, 2006) and issues associated with bar-code printing imperfections and bar-code scanner resolution can be mitigated
(Snyder, Carter, Jenkins, & Frantz, 2010). As discussed later in this chapter, RFID technologies may also be an important component
of a medication compliance system for patients.
BCMA systems help to ensure adherence to the five rights of medication administration. Whether BCMA is part of the larger EHR
or a free-standing electronic medication administration system (eMAR), bar-code technology provides a system of checks and
balances to ensure medication safety. The nurse begins by scanning his or her name badge, thereby logging in as the person
responsible for medication administration. Next, the bar code on the patient’s identification bracelet is scanned, prompting the
electronic system to pull up the medication orders. Next, the bar code on each of the medications to be administered is scanned. This
technology check ensures that the five rights of medication administration are met. If there is a discrepancy between the order and the
medication that was scanned or a contraindication for administration, an alert is generated by the system. For example, in an EHR
system with CDS, the nurse may be prompted to check the most recent laboratory results for electrolytes before administering a
potassium supplement. In a free-standing eMAR without CDS or EHR links, if the medication orders have recently been changed, the
nurse is alerted to the change. When an alert is generated, the nurse must chart the action taken in response to that alert. For example,
an early dose might need to be given if the patient is leaving the unit for a diagnostic test.
Despite the promising advances in patient safety afforded by this technology, it is not fail safe (Cochran, Jones, Brockman,
Skinner, & Hicks, 2007). Medications that are labeled individually by the in-house pharmacist increase the potential for human error if
the medication is given an incorrect bar code, such as one signifying a wrong dose or even the wrong medication. In addition, the bar-
code printers themselves may generate unreadable labels, leading to staff workarounds in the interest of saving time. Cochran et al.
make the following recommendations to reduce BCMA errors:
Purchase unit-of-use medications with manufacturer bar codes whenever possible.
Double-check all hospital-generated bar-code labels, including those for compounded injectable medications, before the product
leaves the pharmacy.
Carefully review all BCMA override reports. Address system workarounds through process change and staff education.
Minimize false-positive warnings to reduce the likelihood that staff will ignore warnings for real errors.
Ensure that an urgent need exists for all “stat” orders, as pharmacy review and advantages of bar code administration are
usually circumvented in such cases.
Establish institutional policies and procedures that can be easily implemented when products fail to scan. Processes in pharmacy
will likely be different than processes at the point of care (p. 300). Smart pump technologies are designed for safe
administration of high-hazard drugs and to reduce adverse drug events during intravenous medication administration. Smart
pumps have software that is programmed to reflect the facility’s infusion parameters and a drug library that compares normal
dosing rates with those programmed into the pump. Discrepancies generate an alarm alerting the clinician to a safety issue. A
soft alarm can typically be overridden by a clinician at the bedside, but a hard alarm requires the clinician to reprogram the
pump so that the dosing falls within the facility’s intravenous administration guidelines for the drug to be infused. All alarms
generated by the smart pump are tracked along with the clinician’s responses to them (Dulak, 2005; UAB, 2013). Smart pumps
can be seamlessly integrated into BCMA systems, and data can be fed directly into the EHR. The IHI recommends the
following steps to ensure safe implementation of smart pump technology:
Prior to deploying these pumps, standardize concentrations within the hospital. Asking the nurse to choose among several
concentrations increases the risk of selection error.
Prior to deploying these pumps, standardize dosing units for a given drug (for example, agree to always dose nitroglycerin in
terms of mcg/min or mcg/kg/min, but not both). Asking the nurse to choose among several dosing units increases the risk of
selection error.
Prior to deploying these pumps, standardize drug nomenclature (for example, agree to always use the term KCl, but not
potassium chloride, K, pot chloride, or others). Asking the nurse to remember and choose among several possible drug names
increases the risk of selection error.
Perform a failure modes and effects analysis on the deployment of these devices.
Ensure that the concentrations, dose units, and nomenclature used in the pump are consistent with that used on the medication
administration record (MAR), the pharmacy computer system, and the EHR.
Meet with all relevant clinicians to come to agreement on the proper upper and lower hard and soft dose limits.
Monitor overrides of alerts to assess whether the alerts have been properly configured or whether additional quality intervention
is required.
Be sure the “smart” feature is utilized in all parts of the hospital. If the pump is set up volumetrically in the operating room but
the “smart” feature is used in the ICU, an error may occur if the pump is not properly reprogrammed.
Be sure there are upper and lower dose limits for bolus doses, when applicable.
Engage the services of a human factors engineer to identify new opportunities for failure when the pumps are deployed.
Identify a procedure for the staff to follow in the event a drug that is not in the library must be given or when its concentration is
not standard.
Deploy the pump in all areas of the hospital. If a different pump is used on one floor and the patient is later transferred, this will
create new opportunities for failure. Also, there may be incorrect assumptions about the technology available to a given floor or
patient.
Consider using “smart” technology for syringe pumps as well as large-volume infusion devices (IHI, 2012, para. 7).
CDS can enhance the medication administration cycle by promoting safety and improving patient outcomes. Clinical decision
making is guided by targeted information delivery ensuring that the five rights of CDS are implemented: the right information
provided to the right person in the right format through the right channel at the right time in workflow. For example, during medication
selection, a CDS helps a clinician select an appropriate medication based on client data, such as clinical condition, weight, renal
function, concurrent medications, and cost. This system ensures that the order is complete by performing checks for drug interactions,
duplications, or allergy contraindications and ensures the right dose and right route are specified. During the verification and
dispensing phase of the medication administration cycle, the CDS provides double checks for interactions, allergies, and appropriate
dose orders. Consideration is also given to potential infusion pump programming issues, incompatibilities during infusion, and proper
notation and dispensing when portions of a dose must be wasted. During the administration phase, the CDS assists with patient
identification and current assessment parameters (i.e., blood pressure, glucose level) that may contraindicate the use of the medication
at that point in time. In addition, checks for interactions with foods or other medications and timing and monitoring guidelines are
provided to the clinician administering the medication. The CDS has patient education guidelines and printable handouts to assist
clinicians in educating patients about their medications. The monitoring functions of the CDS provide a structured data reporting
system to track side effects and adverse events across the population (Healthcare Information and Management Systems Society
[HIMSS], 2009a).
Several promising technologies may become available in the future to assist patients with medication compliance after discharge.
For example, eMedonline collects patient medication compliance data by scanning package bar codes or RFID medication tags and
using personal digital assistant or smartphone technology to send compliance data to the server. Clinicians review the medication
compliance data and provide education and feedback to patients to increase their compliance with proper medication administration
(eMedonline, n.d.). The SIMpill Medication Adherence System uses Web-based technology to monitor patient compliance and provide
reminders about taking medications or refilling prescriptions by sending text messages to the patient or caregivers (SIMpill, 2008).
Caps of pill bottles may contain RFID tags that monitor and collect data on when the bottle is opened, or that contain flashing time
reminders when a dose is due (Blankenhorn, 2010). Smart inhalers track asthma medication compliance using a microprocessor that
records and stores medication compliance. They may also include visual and audio reminders to use the inhaler (Nexus 6, 2010). These
are just a sampling of the newer technologies for medication adherence; more are expected to emerge in the future.
Additional Technologies for Patient Safety
CDS systems have safety uses beyond the medication administration cycle. The robust data collection and data management functions
help to ensure quality approaches to patient health challenges based on research evidence and clinical guidelines. A CDS may also
ensure cost-effectiveness by alerting clinicians to duplicate testing orders, or by suggesting the most cost-effective diagnostic test
based on specific patient data (HIMSS, 2009b). Consider this description of the features of a CDS based on screen captures performed
by a CDS system:
The patient is a 75-year-old male with coronary artery disease (CAD), diabetes mellitus (DM), and elevated creatine kinase
(CK). Assessment prompts and reminders on the screen for this patient include: no recent LDL test; BP is above goal; patient
is due for Pneumovax and influenza vaccines; patient is a current smoker, not thinking of quitting, last counseled with date;
patient is overweight; patient is due for eye and ear checks. The patient management prompts include:
Lipid Management: “No Recent LDL Management” is printed red with a series of check boxes presenting choices to the
clinician:
Order lipid panel now
Order lipid panel with direct LDL now
Print instructions for fasting lipid panel (link)
Print orders for outside lab request for lipid panel testing (link)
BP management: BP is above goal average over last 2 visits; goal is 130/80
Choices on checkboxes: Start another antihypertensive (“help me choose”) link
Series of links listing current medications with opportunities to adjust each
Order Chem 7 now or order Chem 7 in (drop-down menu for timing of order)
Suggestions for referrals include: Refer to nutritionist
Refer to cardiac rehabilitation (“help me choose” link) Refer to BP specialist (“help me choose” link)
Prompts for patient education handouts include: Print “control high blood pressure” link
Print DASH diet instructions link
Print exercise prescription (White, Shiffman, Middleton, & Cabán, 2008)
The prompts and instructions provided to the clinician by the system in this example are detailed and easy to navigate. As the
example suggests, implementation of a CDS has the potential to optimize care by ensuring that all of the details of a patient’s health
issues are presented to the clinician for management, thereby promoting individualized approaches to the total health of the patient
based on best available evidence and clinical guidelines (HealthIT.gov, n.d.).
RFID technologies have both supply chain and patient care applications to patient safety. An RFID system contains a tag affixed to
an object or to a person that functions as a radiofrequency transponder and provides a unique identification code, a reader that receives
and decodes the information contained on the tag, and an antenna that transmits the information between the tag and the reader. When
RFID tags are embedded in patient identification bracelets, they can help with patient tracking during procedures and testing or
function as part of the EHR communicating pertinent information to clinicians at the bedside. RFIDs may be part of the medication
administration process, replacing barcode technologies. They can be used to track medical supplies and equipment, thereby reducing
staff time in locating such items. They may also be embedded into surgical supplies to automate supply-counting procedures, thereby
reducing the likelihood that sponges or tools will be erroneously left in a patient. RFIDs may also reduce the likelihood of wrong-
patient, wrong-site surgical procedures (Revere, Black, & Zalila, 2010). RFIDs used in the medication supply chain protect patients by
reducing the potential that a counterfeit medication might be inadvertently introduced into the supply, and by providing for efficient
medication recalls. Potential terrorist manipulation of the medication supply is also thwarted by RFID supply chain tracking
technology. Blood and blood products can be efficiently tracked by RFIDs because specialized tags can detect temperature fluctuations
and, therefore, ensure that the blood or blood product was stored at the optimal temperature for safe administration (Wicks et al.,
2006).
Smart rooms are being tested for wider use in healthcare facilities. As a caregiver enters the room, the RFID tag on his or her
name badge announces to the patient on a monitor (typically mounted on the wall in the patient’s line of sight) exactly who has entered
the room and triggers “need to know” data based on caregiver status to be displayed on the monitor in the room. For example, when a
dietary aide enters the room, only dietary information is displayed; when a physician or nurse enters, all of the pertinent medical data
from the EHR is available. Clinicians can review patient data in real time and chart care at the bedside using touch-screen technology,
thereby increasing productivity (Cronin, 2010). Some smart room technologies include workflow algorithms to alert clinicians as they
enter the room about procedures that need to be implemented for the patient and can track individual clinician efficiency and
effectiveness by aggregating data over time (Sharbaugh & Boroch, 2010).
New technologies to improve patient monitoring include wearable devices and wireless area networks, variously called “body area
networks” or “patient area networks.” The technologies provide the ability to wear a small unobtrusive monitor that collects and
transmits physiologic data via a cell phone to a server for clinician review. Although most of these technologies are designed for
monitoring patients with chronic diseases, they also have safety implications because they help to identify early warning physiologic
signs of impeding serious health events (California Healthcare Foundation, 2007). A wireless chip on a disposable Band-Aid with a 5-
to 7-day battery promises to be able to monitor the patient’s heart rate and electrocardiogram, blood glucose, blood pH, and blood
pressure, allowing for the collection of important clinical data outside the hospital (Miller, 2008). Wearable stress-sensing monitors
detect electrical changes in the skin that may signal increased stress in autistic children who are unable to communicate an impending
crisis; caregivers are alerted to the potential crisis via wireless transmission and can intervene to reduce the stress and prevent the crisis
(Murph, 2010). Several new technologies promise to aid in early detection of falls in the elderly, including a wearable pendant that
triggers a personal emergency response system (Aging in Place Technology Watch, 2012) and smart slippers with pressure sensors in
the soles that transmit movement data wirelessly to a remote monitoring site (Mobihealthnews, 2009).
Robotics technologies are also being increasingly tested for safety and efficiency uses. Robotics has been used in minimally
invasive surgery for some time; however, newer devices are including haptic (tactile) feedback to the surgeon, thereby increasing the
sense of reality during the procedure and reducing the potential for unsafe manipulation (June, 2010). A robot designed to assist with
patient lifting promises increased safety for both patients and clinicians (Melanson, 2010). Finally, laser-guided robots are performing
such routine functions as emptying and disposing of trash, cleaning rooms, delivering supplies and meals, and dispensing drugs
(Savoy, 2010).
Role of the Nurse Informaticist
The human side of patient safety is paramount. As technologies that can help to reduce errors and increase safety are integrated into
caregiving activities, healthcare professionals must also improve their ability to use and manage these technologies. Therefore, not
only must the technology be scrutinized and tested routinely, but the users must also be maintained and nurtured so that they are able
to use the tools to the patient’s benefit, avoiding harm and keeping the patient safe. Even the best CDS systems can contribute to
mistakes by providing meaningless or harmful information. Nurse informaticists and the IT team in the facility must ensure that all
systems are properly configured and maintained. They should routinely monitor and check these systems while making sure that their
human potential—that is, the users—is capable of using the systems accurately to avoid errors. A technology and its user can never be
left to their own devices.
Human inputting activities must focus on patient safety to raise the appropriate issues and sound out solutions. Nurse informaticists
must be involved in all stages of the system development life cycle, while maintaining a focus on safety. Safety concerns and remedies
need to be analyzed, synthesized, and integrated throughout the system development life cycle to have a robust tool that provides
meaningful information and enhances patient care while preventing errors and promoting patient safety. According to Effken and
Carty (2002), “Creating a safe patient environment is a very complex issue that will require the combined knowledge and skill of
clinical informaticists, informatics faculty, researchers, and system designers” (para. 16). The Research Brief describes the results of a
survey on the impact of nurse informaticists on patient safety.
Research Brief
In 2009, HIMSS conducted an Informatics Nurse Impact Survey sponsored by McKesson (HIMSS, 2009). This Webbased survey yielded 432
acceptable responses over a 2-month period from December 2008 to February 2009.
One of the areas assessed was “value and impact of informatics nurse,” on a scale of 1 to 7, with 7 being the highest rating:
Respondents believe that informatics nurses involved in system analysis, design, selection, implementation and optimization of IT have the
greatest impact on patient safety (6.21), workflow (6.17) and user/clinician acceptance (6.15). The area with the least impact was integration
with other systems (6.03). These findings suggest the informatics nurse is a driver of quality of care and enhanced patient safety within their
organization. (p. 2)
This demonstrates the belief that nurse informaticists can greatly improve patient safety. The nurse executives who responded rated the positive
impact of nurse informaticists on patient safety at 6.36 out of 7.
In their conclusion, the researchers stated that:
The role of informatics nurses is not limited to IT; this research also suggests that informatics nurses play an instrumental role with regard to
patient safety, change management and usability of systems as evidenced by their impact on quality outcomes, workflow, and user
acceptance. These additional areas highlight the value of informatics nurses—their expertise truly translates to the adoption of more effective,
higher quality clinical applications in healthcare organizations. (p. 11)
Source: The full article appears in HIMSS. (2009). Informatics nurse impact survey sponsored by McKesson.
http://www.himss.org/files/HIMSSorg/content/files/HIMSS2009NursingInformaticsImpactSurveyFullResults
Summary
Patient safety is an important and ubiquitous issue in health care. This chapter explored the characteristics of a safety culture and technologies designed
to promote patient safety. The need to evaluate errors carefully to determine why and how they occurred and how workflow processes might be changed
to prevent future errors of the same type was emphasized. Technology is changing rapidly, and the culture of sharing related to technology
implementation, error reporting, and troubleshooting should prompt continuous process improvements. The key for organizations is to invest in their
users and choose wisely so that the technologies they are adopting will be interoperable and easily upgradable as technologies and safety practices
evolve.
Organizations must make a commitment to a safety culture in which everyone at every level is committed to patient safety at every
moment. In an ideal world, everyone would first stop and think “Is this safe?” before every action, workarounds would not occur, and
everyone would embrace rather than resist the technologies and workflow processes designed to promote patient safety. Table 16-1
provides a list of websites to watch for updates on patient safety technologies.
TABLE 16-16 PATIENT SAFETY WEBSITES
TITLE URL
AHRQ Patient Safety Network http://www.psnet.ahrq.gov/primerHome.aspx
National Patient Safety Foundation http://www.npsf.org/
National Center for Patient Safety http://www.patientsafety.va.gov/
Institute for Healthcare
Improvement http://www.ihi.org/explore/patientsafety/Pages/default.aspx
Center for Patient Safety http://www.centerforpatientsafety.org/
QSEN Institute (Quality and Safety
Education for Nurses) http://qsen.org/
THOUGHT-PROVOKING
QUESTIONS
1. What are the current patient safety characteristics of your organizational culture? Identify at least three aspects of your culture that need to be
changed with regard to patient safety, and suggest strategies for change.
2. Describe a current technology that you use in patient care that would benefit from human factors engineering concepts. What are some ways this
technology should be improved?
3. Identify a workaround that you have used and analyze why you chose this risk-taking behavior over behavior that conforms to a safety culture.
References
Agency for Healthcare Research and Quality (AHRQ). (2012). Patient safety primer: Safety culture. http://psnet.ahrq.gov/primer.aspx?primerID=5
Aging in Place Technology Watch. (2012). New more accurate fall detector helps seniors age in place safely.
http://www.ageinplacetech.com/pressrelease/new-more-accurate-fall-detectorhelps-seniors-age-place-safely
Bates, D., & Gawande, A. (2003). Improving safety with information technology. New England Journal of Medicine, 348, 2526–2534.
Blankenhorn, D. (2010). Can better tools overcome the medical compliance crazy? http://www.zdnet.com/blog/healthcare/can-better-tools-overcome-
the-medical-compliance-crazy/3925
California Healthcare Foundation. (2007). Healthcare unplugged: The evolving role of wireless technology. Retrieved November 2010 from
http://www.chcf.org/~/media/Files/PDF/H/PDF%20HealthCareUnpluggedTheRoleOfWireless
Cochran, C., Jones, K., Brockman, J., Skinner, A., & Hicks, R. (2007). Errors prevented by and associated with bar-code administration systems. The
Joint Commission Journal on Quality and Patient Safety, 33(5), 293–301. http://www.scribd.com/doc/12844778/Errors-Prevented-by-and-
Associated-With-BCMA
Cohen, M. (2002). Bar code labeling for drug products. Proceedings of the Food and Drug Administration’s July 26, 2002, public meeting.
http://www.ismp.org/pressroom/viewpoints/FdaBarCoding.asp
Cronin, M. (2010). SmartRooms from IBM connect hospital staff to patient data.
http://www.cerner.com/uploadedFiles/Content/Solutions/_White_Papers/Medical_Devices/NCH_Smart_Room_Whitepaper
Dulak, S. (2005). Technology today: Smart IV pumps. http://www.modernmedicine.com/modernmedicine/article/articleDetail.jsp?id=254828
Ebben, S., Gieras, I., & Gosbee, L. (2008). Harnessing hospital purchase power to design safe care delivery. Biomedical Instrumentation & Technology,
42(4), 326–331. Retrieved from ProQuest Nursing & Allied Health Source (Document ID: 1548954831).
Effken, J., & Carty, B. (2002). The era of patient safety: Implications for nursing informatics curricula. Journal of the American Medical Informatics
Association, 9(6 suppl 1). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC419434/
eMedonline. (n.d.). How eMedonline works. Retrieved November 2010 from http://www.emedonline.com/about.asp?topic=howitworks
Harrison, J., & Daly, M. (2009). Leveraging health information technology to improve patient safety. Public Administration and Management, 14(1),
218–237. Retrieved from ABI/INFORM Global (Document ID: 1685699891).
Healthcare Information and Management Systems Society (HIMSS). (2009a). Approaching CDS in medication management. Retrieved November 2010
from http://healthit.ahrq.gov/images/mar09_cds_book_chapter/CDS_MedMgmnt_ch_1_sec_3_applying_CDS.htm
Healthcare Information and Management Systems Society (HIMSS). (2009b). Clinical decision support (CDS) fact sheet. Retrieved November 2010
from http://www.himss.org/content/files/CDSFactSheet3-17-09
HealthIT.gov. (n.d.). CDS Implementation. http://www.healthit.gov/policy-researchersimplementers/cds-implementation
Institute of Medicine (IOM). (2000). To err is human: Building a safer health system. L. Kohn, Corrigan, & M. Donaldson (Eds.). Washington, DC:
Committee on Quality of Health Care in America, National Academies Press. http://psnet.ahrq.gov/resource.aspx?resourceID=1579
Institute of Medicine (IOM). (2001). Crossing the quality chasm: A new health system for the 21st century. Committee on Quality of Health Care in
America, Institute of Medicine. Washington, DC: National Academies Press. http://psnet.ahrq.gov/resource.aspx?resourceID=1564
Institute for Healthcare Improvement (IHI). (2011a). Develop a culture of safety.
http://www.ihi.org/knowledge/Pages/Changes/DevelopaCultureofSafety.aspx
Institute for Healthcare Improvement (IHI). (2011b). Failure modes and effects analysis tool.
http://www.ihi.org/knowledge/Pages/Tools/FailureModesandEffectsAnalysisTool.aspx
Institute for Healthcare Improvement (IHI). (2012). Reduce adverse drug events (ADES) involving intravenous medications: Implement smart infusion
pumps. http://www.ihi.org/knowledge/Pages/Changes/ReduceAdverseDrugEventsInvolvingIntravenousMedications.aspx
Institute for Safe Medication Practices. (2007, April 19). Smart pumps are not smart on their own.
http://ismp.org/Newsletters/acutecare/articles/20070419.asp
Institute for Safe Medication Practices. (2008). Guidance on the interdisciplinary safe use of automated dispensing cabinets.
http://www.ismp.org/tools/guidelines/ADC_Guidelines_Final
Joint Commission. (2008). Sentinel event alert #42: Safely implementing health information and converging technologies.
http://www.jcrinc.com/Sentinel-Event-Alert-42/
June, L. (2010). Sofie surgical robot gives haptic feedback for a more humane touch. http://www.engadget.com/2010/10/11/sofie-surgical-robot-gives-
haptic-feedback-for-a-more/
LeapFrog Group. (2008). Fact sheet: Computerized physician order entry. http://www.leapfroggroup.org/media/file/FactSheet_CPOE
Manor, P. (2010). CPOE: Strategies for success. Nursing Management, 41(5), 18. Retrieved from ABI/INFORM Global (Document ID: 2044925551).
Melanson, D. (2010). Yurina health care robot promises to help lift, terrify patients. http://www.engadget.com/2010/08/13/yurina-health-care-robot-
promises-to-help-lift-terrify-patients/
Miller, P. (2008). Wireless chip on a Band-Aid to monitor patients from home. http://www.engadget.com/2008/02/05/wireless-chip-on-a-band-aid-to-
monitor-patients-from-home/
Mobihealthnews. (2009). AT&T develops “smart slippers” for fall prevention. http://mobihealthnews.com/5675/att-develops-smart-slippers-for-fall-
prevention/
Murph, D. (2010). Affectiva’s Q Sensor wristband monitors and logs stress levels, might bring back the snap bracelet.
http://www.engadget.com/2010/11/02/affectivas-q-sensor-wristbandmonitors-and-logs-stress-levels/
National Patient Safety Foundation. (2013). Definitions and hot topics. http://www.npsf.org/for-healthcare-professionals/resource-center/definitions-and-
hot-topics/
Nexus 6. (2010). What are smart inhalers? http://www.smartinhaler.com/
Revere, L., Black, K., & Zalila, F. (2010). RFIDs can improve the patient care supply chain. Hospital Topics, 88(1), 26–31. Retrieved from Health
Module (Document ID: 2119405721).
Savoy, V. (2010). Robots to invade Scottish hospital, pose as “workers.” http://www.engadget.com/2010/06/21/robots-to-invade-scottish-hospital-pose-
as-workers/
Sharbaugh, D., & Boroch, M. (2010). Hospital smart rooms are ready for rollout. http://www.buildings.com/tabid/3413/ArticleID/9675/Default.aspx
SIMpill. (2008). The SIMpill medication adherence solution. http://www.simpill.com/thesimplesolution.html
Snyder, M., Carter, A., Jenkins, K., & Frantz, C. (2010). Patient misidentifications caused by errors in standard barcode technology. Clinical Chemistry,
56(10), 1554–1561.
University of Alabama at Birmingham (UAB). (2013). UAB Medicine: The connected hospital. http://www.uabmedicine.org/news/news-nursing-the-
connected-hospital
Wachter, R. (2010). Patient safety at ten: Unmistakable progress, troubling gaps. Health Affairs, 29(1), 165–173. Retrieved from ABI/INFORM Global
(Document ID: 194983018).
White, J., Shiffman, R., Middleton, B., & Cabán, T. (2008). A national web conference on using clinical decision support to make informed patient care
decisions. Slide 63: Partners CDS services: CAD/DM smart, Form Slides 63–65. Presented at Agency for Healthcare Research and Quality,
September 19, 2008. Retrieved November 2010 from http://healthit.ahrq.gov/images/sep08cdswebconference/textmostly/
Wicks, A., Visich, J., & Li, S. (2006). Radio frequency identification applications in healthcare. International Journal of Healthcare Technology and
Management, 7(6), 522–540.
Williams, J. (2009). Biomeds’ increased involvement improves processes, patient safety. Biomedical Instrumentation & Technology, 43(2), 121–3.
Retrieved from ProQuest Nursing & Allied Health Source (Document ID: 1692747971).
Chapter 17
Supporting Consumer Information and Education Needs
Kathleen Mastrian and Dee McGonigle
OBJECTIVES
1. Define health literacy and e-health.
2. Explore various technology-based approaches to consumer health education.
3. Identify barriers to use of technology and issues associated with healthrelated consumer information.
4. Imagine future approaches to technology-supported consumer health information.
Key Terms
Blog
Digital divide
Domain name
E-brochure
E-health
eHealth Initiative
Empowerment
Gray gap
Health literacy
HONcode
Interactive technologies
Know–do gap
Static medium
Trust-e
Voice recognition
Web quest
Weblog
Introduction
Imagine that you have decided to take up running as your preferred form of exercise in a quest to get in shape. You start slowly by
running a half mile and walking a half mile. You gradually build up your endurance and find yourself running nearly every day for
longer distances and longer periods of time. But then you notice a nagging pain first in your right hip; over a few weeks, it gradually
spreads to the center of your right buttocks and then down your right leg. You try rest and heat, but nothing seems to help. You visit
your doctor, and she indicates that you have developed piriformis syndrome and prescribes a series of stretching exercises, ice to the
involved area, and rest. You are intrigued by the diagnosis. Upon your return home, you log on to the Internet and begin a search for
information about piriformis syndrome. When you type the words into your favorite search engine, you get 484,000 results in response
to your query.
Your use of the Internet to seek health information mirrors the behavior of many consumers, who are increasingly relying on the
Internet for health-related information. The challenge for consumers and healthcare professionals alike is the proliferation of
information on the Internet and the need to learn how to recognize when information is accurate and meaningful to the situation at
hand.
This chapter explores consumer information and education needs and considers how technology may help to meet those needs, yet
at the same time create everincreasing demands for health-related information. It begins with a discussion of health literacy, e-health,
and health education and information needs and explores various approaches by healthcare providers to using technology to promote
health literacy. Also examined is the use of games, Web quests, and simulations as means of increasing health literacy among the
school-age population. Issues associated with the credibility of Web-based information and barriers to access and use are discussed.
Finally, future trends related to technology-supported consumer information are explored.
Consumer Demand for Information
This is the Knowledge Age; many people want to be in the know. People demand news and information, and they want immediate
results and unlimited access. This is increasingly true with health information. More and more people, in a trend known as consumer
empowerment, are interested in taking control of their health and are not satisfied being dependent on a healthcare provider to supply
them with information.
The Pew Internet and American Life Project survey report of 2013 (Fox, 2013) indicates that 8 in 10 (comparable to the numbers
in previous surveys conducted in 2006 and 2011) Americans who are online have searched for health information. The most frequent
health topic searches (69%) are related to a specific disease or medical problem that the searcher or a member of the family is
experiencing. Other frequent topics of health-related searches are weight, diet, and exercise (60%) and health indicators such as blood
parameters or sleep patterns (33%). The 2011 survey (n = 3,001) reports that consumers also searched for information on food (29%)
and drug safety (24%) (Fox, 2011). A 2006 Pew Internet survey tracked 8 million searches for health information by American adults
in a single day and suggested that most begin their searches with a search engine. The impacts of the searches were reported as both
positive and negative. These impacts ranged from affecting decisions about their care (58%), changing their approaches to overall
health maintenance (55%), and providing material for posing questions to healthcare providers (54%), to feeling overwhelmed (25%)
or confused (18%) by materials they found online about their health (Fox, 2006).
It is important to note that these surveys are limited to those individuals who are online and do not reflect the health information
needs or demands of those persons who are not online. Digital divide is the term used to describe the gap between those who have and
those who do not have access to online information. Nurses and healthcare providers need to be aware of the various components of
the digital divide to ensure that patients and clients are receiving the health information they need in a format that they are interested in
and can comprehend. Notably, persons with chronic diseases are less likely to have Internet connectivity. Fox and Purcell (2010)
explain the disparity in that having a chronic disease is associated with increased age, level of education, ethnicity, and income—all
factors also associated with the digital divide. Persons living with a chronic disease who have Internet access are likely to use the
Internet for blogging and online discussion forums, activities popularly referred to as peer-to-peer support.
Missen and Cook (2007) discuss the potential impact that technology-based health information dissemination can have on the
know–do gap in developing countries. The know–do gap reflects the fact that solutions to global health problems exist but are not
implemented in a timely fashion because of the lack of access to important health information. The Internet connections in developing
countries are widely scattered and may not be efficient or sufficient for viewing healthcare information. Missen and Cook describe the
use of a freestanding hard drive loaded with hundreds of CDs of health-related information in a webpage format that responds to a
search command. This is a great example of providing technologies that work with the constraints of the situation. Another example of
addressing the digital divide is the growing number of health-related websites that support a Spanish language format.
Health Literacy and Health Initiatives
The goal of health literacy for all is one that is widely embraced in many sectors of health care; it was a major goal of Healthy People
2010, and is being continued in the health communication and health information technology objective of Healthy People 2020 (Office
of Disease Prevention and Health Promotion & U.S. Department of Health and Human Services, 2009). Clinicians who have been
practicing for some time recognize that informed patients have better outcomes and pay more attention to their overall health and
changes in their health than those who are poorly informed. Some of the earliest formally developed patient education programs,
which included postoperative teaching, diabetes education, cardiac rehabilitation, and diet education, were implemented in response to
research that suggested the positive impact of patient education on health outcomes and satisfaction with care. Glassman (2008)
updated the National Network Libraries of Medicine webpage on health literacy (http://nnlm.gov/outreach/consumer/hlthlit.html). She
concluded from the research on the economic impact of health literacy that those persons with low health literacy have less ability to
manage a chronic illness properly and tend to use more healthcare services than those who are more literate. In addition, she used
results of health research to demonstrate the impact of low health literacy and the incidence of disease.
The site states that “Health Literacy is defined in the Institute of Medicine report Health Literacy: A Prescription to End Confusion
as ‘The degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed
to make appropriate health decisions’” (Glassman, 2008, para. 2). For example, healthcare providers depend on a patient’s ability to
understand and follow directions associated with preparation for surgery or taking medications. It is also assumed, sometimes
erroneously, that people will correctly interpret symptoms of a serious illness and act appropriately. The ability to locate and evaluate
health information for credibility and quality, to analyze the various risks and benefits of treatments, and to calculate dosages and
interpret test results are among the tasks Glassman identifies as essential for health literacy. Other important and less easily learned
health literacy skills are the ability to negotiate complex healthcare environments and understand the economics of payment for
services. Parker, Ratzan, and Lurie (2003) estimated that at least one third of all Americans have health literacy problems and lament
that in a time-is-money economic climate, healthcare practitioners are not always reimbursed for patient education activities. This is
still true today.
The eHealth Initiative was developed to address the growing need for managing health information and to promote technology as
a means of improving health information exchange, health literacy, and healthcare delivery. The eHealth Initiative website provides
more information (http://www.ehealthinitiative.org/). Although the scope of the eHealth Initiative goes beyond health literacy, a major
goal continues to be empowering consumers to understand their health needs better and to take action appropriate to those needs. Poor
interoperability among healthcare systems and failure to embrace national data standards for health care continue to be identified as
barriers to the eHealth Initiative. Further, concerns about privacy and security of information and the failure to invest appropriately in
technology have slowed the development of this important initiative. Several states developed viable health information networks as
part of the eHealth Initiative: the Utah Health Information Network (http://www.uhin.org) and the Vermont Health Information
Technology Plan (http://hcr.vermont.gov/sites/hcr/files/pdfs/vermont_health_it_plan ) are examples of attempts to implement e-
health initiatives. The Centers for Disease Control and Prevention (CDC, 2012) maintains an interactive map of the United States that
provides access to health literacy and e-health initiatives by state (http://www.cdc.gov/healthliteracy/).
Healthcare Organization Approaches to Education
Healthcare organizations (HCOs) use a wide variety of approaches and tools to promote patient education and health literacy.
Although the old standby for disseminating information is the paper-based flyer, some HCOs are recognizing that today’s consumers
are more attracted to a dynamic rather than static medium. In addition, the cost of designing and printing pamphlets and flyers
becomes prohibitive when one considers the rapidity of change of information; the brochure may be outdated almost as soon as it is
printed. One approach to deal with these issues is to have patient education information stored electronically so that changes can be
made as needed or information can be better tailored to the specific patient situation and then printed out and reviewed with the
patient.
Another old standby approach that is still widely used is the group education class. These classes initially were developed to help
people manage chronic health problems (e.g., diabetes) and were typically scheduled while people were hospitalized. Now, many
HCOs also sponsor health promotion education classes as a way of marketing their facilities and showcasing some of their expert
practitioners.
The movement from static to dynamic presentations began in many HCOs as DVDs and videotapes that were shown in groups or
broadcast on demand over dedicated channels via television in patients’ rooms. HCOs are now also taking advantage of the fact that
patients and families are captive audiences in waiting rooms by promoting education via pamphlet distribution, health promotion
programs broadcast on television, and health information kiosks in those locations. The kiosks are typically computer stations and
often contain a variety of self-assessment tools (especially those related to risks for diabetes, heart disease, or cancer) and searchable
pages of information about specific health conditions. The self-assessment tools represent yet another step forward in technological
support for education: In addition to being dynamic, the kiosk is interactive. On the assessment page, the user is asked to respond to a
series of questions and then the health risk is calculated by the computer program. One caution, however, is that just because the
information is made available, it does not mean that people will participate or that they will understand what they have experienced.
Issues related to the level of health literacy, the digital divide, and the gray gap still exist in these situations.
Many HCOs have invested time and money in developing interactive websites and believe that Web presence is a critical
marketing strategy. Sternberg (2002) suggests that many websites begin as an e-brochure and progress through various stages to reach
a true e-care status. Most offer physician search capabilities, e-newsletters, and call-center tie-ins. As with all patient education
materials, there must be a sincere commitment to keeping information current and easily accessible. Web designers must pay particular
attention to the aesthetics of the site, the ease of use, and the literacy level of those in the intended audience.
A usability study conducted by Lauterbach (2010) provides some insights into how to measure website usability. This researcher
compared the usability of the symptom checker functions of two popular websites by asking volunteers to navigate each using four
different case scenarios. Users navigated the site to find the symptom checker and then entered the symptoms and evaluated site
feedback. Users rated the ease of understanding for each site and completed a short comprehension quiz. Data were collected on
descriptions of user site preferences, user satisfaction with the sites, results of the comprehension quiz, and efficiency, which was
measured by tracking the number of webpage changes the user performed while navigating the site.
The rapid growth of electronic communication through increased use of computers and access to the Internet, particularly for
medical purposes, empowers the clinician as well as the consumer of healthcare information. The integration of information and
communication technologies (ICTs) and the growing trend of consumer empowerment have reshaped the delivery of health care. Some
HCOs have developed secure patient portals that allow patients to access their health records, including tracking laboratory results and
reviewing the records. Most HCOs, however, do not allow patients to edit these records. Many people are interested in interacting with
others who have the same or similar conditions, and some HCOs are providing the information necessary to help them connect. This
so-called peer-to-peer support is especially popular with patients who have cancer diagnoses, diabetes, and other chronic and
debilitating conditions (Lober & Flowers, 2011).
Research Brief
Case studies of educational videos used on six hospital websites were the focus of this qualitative study by Huang (2009). This researcher used both
within-case and cross-case analyses to describe rationales for developing and using e-health videos. He identified six main reasons for implementing
e-health videos:
1. The proliferation of high-speed Internet access has made online video a well-accepted culture in the United States.
2. Many online visitors, especially young people, like to watch videos rather than read lengthy articles.
3. Videos can draw online visitors’ attention, thereby attracting online traffic and further attracting visitors to the hosting hospital.
4. A video, when well produced, is worth more than a thousand words.
5. Videos can relate not only to online visitors’ minds but also, and more importantly, to their hearts to build trust and drive their decision making.
5. Videos empower and inform the visitors outside of the hospital visit time, and such self-driven homework is beneficial to both the visitor and
the hospital (pp. 67–68).
Source: The full article appears in Huang, E. (2009). Six cases of e-health videos on hospital web sites. E-Service Journal, 6(3), 56–72. Retrieved
from ABI/INFORM Global (Document ID: 1945312291).
Some HCOs are using social media as health education tools to promote actual engagement of audiences rather than as means of
one-way messaging. Neiger, Thackeray, Burton, Giraud-Carrier, and Fagen (2013) suggested “that the use of social media in health
promotion must lead to engagement between the health promotion organization and its audience members, that engagement must
provide mutual benefit, and that an engagement hierarchy culminates in program involvement with audience members in the form of
partnership or participation (as recipients of program services)” (p. 158). The CDC (2011) has an excellent social media tool kit that
can be used by health educators to guide the planning and implementation of social media strategies for health promotion. This tool kit
http://www.cdc.gov/healthliteracy/
can be accessed at the following website: http://www.cdc.gov/socialmedia/Tools/guidelines/pdf/SocialMediaToolkit_BM .
Promoting Health Literacy in School-Aged Children
Promoting health literacy in school-aged children presents special challenges to health educators. There is wide agreement that
childhood obesity is a serious and growing issue, which is related not only to poor choice of foods, but also to the sedentary lifestyles
promoted by video games and television. In addition, the time once devoted to health and physical education programs in schools has
given way to more time spent on core subjects, such as math and science.
The Children’s Nutrition Research Center responded early to these challenges by supporting the development of nutrition
education programs as interactive computer games, video games, and cartoons referred to as “edutainment” (Flores, 2006). These e-
health programs are developed specifically to appeal to the generational (highly connected and computer literate) and cultural needs of
this group. Flores describes the Family Web project, which uses comic strips to impart nutrition information, and Squires Quest, where
the students earn points by choosing fruits and vegetables to fight the snakes and moles that are trying to destroy the healthy foods in
the Kingdom of SALot. These are great examples of health education programs that are designed to appeal to this connected
generation of learners and their intuitive ability to use interactive technologies.
Donovan (2005) describes an interdisciplinary Web quest designed to appeal to older school-aged children. The quest is
interdisciplinary, in that it requires reading comprehension, critical thinking, presentation, and writing; thus core skills and health
literacy skills are learned in a single assignment. Students are directed to the Web to search for information on the pros and cons of
low-carbohydrate diets and obesity prevention. Students learn along the way as they search for information, collect and interpret it, and
then develop a presentation and final paper.
The Cancer Game (Oda & Kristula, n.d.) was developed by a young man taking a college class on Macromedia software and who
had previously undergone a bone marrow transplant. Subsequently, he and a professor collaborated and expanded the project to its
present form. The game is designed as an arcade-style video game for cancer patients to relieve stress by visualizing the fighting of
cancer cells. Although cancer victims of any age can access and play the game, it has a special appeal to children and adolescents. Find
the game here: http://www.cancergame.org/. Similarly, Ben’s Game (http://www.makewish.org/site/pp.asp?
c=cvLRKaO4E&b=64401) is a video game designed to help relieve the stress of cancer treatment for children (Anderson & Klemm,
2008).
Games have also been designed for other conditions, such as diabetes (Glymetrix, 2009). You can access these newer games on the
Internet by typing “health games” into a search engine. Be sure to review the information presented in the game for accuracy before
you recommend it to parents and children.
Supporting Use of the Internet for Health Education
Nurses and other healthcare providers need to embrace the Internet as a source of health information for patient education and health
literacy. Patients are increasingly turning there for instant information about their health maladies. Health-related blogs (short for
weblog, an online journal) and electronic patient and parent support groups are also proliferating at an astounding rate. Clinicians need
to be prepared to arm patients with the skills required to identify credible websites. They also need to participate in the development of
well-designed, easy-to-use health education tools. Finally, they need to convince payers of the necessity of health education and the
powerful impact education has on promoting and maintaining health. Box 17-1 provides more information about health education.
BOX 17-1 CONSIDERATIONS FOR PATIENT EDUCATION
Julie A. Kenney and Ida Androwich
Nurses need to take many things into account when teaching patients. They need to assess patients’ willingness to learn, their reading ability, the
means by which they learn best, and their existing knowledge about the subject. Nurses also need to take cultural differences and language
differences into account when teaching their patients. If the nurse chooses to use an electronic method to educate the patient, digital natives (patients
who have grown up with technology) need to be taught differently than digital immigrants (those who have been forced to learn technology)
(“Educational Strategies,” 2006). Digital natives are typically born after 1982 and may also be referred to as “Generation Y.” This generation prefers
to learn using technology. The younger group learns quite well if information is presented in a format to which they are accustomed, such as an
interactive video game to introduce them to a topic. This group is also comfortable using information that they can access via their iPods and MP3
players (Maag, 2006). Those born before 1982 have learning styles that range from preferring to learn in a classroom setting to reading a book about
the topic to learning using a hands-on, interactive approach (“Educational Strategies,” 2006).
REFERENCES
Educational strategies in generational designs. (2006). Progress in Transplantation, 16(1), 8–9.
Maag, M. (2006). Pod casting and MP3 players: Emerging education technologies. CIN: Computers, Informatics, Nursing, 24(1), 9–13.
PATIENT EDUCATION WEBSITES
American Academy of Family Physicians: http://www.familydoctor.org American Cancer Society: http://www.cancer.org
American Heart Association: http://www.americanheart.org
Centers for Disease Control and Prevention: http://www.cdc.gov Krames (products to purchase): http://www.krames.com
Merck (products to purchase and free information for patients):
http://www.merckservices.com/portal/site/merckservices/&tcode=K09FA
Thomson—Educational products for nurses (requires a subscription):
http://www.micromedex.com/products/nurses and http://www.micromedex.com/products/carenotes/cn_brochure
The Health on the Net (HON) Foundation (2005) survey describes the certifications and accreditation symbols that identify trusted
health sites. The HONcode and Trust-e were identified as the two most common symbols that power users look for. The survey also
indicated that Internet users look at the domain name and frequently gravitate toward university sites (.edu), government sites (.gov),
and HCO sites (.org). Half of the survey respondents were in favor of the use of a domain name called .health to identify quality health
information websites. In contrast, Pew/Internet (2006) indicates that nearly 75% of online searchers do not check the date or the source
of information they are accessing on the Web and 3% of online health seekers report knowing someone who was harmed by following
health information found on the Web.
The U.S. National Library of Medicine and the National Institutes of Health jointly sponsor MedlinePlus, a website that has a
tutorial for learning how to evaluate health information and an electronic guide to Web surfing that is available in both English and
Spanish. This site is found at http://www.nlm.nih.gov/medlineplus/webeval/webeval.html. A similar guide explains the major things
one should evaluate when accessing health-related resources on the Web (National Center for Complementary and Alternative
Medicine, 2008) and can be accessed at http://nccam.nih.gov/health/webresources. Suggest that patients visit these sites to become
more adept at identifying whether a website is credible before they adopt the recommendations provided.
Clearly, the clinician is very important in patient education. Refer to Boxes 17-1 and 17-2 to review effective education methods
used in teaching patients and their families.
BOX 17-2 A CLINICIAN’S VIEW ON PATIENT EDUCATION
Denise D. Tyler
Knowledge dissemination in nursing practice includes sharing information with patients and families so that they understand their healthcare needs
well enough to participate in developing the plan of care, make informed decisions about their health, and ultimately comply with the plan of care,
both during hospitalization and as outpatients.
There are several effective methods for educating patients and their families. Providing one-on-one and classroom instruction are traditional and
valuable forms of education. One-on-one education is interactive and can be adjusted at any time during the process based on the needs of the
individual patient or family; it can also be supplemented by written material, videos, and Web-based learning applications. Classroom education can
be beneficial because patients and families with similar needs or problems can network, thereby enhancing the individual experience. However, the
ability to interact with each member of the group and to tailor the educational experience based on individual needs may be limited by the size and
dissimilarities of the group. Individual follow-up should be available when possible.
Paper-based education that is created, printed, and distributed by individual institutions or providers can be very effective because materials can
be distributed at any time and reviewed when the patient feels like learning. Many agencies, such as the Centers for Disease Control and Prevention,
have education for patients available on their websites. These documents can be reviewed online, or they can be printed out by healthcare providers
or patients. Organizations can also develop and distribute information and instructions specific to their policies and procedures. In addition, printed
educational material can be purchased from companies that employ experts in the subject matter and instructional design.
One of the newer sources of patient education information is the Internet. Many hospitals and healthcare organizations provide proprietary
information, such as directions, information on procedures, and instructions on what to expect during hospitalization, in this manner. Other health
organizations, such as the National Institutes of Health, provide detailed information on their websites. Clinicians should be cautious when
recommending websites to patients and families, because not all sites are reliable or valid.
Many companies that provide clinical information systems also include patient education materials linked to the clinical system via an intranet.
Thus standardized instructions that are specific to a procedure or disease process can be printed from this computer-based application. Discharge
instructions that are interdisciplinary and patient specific can often be modified via drop-down lists or selectable items that can be deleted or changed
by the clinician. This ability to modify before printing provides more consistent and individualized instruction. The computer-based generation of
instruction is preferable to free text and verbal instruction modification because it also allows the information to be linked to a coded nursing
language and, therefore, easily used for measurement and quality assurance reporting. Relevant triggers may be embedded in the clinical information
systems. For example, when a patient answers “yes” to a question about current smoking, smoking cessation information should automatically be
printed, or a trigger should remind the nurse to explore this topic with the patient and then provide the patient with preprinted information on
smoking cessation.
Integration of standardized discharge instructions and patient education into the clinical system is another way to improve the compliance and
documentation of education; it also streamlines the workflow of clinicians. Printing the information to give to the patient should be seamless to the
clinician who is charting. The format should be logical and easy to read. The more transparent the process, the more efficient the system and the
easier it is to use for the clinician. What I envision for the future is a system that “remembers” the style of learning preferred by patients and their
families, prompts the provider to print handouts, and programs the bedside computer/video education system based on previous selections and
surveys. This interactive patient and family education will be integrated into the clinical system and the patient’s personal health record.
Some providers have developed a list of credible websites and apps that are shared with patients or family members.
Recommendations for websites might include the U.S. Department of Health and Human Services–sponsored healthfinder site
(http://www.healthfinder.gov), a website dedicated to helping consumers find credible information on the Internet. Other excellent
sources of reliable information are the National Institutes of Health (http://www.nih.gov), the Centers for Disease Control and
Prevention (http://www.cdc.gov), Medline Plus (www.medlineplus.gov), NIHSeniorHealth (www.nihseniorhealth.gov), and the
National Health Information Center (http://www.health.gov/nhic). Some of the apps that might be recommended include Mayo Clinic
on Pregnancy (https://itunes.apple.com/WebObjects/MZStore.woa/wa/viewSoftware?id=656908781&mt=8), WebMD Pain Coach
(https://itunes.apple.com/us/app/webmd-pain-coach/id536303342?mt=8), and Understanding Diseases
(https://itunes.apple.com/us/app/understanding-diseases/id530900371?mt=8). These are great examples of the wealth of patient
information being developed as apps by hospitals and other health care providers.
Future Directions
Predicting future directions for technology-based health education is somewhat difficult, because one may not be able to completely
envision the technology of the future. One can predict, however, that some current technologies will be used increasingly to support
health literacy. For example, audio and video podcasts may become more commonplace in health education and be provided as free
downloads from the websites of HCOs.
Abreu, Tamura, Sipp, Keamy, and Eavey (2008) described the processes used to create patient and family surgical education video
podcasts. They began by digitally recording actual pediatric otologic surgeries, and then edited the videos, developed a script, and
finally recorded the audio. They report that the educational podcasts were developed in 8–10 hours (excluding the surgical recording
time). These podcasts are used to supplement the traditional face-to-face patient and family education sessions for pediatric otologic
surgeries.
Voice recognition software used to navigate the Web may reduce the frustration and confusion associated with attempting to spell
complex medical terms. However, the confusion and frustration may increase if the patient or client is unable to pronounce the terms.
Voice interactivity should help to reduce the digital access disparity associated with those who have limited keyboard or mouse skills.
For those persons with visual impairments, some websites may provide both audio and text information and support increased text size
for greater ease of reading (Anderson & Klemm, 2008).
Many websites associated with government and national organizations are also providing multiple-language access to health
information and decision-support tools. The multilanguage access broadens the population to whom education can be provided, and
the decision-support programs allow users to access results that are tailored to their age, risk factors, or disease state (Anderson &
Klemm, 2008).
Those individuals who are frequent e-mail users may be interested in being able to communicate with physicians and other
healthcare personnel via e-mail rather than the telephone. This idea may meet with some resistance from physicians who perceive the
e-mail correspondence as bothersome and time consuming. However, it is possible that work efficiency might also increase if patients
and their needs are screened via e-mail before an office visit. For example, as a result of an e-mail correspondence in lieu of an initial
office visit, medications could be changed or diagnostic tests could be performed before the office visit. In addition, patients could be
directed to an interactive screening form housed on a website where they would answer a series of questions that would help them
make a decision about whether they should call for an appointment, head for the emergency room, or self-manage the issue. If self-
management is the outcome of the screening tool, then the patient or caregiver could be directed to a credible website for more
information. The idea is not to interfere with or replace the face-to-face visit, but rather to supplement the physician–patient
relationship and perhaps streamline the efficiency of healthcare delivery. McCray (2005) also suggests that physicians may be resistant
to providing e-mail consultations and recommending health-related websites because of the potential for malpractice liability. There is
some evidence, however, that text message reminders delivered via a cell phone are more effective and efficient as appointment
reminders than traditional phone calls (Car, Gurol-Urganci, de Jongh, Vodopivec-Jamsek, & Atun, 2012). Similarly, in a descriptive
research study by Dudas, Pumilia, and Crocetti (2013) it was found that parents of children who recently visited an emergency
department were interested in receiving follow-up communication from healthcare providers by text messaging and/or email. A major
barrier to widespread adoption of e-mail and text messaging among American healthcare providers is the fact that reimbursement
mechanisms for electronic health care are inadequate or nonexistent.
Piette (2007) describes the use of interactive behavior change technology to improve the effectiveness of diabetes management.
The goal of the interactive behavior change technology is to improve communication between patients and healthcare providers and to
provide educational interventions that promote better disease management between visits. The combination of electronic medication
reminders, meters that track glycemic control longitudinally, and personal digital assistant–based calculators was found to support the
behavioral interventions necessary to better manage the diabetes.
As a conclusion to their study, Watson, Bell, Kvedar, and Grant (2008) caution that even though patients are part of the digital
divide (lacking access or skill in electronic communications and Internet use), one cannot assume that they will be resistant to using
other forms of technology to support health. These authors compared Internet users to non-Internet users and found that both groups
were willing to learn to use new technology to manage type 2 diabetes, including wireless communication devices for information
sharing with physicians.
Healthcare practitioners may soon embrace the use of “information prescriptions” (D’Alessandro, 2010) that direct patients and
families to credible websites, including government and HCO websites, and wikis and blogs that may help them understand their
health issues or share information with and seek support from others who have similar issues. “Information prescriptions are
prescriptions of focused, evidence-based information given to a patient at the right time to manage a health problem” (p. 81). The
National Health Service in the United Kingdom has developed an information prescription generator that can be used by providers or
the public to access Web-based health information (http://www.nhs.uk/ipg/Pages/IPStart.aspx).
Summary
It is clear that the consumer empowerment movement will continue to drive the need for access to quality health education and support
programs. In an ideal world, practitioners will design educational materials that are user friendly, culturally competent, interesting,
dynamic, and interactive, and that meet the skills, education needs, and interests of the user.
THOUGHT-PROVOKING
QUESTIONS
1. How do you envision technology enhancing patient or consumer education in your setting?
2. Formulate a plan evidencing a potent patient education episode on methicillinresistant Staphylococcus aureus. Provide a rationale for each
approach and describe a tool you would use to educate the patient and his or her family.
References
Abreu, D., Tamura, T., Sipp, J., Keamy, D., & Eavey, R. (2008). Podcasting: Contemporary patient education. Ear, Nose & Throat Journal, 87(4), 208,
http://www.nhs.uk/ipg/Pages/IPStart.aspx
210–211. Retrieved from ProQuest Nursing & Allied Health Source (Document ID: 1468588971).
Anderson, A., & Klemm, P. (2008). The Internet: Friend or foe when providing patient education? Clinical Journal of Oncology Nursing, 12(1), 55–63.
Retrieved from ProQuest Nursing & Allied Health Source (Document ID: 1430141231).
Car, J., Gurol-Urganci, I., de Jongh, T., Vodopivec-Jamsek, V., & Atun, R. (2012). Mobile phone messaging reminders for attendance at healthcare
appointments. Cochrane Database of Systematic Reviews, 7.
Centers for Disease Control and Prevention (CDC). (2011). The health communicator’s social media toolkit.
http://www.cdc.gov/socialmedia/Tools/guidelines/pdf/SocialMediaToolkit_BM
Centers for Disease Control and Prevention (CDC). (2012). Health literacy: Accurate, accessible and actionable health information for all.
http://www.cdc.gov/healthliteracy/
D’Alessandro, D. (2010). Challenges and options for patient education in the office setting. Pediatric Annals, 39(2), 78–83. Retrieved from Health
Module (Document ID: 1972816891).
Donovan, O. (2005). The carbohydrate quandary: Achieving health literacy through an interdisciplinary WebQuest. Journal of School Health, 75(9),
359–362. Retrieved from Health Module database (Document ID: 924409661).
Dudas, R. A., Pumilia, J., & Crocetti, M. (2013). Pediatric Caregiver Attitudes and Technologic Readiness Toward Electronic Follow-Up
Communication in an Urban Community Emergency Department. Telemedicine & E-Health, 19(6), 493–496. doi:10.1089/tmj.2012.0166
Flores, A. (2006). Using computer games and other media to decrease child obesity. Agricultural Research, 54(3), 8–9. Retrieved from Research Library
Core database (Document ID: 1005199991).
Fox, S. (2006). Online health search 2006. http://www.pewinternet.org/Reports/2006/Online-Health-Search-2006.aspx
Fox, S. (2011, February 1). Health topics. http://www.pewinternet.org/Reports/2011/HealthTopics.aspx
Fox, S. (2013). Health online 2013. http://pewinternet.org/Reports/2013/Health-online.aspx Fox, S., & Purcell, K. (2010). Chronic disease and the
Internet. http://www.pewinternet.org/Reports/2010/Chronic-Disease/Summary-of-Findings/Adults-living-with-chronic-diseaseare-
disproportionately-offline-in-an-online-world.aspx
Glassman, P. (2008). Health literacy. http://nnlm.gov/outreach/consumer/hlthlit.html Glymetrix Diabetes Game. (2009). http://www.diabetesgame.com/
Health on the Net (HON) Foundation. (2005). Analysis of 9th HON survey of health and medical Internet users.
http://www.hon.ch/Survey/Survey2005/res.html
Lauterbach, C. (2010). Exploring the usability of e-health websites. Usability News, 12(2).
http://psychology.wichita.edu/surl/usabilitynews/122/pdf/Usability%20News%20122%20-%20Lauterbach
Lober, W. B., & Flowers, J. L. (2011, August). Consumer empowerment in health care amid the Internet and social media. Seminars in Oncology
Nursing, 27(3), 169–182. http://dx.doi.org/10.1016/j.soncn.2011.04.002
McCray, A. (2005). Promoting health literacy. Journal of the American Medical Informatics Association, 12(2), 152–163. Retrieved from ProQuest
Nursing & Allied Health Source database (Document ID: 810410751).
Missen, C., & Cook, T. (2007). Appropriate information-communications technologies for developing countries.
http://www.who.int/bulletin/volumes/85/4/07-041475/en/index.html
National Center for Complementary and Alternative Medicine (NCCAM), National Institutes of Health. (2008). 10 things to know about evaluating
medical resources on the Web. http://nccam.nih.gov/health/webresources
Neiger, B. L., Thackeray, R., Burton, S. H., Giraud-Carrier, C. G., & Fagen, M. C. (2013). Evaluating social media’s capacity to develop engaged
audiences in health promotion settings:
Use of Twitter metrics as a case study. Health Promotion Practice, 14(2), 157–162. doi: 10.1177/1524839912469378
Oda, Y., & Kristula, D. (n.d.) The Cancer Game: A side-scrolling, arcade-style, cancer-fighting video game. http://www.cancergame.org/
Office of Disease Prevention and Health Promotion & U.S. Department of Health and Human Services. (2009). Proposed Healthy People 2020
objectives. http://www.healthypeople.gov/hp2020/Objectives/TopicAreas.aspx
Parker, R., Ratzan, C., & Lurie, N. (2003). Health literacy: A policy challenge for advancing highquality health care. Health Affairs, 22(4), 147.
Retrieved from ABI/INFORM Global database (Document ID: 376436551).
Pew/Internet. (2006). The future of the Internet II. http://news.bbc.co.uk/1/shared/bsp/hi/pdfs/22_09_2006pewsummary
Piette, J. (2007). Interactive behavior change technology to support diabetes self-management: Where do we stand? Diabetes Care, 30(10), 2425–2432.
Retrieved from Health Module database (Document ID: 1360494771).
Sternberg, D. (2002). Building on your quick wins. Marketing Health Services, 22(3), 41–43. Retrieved from ABI/INFORM Global database (Document
ID: 155769441).
Watson, A., Bell, A., Kvedar, J., & Grant, R. (2008). Reevaluating the digital divide: Current lack of Internet use is not a barrier to adoption of novel
health information technology. Diabetes Care, 31(3), 433–435. Retrieved from Health Module.
Chapter 18
Using Informatics to Promote Community/Population
Health
Margaret Ross Kraft, Ida Androwich, Kathleen Mastrian, and Dee McGonigle
OBJECTIVES
1. Provide an overview of community and population health informatics.
2. Describe informatics tools for promoting community and population health.
3. Define the roles of federal, state, and local public health agencies in the development of public health informatics.
Key Terms
Behavioral Risk Factor Surveillance System
Bioterrorism
Centers for Disease Control and Prevention (CDC)
Community risk assessment (CRA)
Crowdsourcing
Epidemiology
National Center for Public Health Informatics
National health information network
National Health and Nutrition Examination Survey
Public health
Public health informatics
Public health interventions
Regional health information exchange
Risk assessment
Social media
Suicide Prevention Community Assessment Tool
Surveillance
Surveillance data systems
Syndromic surveillance
Youth Risk Behavior Surveillance System
Introduction
In late fall of 2002, severe acute respiratory syndrome (SARS) appeared in China. By March 2003, SARS had become recognized as a
global threat. According to World Health Organization (WHO) data, more than 8,000 people from 29 countries became infected with
this previously unknown virus and more than 700 people died. By 2004, the last SARS cases were caused by laboratory-acquired
infections. Because of computerized global data collection, the potentially negative impact of a widespread global epidemic was
averted.
Many surveillance systems, loosely termed “syndromic surveillance systems,” use data that are not diagnostic of a disease but that
might indicate the early stages of an outbreak. Outbreak detection is the overriding purpose of syndromic surveillance for terrorism
preparedness. Enhanced case finding and monitoring the course and population characteristics of a recognized outbreak also are
potential benefits of syndromic surveillance. In recent years, new data have been used by public health officials to enhance
surveillance, such as patients’ chief complaints in emergency departments, ambulance log sheets, prescriptions filled, retail drug and
product purchases, school or work absenteeism, and medical signs and symptoms in persons seen in various clinical settings. With
faster, more specific and affordable diagnostic methods and decision-support tools, timely recognition of reportable diseases with the
potential to lead to a substantial outbreak is now possible. Tools for pattern recognition can be used to screen data for patterns needing
further public health investigation. During the 2003 epidemic, the Centers for Disease Control and Prevention (CDC) worked to
develop surveillance criteria to identify persons with SARS in the United States, and the surveillance case definition changed
throughout the epidemic, to reflect increased understanding of SARS (CDC, 2007).
Information acquired by the collection and processing of population health data becomes the basis for knowledge in the field of
public health. There is an ever-increasing need for timely information about the health of communities, states, and countries.
Knowledge about disease trends and other threats to community health can improve program planning, decision making, and care
delivery. Patients seen from the perspective of major health threats within their communities can benefit from opportunities for early
intervention.
This chapter focuses on the application of informatics methods to public health surveillance. The availability of clinical
information for public health has been fundamentally changed by the introduction of the electronic health record (EHR) and health
information technology (IT), which now give public health “an unprecedented opportunity to leverage the information, technologies
and standards to support critical public health functions such as alerting and surveillance” (Garrett, 2010).
Core Public Health Functions
The core public health functions are “The assessment and monitoring of the health of communities and populations at risk to identify
health problems and priorities; The formulation of public policies designed to solve identified local and national health problems and
priorities; To assure that all populations have access to appropriate and cost-effective care, including health promotion and disease
prevention services, and evaluation of the effectiveness of that care” (Medterms Medical Dictionary, 2007). “Public health is a field
that encompasses an amalgam of science, action, research, policy, advocacy and government” (Yasnoff, Overhage, Humphreys, &
LaVenture, 2001, p. 536).
Historically, Dr. John Snow can be designated as the “father” of public health informatics (PHI). In 1854, he plotted information
about cholera deaths and was able to determine that the deaths were clustered around the same water pump in London. He convinced
authorities that the cholera deaths were associated with that water pump; when the pump handle was removed, the cholera outbreak
ended. It was Dr. Snow’s focus on the cholera-affected population as a whole rather than on a single patient that led to his discovery of
the source of the cholera outbreak (Vachon, 2005).
Florence Nightingale should also be recognized as an early public health informaticist. Her recommendations about medical reform
and the need for improved sanitary conditions were based on data about morbidity and mortality that she compiled from her
experiences in the Crimea and England. Her efforts led to a total reorganization of how and which healthcare statistics were collected
(Dossey, 2000).
Just as information has been recognized as an asset in the business world, so health care is now recognized as an information-
intensive field requiring timely, accurate information from many different sources. Health information systems address the collection,
storage, analysis, interpretation, and communication of health data and information. Many health disciplines, such as medicine and
nursing, have developed their own concepts of informatics integrating computer, information, and cognitive science with the science of
the professional domain. That trend has reached the field of public and community health. PHI represents “a systematic application of
information and computer science and technology to public health (PH) practice, research and learning” (Yasnoff, O’Carroll, Koo,
Linkings, & Kilbourne, 2000, p. 67). This area of informatics differs from others because it is focused on the promotion of health and
disease prevention in populations and communities. PHI efficiently and effectively organizes and manages data, information, and
knowledge generated and used by public health professionals to fulfill the core functions of public health: assessment, policy, and
assurance (Agency for Toxic Substances and Disease Registry, 2003). Public health changes the social conditions and systems that
affect everyone within a given community. It is because of public health initiatives that people understand the importance of clean
water, the dangers of second-hand smoke, and the fact that seat belts really do save lives (Public Health Institute, 2007).
The scope of PHI practice includes knowledge from a variety of additional disciplines, including management, organization theory,
psychology, political science, and law and fields related to public health, such as epidemiology, microbiology, toxicology, and
statistics (O’Carroll, Yasnoff, Ward, Ripp, & Martin, 2003, p. 5). PHI focuses on applications of IT that “promote the health of
populations rather than individuals, focus on disease prevention rather than treatment, focus on preventive intervention at all
vulnerable points” (pp. 3–4). PHI addresses the data, information, and knowledge that public health professionals generate and use to
meet the core functions of public health (Public Health Data Standards Consortium [PHDSC], 2007b). Yasnoff et al. (2000) defined
four principles that define and guide the activities of PHI: (1) applications promote the health of populations, (2) applications focus on
disease and injury prevention, (3) applications explore prevention at “all vulnerable points in the causal changes,” and (4) PHI reflects
the “governmental context in which public health is practiced” (p. 69).
The Institute of Medicine defines the role of public health as “fulfilling society’s interest in assuring conditions in which people
can be healthy” (Khoury, 1997, p. 176). Functions of public health include prevention of epidemics and the spread of disease,
protection against environmental hazards, promotion of health, disaster response and recovery, and providing access to health care
(PHDSC, 2007a).
The initiative of integrating the healthcare enterprise to ensure that healthcare information can be shared more easily and used
more effectively has inspired the creation of the domain known as quality, research, and public health (QRPH). Participants in this
domain address the repurposing of clinical, demographic, and financial data collected in the process of providing clinical care to the
monitoring of disease patterns; incidence, prevalence, and situational awareness of such patterns; and the identification of new patterns
of disease not previously known or anticipated. Such data can be incorporated within existing public health population analyses and
programs for direct outreach and condition management through registries and locally determined appropriate treatment programs or
protocols (QRPH, 2010).
Community Health Risk Assessment: Tools for Acquiring Knowledge
As the public has become more aware of harmful elements in the environment, risk assessment tools have been developed. Such tools
allow assessment of pesticide use, exposure to harmful chemicals, contaminants in food and water, and toxic pollutants in the air to
determine if potential hazards need to be addressed. A risk assessment may also be called a “threat and risk assessment.” A “threat” is
a harmful act, such as the deployment of a virus or illegal network penetration. A “risk” is the expectation that a threat may succeed
and the potential damage that can occur (PCMag.com, 2007). “Risk factor assessments complement vital statistics data systems and
morbidity data systems by providing information on factors earlier in the causal chain leading to illness, injury or death” (O’Carroll,
Powell-Griner, Holtzman & Williamson, 2003, p. 316).
“Health risk assessments are used to estimate whether current or future exposures will pose health risks to broad populations”
(California Environmental Protection Agency [CEPA], 1998, p. 4) and are used to weigh the benefits and costs of various program
alternatives for reducing exposure to potential hazards. They may also influence public policy and regulatory decisions. Health risk
assessment is a constantly developing process based in sound science and professional judgments. There are usually four basic steps
ascribed to risk assessment:
1. Hazard identification seeks to determine the types of health problems that could be caused by exposure to a potentially
hazardous material. All research studies related to the potentially hazardous material are reviewed to identify potential health
problems.
2. Exposure assessment is done to determine the length, amount, and pattern of exposure to the potentially hazardous material.
3. Dose–response assessment is an estimation of how much exposure to the potential hazard would cause varying degrees of
health effects.
4. Risk characterization is an assessment of the risk of the hazardous material causing illness in the population (CEPA, 1998).
The overall question the risk assessment has to answer is, “How much risk is acceptable?” Risk factor systems are used throughout the
United States and may be local, regional, or national in scope. Specific risk assessment tools exist for specific health issues, such as the
Suicide Prevention Community Assessment Tool, which addresses general community information, prevention networks, and the
demographics of the target population and community assets and risk factors. Other risk assessment tools include the Youth Risk
Behavior Surveillance System, the Behavioral Risk Factor Surveillance System, and the National Health and Nutrition
Examination Survey.
Determining the presence of risk factors in community is a key part of a community risk assessment (CRA). Communities may
be concerned about which elements in the environment affect or may affect the community’s health, the level of environmental risk,
and other factors that should be included in public health planning. Ball (2003) defines value as “a function of cost, service, and
outcome” (p. 41). The value of a CRA derives from its ability to provide information crucial to planning, build consensus regarding
how to mobilize community resources, and allow for comparison of risks with those of other communities. The goal of a CRA is risk
reduction and improved health. A CRA may identify unmet needs and opportunities for action that may help set new priorities for
local public health units. It may also be used to monitor the impact of prevention programs.
Processing Knowledge and Information to Support Epidemiology and Monitoring Disease
Outbreaks
There is a need to define the role of federal, state, and local public health agencies in the development of PHI and IT applications. The
availability of IT today challenges all stakeholders in the health of the public to adopt new systems that can provide adequate disease
surveillance; it also challenges people to improve outmoded processes.
Preparedness in public health requires more timely detection of potential health threats, situational awareness, surveillance,
outbreak management, countermeasures, response, and communications. Surveillance uses health-related data that signal a sufficient
probability of a case or an outbreak that warrants further public health response. Although historically syndromic surveillance has been
used to target investigations of potential infectious cases, its use to detect possible outbreaks associated with bioterrorism is
increasingly being explored by public health officials (CDC, 2007). Early detection of possible outbreaks can be achieved through
timely and complete receipt, review, and investigation of disease case reports; by improving the ability to recognize patterns in data
that may be indicative of a possible outbreak early in its course; and through receipt of new types of data that can signify an outbreak
earlier in its course. Such new types of data might include identification of absences from work or school; increased purchases of
healthcare products, including specific types of over-the-counter medications; presenting symptoms to healthcare providers; and
laboratory test orders (CDC, 2007). The University of Pittsburgh Realtime Outbreak and Disease Surveillance Laboratory (RODS), for
example, developed the National Retail Data Monitor (NRDM) system. The NRDM collects data on over-the-counter medications and
other healthcare products from 28,000 stores and uses computer algorithms to detect unusual purchase patterns that might potentially
signal a disease outbreak (RODS Laboratory, 2013). A comprehensive surveillance effort supports timely investigation and identifies
data needs for managing the public health response to an outbreak or terrorist event. Informatics tools are becoming increasingly
important in these public health efforts.
To appropriately process public health data, PHI has a need for a standardized vocabulary and coding structure. This is especially
important as national public health data are collected and data mining performed so that data variables can be understood across
systems and between agencies. Health information organizations (HIOs) have been established to support data sharing via health
information exchanges (HIE) promoted by the meaningful use criteria of the electronic health record (EHR). Central to these initiatives
is the need for standardized codes and terminologies that may be used by the HIOs to map data from disparate sources (Shapiro,
Mostashari, Hripcsak, Soulakis, & Kuperman, 2011).
In the early 1990s, the CDC launched a plan for an integrated surveillance system that moved from stand-alone systems to
networked data exchange built with specific standards. Early initiatives were the National Electronic Telecommunications System for
Surveillance and the Wide-ranging Online Data for Epidemiologic Research. Six current initiatives reflect this early vision:
1. PulseNet USA: A surveillance network for food-borne infections.
2. National Electronic Disease Surveillance System: Facilitates reporting on approximately 100 diseases, with data feeding
directly from clinical laboratories, which allows for early detection.
3. Epidemic Information Exchange: A secure communication system for practitioners to access and share preliminary health
surveillance information.
4. Health Alert Network: A state and nationwide alert system.
5. Biosense: Provides improved real-time biosurveillance and situational awareness in support of early detection.
6. Public Health Information Network: Promotes standards and software solutions for the rapid flow of public health information.
Certainly, the events of September 11, 2001, which indicated the need for the United States to increase its efforts directed toward
prevention of terrorism, accelerated the need for informatics in public health practice. Today, response requirements include fast
detection, science, communication, integration, and action (Kukafka, 2006). In 2005, the CDC created the National Center for Public
Health Informatics to provide leadership in the field. This center aims to protect and improve health through PHI (McNabb, Koo,
Pinner, & Seligman, 2006).
Information is vital to public health programming. The data processed into public health information can be obtained from
administrative, financial, and facility sources. Included in this data stream may be encounter, screening, registry, clinical, and
laboratory and surveillance data. It has been recommended that the functions of population health beyond surveillance be integrated
into the EHR and the personal health record. Such an initiative might allow for population-level alerts to be sent to clinicians through
these electronic record systems. Systems now being developed allow for automated syndromic surveillance of emergency department
records and media surveillance, which in turn supports early detection of potential pandemic occurrences. Such systems were tested
during the 2009 H1N1 flu outbreak. The public health–enhanced electronic medical record can provide immediate detection and
reporting of notifiable conditions. The incorporation of geographic information systems allows public health data to be mapped to
specific locations that may indicate an immediate need for intervention (Grannis & Vreeman, 2010).
Vital statistics from state and local governments are also used for public health purposes. It should be noted that databases created
with public funds are public databases that are available for authorized public representatives for public purposes (Freedman & Weed,
2003).
Widespread implementation of EHRs is likely to facilitate the concept of a public health–enabled record, which can automatically
send patient information alerts from the point of care to public health departments when reportable symptoms, conditions, or diseases
are encountered. A public health–enabled EHR can be bidirectional, allowing public health information and recommendations for
treatment to be accessible at the point of care. One public health EHR prototype addresses the information flow related to newborn
screenings (Orlova et al., 2005).
Potential applications of HIE to public health have been described by Shapiro et al. (2011). They include syndromic surveillance
using data generated from mandated and nonmandated laboratory results, physician diagnoses, and emergency or clinic chief
complaints; strategies to locate loved ones in mass-casualty events; and public health alerts at the individual and population levels.
Applying Knowledge to Health Disaster Planning and Preparation
The availability of data and the speed of data exchange can have a significant impact on critical public health functions, such as
disease monitoring and syndromic surveillance. Currently, surveillance data are limited and historical in nature, although this situation
is rapidly changing. Nevertheless, special data collections are needed to address specific public health issues, and investigations and
emergencies are still frequently addressed and managed with paper. In the future, PHI will make real-time surveillance data available
electronically, and investigations and emergences will be managed with the tools of informatics (Yasnoff et al., 2004). “Surveillance
data systems such as infectious disease trackers that collect data on adverse health effects are invaluable tools for public health
officials to tap for planning, evaluation, or implementation of public health interventions” (Agency for Toxic Substances and Disease
Registry, 2003). “Syndromic surveillance for early outbreak detection is an investigational approach where health department staff,
assisted by automated data acquisition and generation of statistical signals, monitor disease indicators continually (real-time) or at least
daily (near realtime) to detect outbreaks of diseases earlier and more completely than might otherwise be possible with traditional
public health methods” (Buehler, Hopkins, Overhage, Sosin, & Tong, 2004, para. 7). Traditionally, there has been no common
infrastructure to respond to pandemics, but the development of health IT is creating opportunities that go far beyond national
boundaries to impact global public health initiatives.
In New York City, a primary care information project funded by the CDC has developed a multifaceted initiative, the Center for
Excellence in Public Health Informatics, to address issues of measurement of meaningful use, disease and outbreak surveillance, and
decision support alerts at the point of care (Buck, Wu, Souliakis, & Kukalka, 2010).
Informatics Tools to Support Communication and Dissemination
The revolution in IT has made the capture and analysis of health data and the distribution of healthcare information more achievable
and less costly. Since the early 1960s, the CDC has used IT in its practice; PHI emerged as a specialty in the 1990s. PHI has become
more important with improvements in IT, changes in the care delivery system, and the challenges related to emerging infections,
resistance to antibiotics, and the threat of chemical and biologic terrorism. Two-way communication between public health agencies,
community, and clinical laboratories can identify clusters of reportable and unusual diseases. In turn, health departments can consult
on case diagnosis and management, alerts, surveillance summaries, and clinical and public health recommendations. Ongoing
healthcare provider outreach, education, and 24-hour access to public health professionals may lead to the discovery of urgent health
threats. The automated transfer of specified data from a laboratory database to a public health data repository improves the timeliness
and completeness of reporting notifiable conditions.
Public health information systems represent a partnership of federal, state, and local public health professionals. Such systems
facilitate the capture of large amounts of data, rapid exchange of information, and strengthened links among these three system levels.
Dissemination of prevention guidelines and communication among public health officials, clinicians, and patients has emerged as a
major benefit of PHI. IT solutions can be used to provide accurate and timely information that guides public health actions. In
addition, the Internet has become a universal communications pathway and allows individuals and population groups to be more
involved and take greater responsibility for management of their own health status.
Few public health professionals have received formal informatics training, and many may not be aware of the potential impact of
IT on their practice. A working group formed at the University of Washington Center for PHI has published a draft of PHI
competencies needed (Karras, 2007). These competencies include the following (Center for Public Health Informatics, 2007):
Supporting development of strategic direction for PHI within the enterprise
Participating in development of knowledge management tools for the enterprise
Using standards
Ensuring that the knowledge, information, and data needs of project or program users and stakeholders are met
Managing information system development, procurement, and implementation
Managing IT operations related to a project or program (for public health agencies with internal IT operations)
Monitoring IT operations managed by external organizations
Communicating with cross-disciplinary leaders and team members
Participating in applied public health informatics research
Developing public health information systems that are interoperable with other relevant information systems
Supporting use of informatics to integrate clinical health, environmental risk, and population health
Implementing solutions that ensure confidentiality, security, and integrity, while maximizing availability of information for
public health
Conducting education and training in PHI
Using Feedback to Improve Responses and Promote Readiness
Improvement of community health status and population health depends on effective public and healthcare infrastructures. In addition
to information from public health agencies, there is now interest in the capture of information from hospitals, pharmacies, poison
control centers, laboratories, and environmental agencies. Timely collection of such data allows early detection and analysis, which
can increase the rapidity of response with more effective interventions. Yasnoff et al. (2000) identify the “grand challenges” still
facing PHI as the development of national public health information systems, a closer integration of clinical care with public health,
and concerns of confidentiality and privacy.
Population health data must be considered an important part of the infrastructure of all regional health information exchanges,
which are the building blocks for a national health information network. Organizations and agencies interested in promoting and
protecting the public’s health must commit to collaboration and seamless data sharing (PHDSC, 2007b). Public health data include
data related to surveillance, environmental health, and preparedness systems as well as client information, such as data from
immunization registries and laboratory results reporting and analysis. These types of data can provide information about outbreaks,
patterns of drug-resistant organisms, and other trends that can help improve the accuracy of diagnostic and treatment decisions
(LaVenture, 2005). A regional health information exchange and national health information network can also support public health
goals through broader opportunities for participation in surveillance and prevention activities, improved case management and care
coordination, and increased accuracy and timeliness of information for disease reporting (LaVenture, 2005).
Much of the information presented here is focused on reaction to issues and timely intervention, rather than harnessing information
technology for disease prevention. Fuller (2011) has advocated for a shift to prevention informatics by harnessing realtime social data
and aggregating and representing these data in a meaningful way so that an appropriate prevention response can be mounted. For
example, Internet searches related to flu symptoms might prompt a public health prevention response such as a school closure to
minimize spread. Newer software tools to support mapping and real-time data visualization include Riff and Ushahidi, each of which
supports “gathering of distributed data from the web and other data streams” (p. 40). “Prevention informatics offers a useful paradigm
for re-imagining health information systems and for harnessing the vast array of data, tools, technologies and systems to respond
proactively to health challenges across the globe” (p. 41).
Harnessing data from social media such as Twitter and Facebook provides yet another example of using citizen-generated
information (crowdsourcing) in community health. Merchant, Elmer, and Lurie (2011) have described how mining data generated in
social media can improve response to mass disasters by helping responders locate people who need help and identify areas where to
send resources, build social capital, and promote community resilience postdisaster. “Tweets and photographs linked to timelines and
interactive maps can tell a cohesive story about a recovering community’s capabilities and vulnerabilities in real time” (p. 291). These
authors caution, however, that social media should be used to augment—not replace—current disaster response and communication
systems, as not all communications in social media are entirely trustworthy.
Summary
Public health informatics strives to ensure that evolving health data systems will meet the data needs of all organizations interested in
population health as national and international standards are developed for healthcare data collection. This includes standardization of
environmental, sociocultural, economic, and other data that are relevant to public health (PHDSC, 2007b). Table 18-1 provides the
names, addresses, and URLs for important organizations dedicated to public health data and informatics. Table 18-2 lists abbreviations
commonly used in PHI.
The future of practice in public health depends on how efficiently and effectively public health data are captured, analyzed, and
disseminated for regional, national, and global health planning and management. In an ideal world, we would see seamless data
collection and sharing and a commitment to global health planning.
TABLE 18-1 IMPORTANT PHI SITES
Name Address Website
American Public Health
Association
APHA
800 I Street, NW
Washington, DC 20001
www.apha.org
Center for Public Health
Informatics
CPHI
University of Washington
1100 NE 45th Street, Ste 405
Seattle, WA 98105
www.cphi.washington.edu
Centers for Disease Control and
Prevention
Centers for Disease Control and
Prevention
1600 Clifton Road
Atlanta, GA 30333
www.cdc.gov
National Center for Public Health
Informatics
The National Center for Public
Health Informatics (NCPHI)
1600 Clifton Road, NE
Mailstop E-78
Atlanta, GA 30333
https://web.archive.org/web/20110123075557/
http://www.cdc.gov/ncphi/
Public Health Data Standards
Consortium
Public Health Data Standards
Consortium
c/o Johns Hopkins Bloomberg
School of Public Health
624 N Broadway, Room 325
Baltimore, MD 21205
www.phdsc.org
Public Health Institute
Public Health Institute
555 12th Street, 10th Floor
Oakland, CA 94607
www.phi.org
TABLE 18-2 ABBREVIATIONS USED IN PHI
BRFSS Behavioral Risk Factor Surveillance System
CDC Centers for Disease Control and Prevention
CEPA California Environmental Protection Agency
CPHI Center for Public Health Informatics
CRA Community Risk Assessment
EPI-X Epidemic Information Exchange
HAN Health Alert Network
IOM Institute of Medicine
IT Information Technology
NCPHI National Center for Public Health Informatics
NEDSS National Electronic Disease Surveillance System
NETSS National Electronic Technology System for Surveillance
NHANES National Health and Nutrition Examination Survey
NHIN National Health Information Network
PH Public Health
PHDSC Public Health Data Standards Consortium
PHI Public Health Informatics
PHIN Public Health Information Network
PHRAP Pennsylvania’s Health Risk Assessment Process
QRPH Quality, Research, Public Health
RHIO Regional Health Information Exchanges
SPRC Suicide Prevention Community Assessment Tool
WONDER Wide-ranging Online Data for Epidemiologic Research
YRBSS Youth Risk Behavior Surveillance System
THOUGHT-PROVOKING
QUESTIONS
1. Imagine that you are a public health informatics specialist and that you and your colleagues are concerned about a new strain of influenza. Which
public health data are used to determine the need for a mass inoculation? Which data will be collected to determine the success of such a
program?
2. What are the advantages and disadvantages of using crowdsourced social media data during a disaster response?
References
Agency for Toxic Substances and Disease Registry. (2003). ATSDR glossary of terms. www.atsdr.cdc.gov/glossary.html
Ball, M. (2003). Better health through informatics: Managing information to deliver value. In P. O’Carroll, W. L. Yasnoff, M. E. Ward, L. Ripp, & E.
Martin (Eds.), Public health informatics and information systems (pp. 39–51). New York, NY: Springer-Verlag.
Buck, M., Wu, W., Souliakis, N., & Kukalka, R. (2010). Achieving excellence in public health informatics: The New York City experience. Washington,
DC: AMIA.
Buehler, J. W., Hopkins, R. S., Overhage, J. M., Sosin, D. M., & Tong, V. (2004). Framework for evaluating public health surveillance systems for early
detection of outbreaks. http://www.cdc.gov/mmwr/preview/mmwrhtml/rr5305a1.htm
California Environmental Protection Agency (CEPA). (1998). A guide to health risk assessment. www.oehha.ca.gov
Center for Public Health Informatics (CPHI). (2007). Draft competencies V7 for the public health informatician. Seattle, WA: University of Washington.
Centers for Disease Control and Prevention (CDC). (2007). www.cdc.gov
Dossey, B. M. (2000). Florence Nightingale: Mystic, visionary, healer. Springhouse, PA: Springhouse.
Freedman, M. A., & Weed, J. A. (2003). National vital statistics system. In P. O’Carroll, W. Yasnoff, M. Ward, L. Ripp, & E. Martin (Eds.), Public
health informatics and information systems (pp. 269–285). New York, NY: Springer-Verlag.
Fuller, S. (2011). From intervention informatics to prevention informatics. Bulletin of the American Society for Information Science & Technology,
38(8), 36–41.
Garrett, N. (2010). Leveraging the EHR for public health alerting. CDC Clinician Outreach & Communication Activity (COCA) conference call, June
22, 2010.
Grannis, S., & Vreeman, D. (2010). A vision of the journey ahead: Using public health notifiable condition mapping to illustrate the need to maintain
value sets. Washington, DC: AMIA.
Karras, B. (2007). Competencies for the public health informatician. Seattle, WA: University of Washington.
Khoury, M. J. (1997). Genetic epidemiology and the future of disease prevention and public health. Epidemiology Reviews, 19(1), 175–180.
Kukafka, R. (2006). Public health informatics. Medical Informatics Course for Health Professionals. Woods Hole, MA.
LaVenture, M. (2005, May). Role of population/public health in regional health information exchanges. PHDSC/eHealth Initiative Annual Conference,
Washington, DC.
McNabb, S., Koo, D., Pinner, R., & Seligman, J. (2006). Informatics and public health at CDC.
http://www.cdc.gov/mmwr/preview/mmwrhtml/su5502a10.htm
Medterms Medical Dictionary. (2007). www.medterms.com
Merchant, R., Elmer, S., & Lurie, N. (2011). Integrating social media into emergency-preparedness efforts. New England Journal of Medicine, 365(4),
289–291. doi:10.1056/NEJMp1103591
O’Carroll, P. L., Powell-Griner, E., Holtzman, D., & Williamson, G. D. (2003). Risk factor information systems. In P. O’Carroll, W. Yasnoff, M. Ward,
L. Ripp, & E. Martin (Eds.), Public health informatics and information systems (pp. 316–334). New York, NY: Springer-Verlag.
O’Carroll, P., Yasnoff, W., Ward, M., Ripp, L., & Martin, E. (Eds.). (2003). Public health informatics and information systems. New York, NY:
Springer-Verlag.
Orlova, A., Dunnagan, M., Finitzo, T., Higgins, M., Watkings, T., Tien, A., & Beales, S. (2005). An electronic health record–public health (EHR-PH)
system prototype for interoperability in 21st century healthcare systems (pp. 575–579). AMIA, Annual Symposium Proceedings.
PCMag.com Encyclopedia. (2007). Retrieved from http://www.pcmag.com/encyclopedia Public Health Data Standards Consortium (PHDSC). (2007a).
Tutorial module 1: What is public health. Retrieved August 2007 from www.phdsc.org/knowresources/Tutorials/module1
Public Health Data Standards Consortium (PHDSC). (2007b). Tutorial module 8: Viewing public health data and data standards in a larger context.
Retrieved August 2007 from www.phdsc.org/knowresources/tutorials/module1
Public Health Institute. (2007). www.phi.org
QRPH. (2010). Retrieved January 2011 from http://www.interoperabilityshowcase.org/…/HIMSS09_interop_QRPH
RODS Laboratory. (2013). About the National Retail Data Monitor. www.rods.pitt.edu/site/content/blogsection/4/42/
Shapiro, J. S., Mostashari, F., Hripcsak, G., Soulakis, N., & Kuperman, G. (2011). Using health information exchange to improve public health.
American Journal of Public Health, 101(4), 616–623. doi: 10.2105/AJPH.2008.158980
Vachon, D. (2005). Dr John Snow blames water pollution for cholera epidemic. Old News, 16(8), 8–10.
http://www.ph.ucla.edu/epi/snow/fatherofepidemiology.html
Yasnoff, W., Humphreys, B., Overhage, J., Detmer, D., Brennan, P., Morris, R., … Fanning, J. (2004). A consensus action agenda for achieving the
national health information infrastructure. Journal of the American Medical Informatics Association, 11(4), 332–338.
Yasnoff, W., O’Carroll, P., Koo, D., Linkings, R., & Kilbourne, E. (2000). Public health informatics: Improving and transforming public health in the
information age. Journal of Public Health Management Practice, 6(6), 67–75.
Yasnoff, W., Overhage, M., Humphreys, B., & LaVenture M. (2001). A national agenda for public health informatics. Journal of the American Medical
Informatics Association, 8(6), 535–545.
Chapter 19
Telenursing and Remote Access Telehealth
Original contribution by Audrey Kinsella, Kathleen Albright, Sheldon Prial, and Schuyler F.
Hoss; revised by Kathleen Mastrian and Dee McGonigle
OBJECTIVES
1. Explore the use of telehealth technology in nursing practice.
2. Identify the socioeconomic factors likely to increase the use of telehealth interventions.
3. Describe clinical and nonclinical uses of telehealth.
4. Specify and describe the most common telehealth tools used in nursing practice.
5. Explore telehealth pathways and protocols.
6. Identify legal, ethical, and regulatory issues of home telehealth practice.
7. Describe the role of the telenurse.
8. Apply the Foundation of Knowledge model to home telehealth.
Key Terms
Call centers
Central stations
Chronic disease
Home health care
Home telehealth care
Medication management devices
Patient informed consent
Peripheral biometric (medical) devices
Personal emergency response system
Portals
Real-time telehealth
Sensor and activity-monitoring systems
Store-and-forward telehealth transmission
Telehealth
Telehealth care
Telemedicine
Telemonitoring
Telenursing
Telepathology
Telephony
Teleradiology
Web servers
Introduction
Telehealth, a relatively new term in the medical and nursing vocabulary, refers to a wide range of health services that are delivered by
telecommunications-ready tools, such as the telephone, videophone, and computer. The most basic of telecommunications technology,
the telephone, has been used by health professionals for many years, sometimes by nurses to counsel a patient or by doctors to change
a patient’s plan of care. Because of these widespread uses, people are already somewhat familiar with the value of the direct, expedient
contact that telecommunications-ready tools provide for healthcare professionals. A recent press release by IMS Research (2013)
reported that telehealth monitoring was used for 308,000 patients in the United States in 2012 and that the demand for services on a
worldwide basis is expected to reach 1.8 million patients by 2017. The growing field of telehealth, particularly in nursing practice, will
allow clinicians to improve care delivery services even more.
History of Telehealth
In the early 1960s, President John F. Kennedy gave the National Aeronautics and Space Administration (NASA) the goal of landing an
American on the moon. A surprise benefit of the space program was the demonstration of effective remote monitoring of the
astronauts—and thus modern telehealth was born.
Although most of the advances in telehealth have taken place in the last 20 to 30 years, Craig and Patterson (2005) describe much
earlier examples, such as the use of bonfires to alert neighboring villages of the arrival of bubonic plague during the Middle Ages.
Postal services and telegraphs were used to transmit health information in the mid-19th century, and 1910 marked the first
transmission of stethoscope sounds over a telephone. Radio communications were used to provide medical support for crews on ships;
the Seaman’s Church Institute of New York (1920) and the International Radio Medical Center (1938) are two examples of
organizations founded to provide health support at a distance. These services were later expanded to cover air travel (Craig &
Patterson, 2005). The National Institutes of Mental Health supported a program in the mid-1950s that connected seven state hospitals
in four different states via a closed-circuit telephone system (Venable, 2005). As technology evolved, its use in health care continued
to grow. The first reported use of television to monitor patients in a clinical facility occurred in the 1950s, which then led to the
development of interactive closed-circuit applications in the mid-1960s. These early applications of television to health care occurred
within the facility but still had the benefit of extending the reach of the caregivers because they did not need to be in the same room to
monitor their patients effectively (Prial & Hoss, 2009).
In the 1970s, uses of more advanced forms of telehealth in the medical field, referred to as telemedicine, included teleradiology
and telepathology—radiologic and pathologic images transmitted to specialists who were located at some distance (Allan, 2006). As
additional specialties, such as dermatology and ophthalmology, entered the telemedicine arena, telehealth use enabled even more
physicians to access information about their patients, regardless of the distances between themselves and the patients, and in sites other
than conventional healthcare settings.
Success in telehealth was achieved after decades spent refining the technology, which resulted in clearer imaging, more speedy
transmissions, and accurate replication of data from remote locations to a central hub. Technical advances in imaging, for example,
have increased the usage of telehealth in wound care and increased specialists’ satisfaction with quality of patient data received from
remote sites (Kinsella, 2002b). The end results of telehealth interactions today have helped to ensure that professionals, whether
working off site or directly with patients, can replicate the usual clinical interactions in all specialties regardless of the distance
involved in the contact.
This capacity to supervise patients away from an office is predicted to continue to grow rapidly; indeed, “worldwide telemedicine
markets at $7 billion in 2009 are expected to reach $24 billion by 2016” (Aarkstore Enterprise, 2010, para. 30). The ability to provide
better healthcare access is the number one benefit of using telehealth. By reducing the need for face-to-face interaction with the
patient, the nurse, physician, or even technician can be much more productive. When information is collected in the home, it becomes
much more convenient for the patient, and the quality and timeliness of the information is improved dramatically. Home
telemonitoring should be viewed as an enhancement to care, because it allows more direct, physical intervention to occur only when it
is actually needed. Care is not directed by prescheduled appointment or subjective perceptions of condition, but instead can be
determined by objective measures of physical status. With telehealth, care can also be delivered at the most appropriate site of care,
reducing reliance on emergency departments and inpatient facilities (Prial & Hoss, 2009).
Nursing Aspects of Telehealth
Understanding telehealth and the potential use of telehealth technology in nursing practice is necessary in today’s changing healthcare
arena. As this chapter describes, nurses using telehealth have much greater access to their patients’ conditions and needs and are able
to respond in a more timely way than is possible using only face-to-face visits. Patients’ responses to new medications, for example,
can be tracked within hours rather than the several days that elapse between face-to-face visits. The telecommunications-ready tools
that can be used to achieve these results are described here, and cases that have demonstrated successful outcomes are highlighted.
Today, the use of many telehealth tools by nurses is new. Telehealth interventions or contacts are performed off site and often
require less time to be spent on certain tasks because of the efficiencies offered by the technology applications. Their use, however,
must never be associated with less care. It is also important to note that nursing activity in telehealth still follows the same best practice
standards as those espoused in conventional care. One should simply look at the use of telehealth tools as a means for nurses to do
their work better.
The case study that follows indicates the capability of a home healthcare nurse’s working with telehealth tools to detect and
respond to a patient’s condition more expediently so that needed care could be accessed.
Driving Forces for Telehealth
A significant increase is expected in the use of information technology tools in nursing venues in the coming decades. This use is
affected by a number of factors in all of Western society. The following factors are drivers of the growing trend toward telehealth and
technology use and will influence nursing practice significantly in the next decades: demographics; nursing and healthcare worker
shortages; chronic diseases and conditions; the new, educated consumers; and excessive costs of healthcare services that are increasing
in need and kind.
CASE STUDY: EARLY DETECTION OF A CHANGE IN CONDITION
Mrs. C., an independent, 96-year-old woman, has a history of rehospitalization because of atrial fibrillation resulting from congestive heart failure
(CHF) and hypertension.
After her most recent hospitalization, Mrs. C. was treated and released into home care at an agency in Washington. A home telemonitoring
system that tracks and transmits patients’ vital signs was placed in her home. The primary goal of placing this patient on the telemonitor was to
provide daily monitoring of her condition, thereby avoiding unnecessary rehospitalizations.
One morning, Mrs. C.’s telenurse detected an alarmingly low oxygen saturation level in the patient’s transmitted data. In response, the nurse
telephoned Mrs. C. and asked her to retake her oxygen reading. The reading was confirmed and the telenurse contacted the patient’s physician, who
requested immediate transportation of the patient to the hospital emergency room. Medics were called and Mrs. C. was taken to the hospital, where
she was diagnosed with a pulmonary embolism.
The prompt response resulted from early detection and timely intervention enabled by the home telehealth equipment and a home health nurse’s
oversight. One notable fact in this case is that although the primary goal of monitoring patients is to avoid unnecessary hospitalization, in this case
the hospitalization was necessary for the patient as a result of her elevated blood pressure and compromised oxygen saturation levels. The patient was
still asymptomatic at the time of detection. However, the telehealth intervention and subsequent hospitalization allowed for the embolism to be
treated before any serious damage occurred.
Under the traditional home care model, this patient might have been seen by a nurse only two to three times per week. The clinician does not
have knowledge of the patient’s condition in between visits; however, having vital patient data tracked and transmitted daily allowed for rapid
response that resulted in a positive outcome, perhaps a life-saving intervention for this patient.
Demographics
One hears it every day: The baby boomers are getting older and people are living longer. In 2000, 13% of the American population
was older than age 65, and the number of older adults is growing significantly. According to the U.S. Bureau of the Census (2006),
7,918 Americans turn 60 years old every day. Consequently, by 2030, 19% of the U.S. population will be 65 years old or older and the
oldest-old (85 years or older) will increase by 3 million persons between 2010 (5.8 million) and 2030 (8.7 million) (U.S. Department
of Commerce, Bureau of the Census, 2010). Also on the rise is the number of older Americans living with at least one chronic disease
or condition; they account for 4 out of every 5 people older than age 50 (American Association for Retired Persons [AARP], 2009).
This trend should alert clinicians to the much greater demand for planned professional care that will arise in the near future.
Nursing and Healthcare Worker Shortages
The crisis related to the well-known nursing shortage has two key aspects: a greater need for nurses by more persons, particularly those
living with sometimes multiple comorbidities, and a significant decrease in the number of young people entering the nursing
profession. In 2004, there were 2.9 million registered nurses (RNs) in the United States, more than 41% of whom were aged 50 years
or older (U.S. Department of Health and Human Services, 2004). Nationwide, the demand for nurses is clearly exceeding the supply. A
report from the Health Resources and Services Administration (U.S. Department of Health and Human Services, 2002) on the shortage
of RNs notes that the shortage was expected to reach 12% by 2010, and then nearly double to 20% as soon as 2015. A more recent
report by Buerhaus, Auerbach, and Staiger (2009) stated that the recession of the past few years has helped to ease the RN shortage
currently, but suggested that the aging of the RN workforce and the aging of the population will likely combine to produce a shortfall
of nurses beginning in 2018, with the deficit reaching nearly 260,000 by 2025.
The very serious shortage of healthcare workers in the United States must be addressed with some foresight. Although there is
currently more focus on training laypeople, such as aides and other paraprofessionals, to perform certain nursing tasks, this venture
certainly cannot replicate the clinical expertise of trained nurses skilled in nursing science. We must begin to look seriously at using
effective adjuncts to skilled care, with telehealth being one of these important developments. Some organizations have already begun
to do so. For example, a recent study by the Pennsylvania Homecare Association and Penn State University (2004) looked at how
telehealth can be used to address workforce issues in the home healthcare industry and determined that telehealth use may enhance
nurses’ job satisfaction and help to retain nurses in their current positions.
Chronic Diseases and Conditions
Chronic conditions are the leading cause of illness, disability, and death in the United States today. The aging population living with
chronic diseases and conditions is expected to increase dramatically in the United States in the next few decades. Many age groups are
also affected by chronic diseases, not just the elderly. Currently, more than 100 million Americans are living with one or more chronic
diseases or conditions. The costs of their direct medical care are excessive—as much as $539 billion per year, according to the Institute
of Aging’s 1996 report, Chronic Care in America: A 21st Century Challenge. As noted in a more recent report from the Centers for
Disease Control and Prevention (2005), medical care costs of people with chronic diseases account for more than 75% of the United
States’ $1.4 trillion medical care expenditures. Furthermore, by the year 2030, 148 million Americans will join these ranks, with at
least one third of them being limited in their ability to go to school or to live independently. Follow the CDC’s chronic disease
surveillance activities at this website http://www.cdc.gov/chronicdisease/stats/index.htm. Securing appropriate, adequate, and
affordable care services for these populations should be a national concern.
Educated Consumers
The wave of today’s aging baby boomers is steering some of the usual health service practices toward a very different course. Many of
these individuals are more educated than their parents and more comfortable with the use of technology. They want to become more
informed and involved with their care plans. These empowered consumers will be financially motivated with the introduction of
consumer-directed healthcare plans that reward healthier lifestyles and better disease management of chronic conditions. All of these
circumstances will further drive the use and the innovation of new technologies to meet consumer need. However, as Kinsella (2002a)
has pointed out, the consumerdriven trend of which the boomers are a part will not only be affected by technological innovation but
will also affect how healthcare services are delivered. New plans for this new generation of consumers very much lean toward meeting
their demands for whenneeded, as-needed care, or care services delivered on their own terms and timing.
Economics
When one connects the drivers of today’s healthcare market—the demographics, nursing shortages, and increased number of persons
living with chronic conditions and their extensive use of healthcare services—with excessive costs of this health care, the critical need
for solutions becomes obvious. The U.S. healthcare system spends $1.4 trillion per year on conventional medical care. Much more will
be spent annually in the coming decades. One must ask: Taking all of the driving factors of today’s healthcare market into account,
what needs to be done to address healthcare issues in the United States and meet the burgeoning numbers and needs of patients?
One solution is to develop a new clinical model for American health care that includes technology. In particular, telehealth
technology should be included to fill the gap resulting from an overabundance of patients and a scarcity of healthcare providers. This
concept is indicated in Figure 19-1.
Figure 19-1 Technology fills the gap
Source: Courtesy of Honeywell
Consider the use of technology that might potentially fill the current gaps in the healthcare system. Tools of telehealth can, for
example, help render needed services without requiring in-person professional care at all contacts. The remote or virtual visit made by
skilled clinicians is just one approach to using the range of health technologies available today. More needs to be learned about what
telehealth is, how it works, and which aspects have been successful so that clinicians can plan to incorporate its use into routine
clinical care.
Telehealth Care
Let us start with a basic definition of telehealth care. Keep in mind, however, that telehealth is an emerging field, and definitions are
subject to change and improvement as technology evolves. The American Telemedicine Association (ATA, 2010) provides the
following definition:
Telemedicine is the use of medical information exchanged from one site to another via electronic communications to improve
patients’ health status. Closely associated with telemedicine is the term “telehealth,” which is often used to encompass a
broader definition of remote health care that does not always involve clinical services. Videoconferencing, transmission of still
images, e-health including patient portals, remote monitoring of vital signs, continuing medical education and nursing call
centers are all considered part of telemedicine and telehealth. (para. 1).
Indeed, “telehealth” is generally used as an umbrella term to describe all of the possible variations of healthcare services that use
telecommunications. Telehealth can refer to clinical and nonclinical uses of health-related contacts. Delivery of patient education, such
as menu planning for patients with diabetes or the transmission of medication reminders, is an example of the health-promoting aspects
of telehealth.
Clinical Uses of Telehealth
A few clinical uses for telehealth technologies and some sample clinical applications include the following:
Transmitting images for assessment or diagnosis. One example is transmission of digital images, such as images of wounds for
assessment and treatment consults.
Transmitting clinical data for assessment, diagnosis, or disease management. One example is remote patient monitoring and
transmitting patients’ objective or subjective clinical data, such as monitoring of vital signs and answers to disease management
questions.
Providing disease prevention and promotion of good health. One example is telephonic case management and patient education
provided through asthma and weight management programs conducted in schools.
Using telephonic health advice in emergent cases. One example is performing teletriage in call centers.
Using real-time video. One example is exchanging health services or education live via videoconference.
Telehealth Transmission Formats and Their Clinical Applications
Nurses must become familiar with the many and varied clinical and nonclinical transmission formats and applications of telehealth
technologies so that they can make informed choices about the tools that are available for their use, as needed. Among these
applications are store-and-forward telehealth, real-time telehealth, remote monitoring, and telephony.
Store-and-Forward Telehealth
In a store-and-forward telehealth transmission, digital images, video, audio, and clinical data are captured and stored on the client
computer or device; then, at a convenient time, the data are transmitted securely (forwarded) to a specialist or clinician at another
location, where they are studied by the relevant specialist or clinician. If indicated, the opinion of the specialist or clinician is then
transmitted back. Based on the requirements of the participating healthcare entities, this round-trip interaction could take anywhere
from a few minutes to 48 hours. In many store-and-forward specialties, such as teleradiology, an immediate response is not critical.
Dermatology, radiology, and pathology are common specialties whose practices are conducive to store-and-forward technologies.
Transmission of wound care images for assessment by specialty care nurses or other specialists has become a frequently used and
important form of home telehealth nursing practice.
Real-Time (or Interactive) Telehealth
In real-time telehealth, a telecommunications link between the involved parties allows a real-time or live interaction to take place.
Videoconferencing equipment is one of the most common forms of technologies used in synchronous telehealth. In addition,
peripheral devices can be attached to computers or to the videoconferencing equipment to facilitate an interactive examination. Use of
computers for real-time two-way audio and video streaming between centers over ever-improving and cheaper communication
channels is becoming common. These developments have contributed to lowering of costs in telehealth.
Examples of real-time clinical telehealth applications include the following:
Telemental health, which uses videoconferencing technology to connect a psychiatric nurse with a mental health client.
Telerehabilitation, which uses videocameras and other technologies to assess patients’ progress in home rehabilitation.
Telehome care, which uses video technologies to observe, assess, and teach patients living in rural areas.
Teleconsultations, which use a variety of technologies to enable collaborative exchanges or consultations between individuals
or among groups that are involved with a case. These teleconsults may be transmitted live using videoconferencing technology.
They may, for instance, involve teaching a certain technique to a less-experienced clinician, or they may provide several
clinicians with an opportunity to discuss an appropriate approach to a difficult case.
Telehospice or telepalliative care, which can use real-time or remote monitoring to provide psychological support to patients
and caregivers. Telehealth devices can also play a role in symptom management, in effect helping end-of-life patients achieve
an optimal quality of life.
Remote Monitoring (Telemonitoring or Remote Patient Monitoring)
In remote monitoring, devices are used to capture and transmit biometric data. For example, a tele-electroencephalogram device can
monitor the electrical activity of a patient’s brain and then transmit those data to a specialist assigned to the case. This interaction
could occur either in real time or as a stored and then forwarded transmission. Examples of telemonitoring include the following:
Monitoring patient parameters during home-based nocturnal dialysis
Cardiac and multiparameter monitoring of remote intensive care units
Home telehealth—for example, daily home telemonitoring of vital signs by patients and subsequent transmission of those data
that enables off-site nurses to track their patients regularly and precisely and address noticeable changes through education and
information suggestions about diet or exercise
Disease management
Telephony
Telephone monitoring (telephony) is the most basic type of telehealth. It can be described as scheduled remote care delivery or
monitoring in which scheduled patient encounters via the telephone occur between a healthcare provider and a patient or caregiver
(Quality Insights of Pennsylvania, 2005). More details about interaction using telephony are discussed later in this chapter.
Nonclinical Uses of Telehealth Technologies
There are also many nonclinical uses of telehealth technologies. Currently, these include distance education including continuing
medical and nursing education, grand rounds, and patient education; administrative uses including meetings among telehealth
networks, supervision, and presentations; and research using the Internet and other online sources for information and health data
management.
All of these telecommunications-assisted activities overcome obstacles of distance and provide access to needed health-related
information. Clearly, with telehealth, the range of patient care possibilities broadens significantly.
Telenursing
Where do nurses using telehealth, as in telenursing, fit into today’s healthcare delivery arena? Telenursing refers to the use of
telecommunications and information technology to provide nursing services in health care and enhance care whenever a physical
distance exists between patient and nurse or among any number of nurses (Skiba, 1998). As a clinical field, telenursing is part of
telehealth and has many points of contacts with other medical and nonmedical applications, such as telediagnosis, teleconsultation, and
telemonitoring. In their study, St George et al. (2009) concluded, “In terms of the kinds of calls received, the dispositions reached after
triage and clinical safety, we could detect no differences between nurses working from home and those working in the call centre” (p.
123). Telenurses do not, in effect, work outside of the broader clinical team effort, no matter where they are located. Instead, they
serve as an integral part of the healthcare delivery team.
Applications of Telenursing in Home Care
The most developing area of telenursing today is in home telehealth care. An accepted definition of home telehealth care provided by
Kinsella (2004) is as follows: “Home telehealth care is clinician-driven remote care delivery and education services that are delivered
to the home via telecommunications-ready tools” (p. 36).
As home telehealth care has evolved, this definition has expanded to include a broader arena of delivery. In fact, some have
defined the “home” as anywhere outside of an acute inpatient setting—a definition that includes nursing homes, assisted living
facilities, and other living situations beyond the single-family home or apartment. Wherever the home setting may be, people want to
be cared for there. Today’s burgeoning senior population, in particular, has become quite vocal about this preference. According to
regular surveys conducted by the AARP (1996), more than 90% of seniors want to remain independent at home and age in a familiar
place. Care at home is clearly a key concern and preference.
Fortuitously, the reach of nurses using telecommunications-ready tools in the home is now remarkably extended. Not only have the
settings for home care expanded beyond what was usual (the family home) in the last four decades of home health’s formal existence,
but the types of services delivered to the home have also become more advanced. The home care industry’s newest challenge is to
work with sicker patients, many of whom have been discharged from hospitals to home earlier than in the past.
This challenge to extend the range of conventional home care is why telehealth can be and needs to be provided to a wide range of
patients, including those who have the following characteristics:
Are immobilized
Live in remote or difficult-to-reach places
Have chronic ailments, such as chronic obstructive pulmonary disease, diabetes, and congestive heart disease
Have debilitating diseases, such as neural degenerative diseases (Parkinson disease, Alzheimer disease, amyotrophic lateral
sclerosis)
All of these patients may stay at home and be visited and assisted regularly by a nurse via videoconferencing, Internet, videophone, or
other telecommunications means. These telecommunications-ready tools enable home telenurses to follow through advanced levels of
care, as needed.
Still other varied applications of home telehealth care involve the care of patients in immediate postsurgical situations, those
needing care of wounds and ostomies, and handicapped individuals needing physical therapy interventions or telerehabilitation. In
addition to this extended range of patients who can be served with telehealth, many more patients can be seen when telehealth is used.
For example, in conventional home health care, depending on the distances of travel involved, one nurse may be able to visit as many
as 7 patients per day. Using telenursing, however, one nurse can remotely visit or televisit 12–16 patients in the same amount of time
using interactive telehealth. Over the last decade, the efficiencies of telenursing have been well documented, as have the resulting
improved patient care outcomes that can be expected by frequent telecontact.
Another outpatient application of telenursing is telephony-based call centers, which may be operated by managed care
organizations, hospitals, and other health organizations. Some call centers also include telemonitoring services, which allow the patient
to stay at home and use different telehealthcare devices to transmit biometric and other medical information back to the call center.
Monitoring can be intermittent or continuous. This use of the teletechnology allows clinicians to evaluate patients’ status and use the
data to make decisions to better manage patients’ health conditions.
Many features of call centers’ services are comparable to conventional hands-on care in the home. For instance, call centers are
typically staffed by RNs who act as case managers or perform patient triage. These professionals can provide information and
counseling to patients as part of a disease management program and as a means to educate them on their disease process. In effect,
their goal is to offer appropriate access to care (from nurses at the call centers) and help patients to prevent avoidable emergency room
visits and rehospitalizations. An example of this assistance includes a nurse calling (i.e., not waiting for a patient to contact the call
center) a recently discharged patient with diabetes on a regularly scheduled basis to evaluate progress at home, activity tolerance,
medication compliance, foot care, and diet management. This empowering of patients toward self-management is a very significant
and needed part of telenursing.
Home care telenursing can also involve other activities, such as providing customized patient education in dietary or exercise
needs, nursing teleconsultations, review of results of medical tests and examinations, and assistance to physicians in the
implementation of medical treatment protocols. The work can be wide ranging; for example, some home-based telecardiology
programs involve the patient, the family, the physician, and a specialized cardiac monitoring center. A multidisciplinary approach is
used along with best practice–defined protocols to manage the patient, improve the patient’s quality of life, and reduce healthcare
costs, especially hospitalization costs. Nurses play a key role in this network of care.
A relatively new role for advanced practice nurses is that of a tele–intensive care nurse. These nurses provide remote monitoring,
oversight, and expert consultation for patients in rural intensive care units (ICUs), by examining real-time data collected at the bedside.
They look for trends indicating that an intervention is needed and then alert the bedside nurse of the need, thereby providing an extra
set of eyes and an advanced level of expertise. “Tele-ICU provides expert-driven, evidence-based, cutting-edge services to the
monitoring and treatment of critically ill patients” (Williams, Hubbard, Daye, & Barden, 2012, p. 62). Goran (2011) studied tele-ICU
nursing competencies and emphasizes that effective listening and collaboration skills and the ability to prioritize patient needs are
among the most important attributes of tele-ICU nurses. She also cautions that nurses who function in these collaborative roles need to
continue to practice at the bedside to keep their clinical skills sharp.
By reviewing all of these examples of telenursing practice, one can see that nurses using telenursing can broaden their involvement
in the targeted care of their patients. Some sources have predicted that home care will soon become the hub of all patient activity: The
home will be where care that is begun in hospitals and other settings will be managed over the very long term and in the most cost-
effective healthcare setting. Home care telenurses can expect to play a vital and dynamic role in the changing delivery systems that are
likely to be put in place in the coming decades.
Telehealth Patient Populations*
Any patient who has a condition that must be monitored is a candidate for home telemonitoring. Patients with chronic illnesses have
particularly benefited from ongoing monitoring to prevent acute episodes. Patients who are homebound or who have limited access to
transportation are also appropriate candidates for such monitoring.
Patients with Chronic Diseases
Given demographics and advances in medical practice, there has been unprecedented growth in the number of patients with chronic
diseases. Those patients are at significant risk of having an acute episode when subtle but significant changes in their medical
condition occur. The ability to identify these changes in a timely fashion allows for changes in medication, lifestyle, or treatment to
occur. Identification of a 3-lb weight gain over 5 days in a patient with CHF, for example, allows for interventions that could prevent
an emergency room visit and subsequent hospitalization. The categories of patients with chronic diseases who are most commonly
monitored today include those with CHF, chronic obstructive pulmonary disease (COPD), or diabetes, and those who require long-
term wound care.
These patients, particularly those with higher acuity levels, are at significant risk of having a medical crisis that might necessitate
emergency or unplanned acute interventions. Many other patients with chronic diseases are less susceptible to a health crisis, but
would greatly benefit from home telemonitoring to improve care and reduce costs.
At-Risk Populations
Telemonitoring can be used effectively on patients who are at greater risk for an episode of acute illness. Patients who have a
predisposition to disease are at increased risk of medical problems associated with employment, lifestyle, or location, and those
patients who have displayed early signs of potentially serious health problems could be placed on preventive monitoring. In such
cases, monitoring is used to ensure that interventions are timely and acute incidents avoided. Such technology could be part of a
healthcare early warning system and could support preventive models of care.
Isolated Patients
Home telemonitoring is effective for patients who cannot physically access healthcare services. The homebound elderly have been
among the first to benefit from this technology in conjunction with the home health services they receive. With increasing limits
affecting the ability of patients to receive services in the home because of staffing shortages and coverage limitations, telemonitoring
technology takes on greater importance in managing homebound patients.
Patients in remote geographic areas have been long-time users of telemedicine interventions, such as robodocs. According to
Strauss (2010), “The Remote Presence Robot (RP-7) from InTouch Health is a mobile telemedicine unit that connects physicians and
specialists with patients and other doctors who are too distant to consult with them in person” (para. 2). With few rural healthcare
facilities being built and access problems becoming more difficult, the use of technology in the home will increase. Even in suburban
and rural areas, access is becoming more problematic, requiring greater use of home telemonitoring interventions. A lack of primary
care and emergency resources in many urban core areas has prompted many health systems to consider managing certain patients
through telemonitoring options and better staging of patient access. Telemedicine also is effective for patients in such institutions as
prisons, for whom it is logistically difficult to travel to traditional care sites.
Hospitalized Patients
Home telemonitoring has proved effective in managing the flow of patients into and out of hospitals and other inpatient facilities.
Patients are monitored to determine when they are admitted, to predict how long they might stay, and to prevent unfunded
readmissions. Patients undergoing semielective procedures can be better staged with scheduling options when they are monitored in
the home before admission. If deterioration of the patient’s condition is observed, a procedure can be accelerated or planned
interventions changed.
Monitoring can also be used effectively in length-of-stay reduction strategies. Physicians and surgeons are more confident in
discharging patients early when they know that vital signs will be monitored and any decline in condition noted in a timely fashion.
Use of monitoring effectively allows for an extension of step-down models of care into the patient’s home. These length-of-stay
management strategies have been particularly useful when hospital beds are in short supply.
Unplanned readmissions are a serious patient care and financial management issue for hospitals. The use of monitoring in the home
has consistently reduced unplanned and unfunded readmissions by enabling healthcare providers to obtain reliable information on the
patient and intervene appropriately to keep the patient at home.
Emergency Response Situations
Telemedicine will likely be a major component of effective patient management in a major disaster, large-scale nuclear or biochemical
attack, or outbreak of highly infectious disease. In such a situation, traditional healthcare delivery systems may become overwhelmed,
and patients will need to be more effectively triaged and managed by remote providers. Telemedicine applications allow for a dramatic
extension of patient management and triaging options and allow off-site providers to be involved in care. If an infectious or
communicable disease is involved, patient isolation could be accomplished in the home using telemedicine technology.
Concerned Patients and Families
Perhaps the largest potential market for home telemonitoring is patients and families who want to have reliable and objective
information that allows for their involvement in healthcare decision making. At one extreme is a young person who wants to monitor
physiologic data as part of a personal wellness or fitness program. At the other extreme are families that want information on the status
of a terminally ill loved one in another city. In between there is a wide range of opportunities for individuals and families to obtain
information that promotes realistic and meaningful dialogue with healthcare professionals.
Assisted Living and Subacute Patients
In assisted living facilities or subacute care centers, a kiosk can be used to obtain vital signs for large groups of people. Vital signs
reports can then be forwarded on a regularly established schedule to physicians and others involved in the patient’s care. This approach
allows for better individual care management and lends itself to developing intervention strategies and education options to benefit the
entire population of a facility. Some facilities have even used access to telemonitoring systems as an inducement to attract potential
residents.
Employers and Wellness Programs
Health care is a vital concern for employers. They have a direct financial interest in lowering costs and are financial beneficiaries of
long-term illness preventive strategies. If they can monitor their workers (e.g., offering telehealth options as a wellness program), they
will see many benefits for themselves, such as reduced absenteeism and increased productivity. Effective monitoring programs can
ultimately lower healthcare costs and associated insurance premiums. Some companies are now exploring the creation of financial
incentives for employees who achieve healthcare objectives, such as appropriate weight, reduced blood pressure, and levels of
exercise. Monitoring could very well be used as a means of tracking performance in this regard.
Tools of Home Telehealth
A wide and growing range of telecommunications-ready tools are available for nurses’ and patients’ use in the home. These tools are
examined in more detail next.
Central Stations, Web Servers, and Portals
Central stations, Web servers, and portals are various terms for the technologies presently used as part of multifunctional
telehealthcare platforms and application servers. These clinical management software programs receive and display patients’ vital
signs and other information transmitted from a medical device, including blood pressure, weight, and glucose information. Such
transmission is most commonly accomplished over plain old telephone system lines (POTS); however, network access and wireless
communication is gaining popularity as technology advances and access improves.
Central stations and Web servers are key components of telehealth that can be as minimal as a single screen display or as
comprehensive as software applications that provide various functions including triaging the data based on medical alerts, which
allows clinicians to quickly identify those patients requiring immediate attention. Other features found in these packages allow
clinicians to build personal medical records for patients and provide trended patient data and analysis reports supporting improved
patient outcomes using telehealth. In addition, some of the software packages provide remote programming capabilities that allow the
clinician to remotely program the medical device in the patient’s home. Such an application can change monitoring report times for
patients, individualize alert parameters, set up reminders, and send educational content to a patient.
Peripheral Biometric (Medical) Devices
Peripheral biometric (medical) devices can consist of fully integrated systems, such as a vital signs monitor, or they may be stand-
alone telecommunications-ready devices, such as blood pressure cuffs and blood glucose meters. Many of these devices plug directly
into the household telephone jack to send data to a central server location.
An ever-increasing number of peripheral devices are being introduced to the market. Examples of other peripheral devices seen in
home telehealth today include pulse oximeters; prothrombin time, International Normalized Ratio meters; spirometers; peak flow
meters; electrocardiogram monitors; and card readers and writers that use smart card technology and enable multiple users to use one
device.
Telephones
Telephones are already the most familiar household communications tool used in telehealth care. A telephone device can be
augmented for easier use by patients, as needed, with a lighted dial pad, an auto-dial system, or a louder ringer.
Videocameras and Videophones
Videocameras and videophones are readily available consumer items that can be used in telehealth for show-and-tell demonstrations
by nurses for patients or to capture wound healing progress, among other applications. Typically, these products operate as a standard
telephone or as a video picture telephone, using standard telephone lines to transmit information or interactions.
Currently, the image quality over a POTS is limited by the bandwidth of POTS technology, which favors use of such images for
assessment rather than delivering diagnostic-quality images. These imaging capabilities will improve as integrated services digital
network lines become more widely available in the home environment. Typically, medical centers and hospitals have access to larger
bandwidth capabilities for image transmission and viewing, thus ensuring high-quality diagnostic images and point-to-point
consultations in hospital- or medical center-based settings.
Personal Emergency Response Systems
Personal emergency response systems are signaling devices worn as a pendant or otherwise made easily accessible to patients to
ensure their safety and to enable them to quickly access emergency care when needed, usually in case of a fall. A preset telephone
number is alerted by the patient’s pushing a button on the pendant; upon this signal, predesignated emergency help is dispatched.
Many newer sensor options for tracking patients at home are being incorporated into multifunctioning personal emergency response
systems devices, such as alerting a central call center to water flooding or smoke in a patient’s home. The next subsection provides
details on these sensors and monitors.
Sensor and Activity-Monitoring Systems
Sensor and activity-monitoring systems can track activities of daily living of seniors and other at-risk individuals in their place of
residence. These sensors and monitoring systems can provide insight into behavior changes that might signal changes or deterioration
of health status. Such technologies consist of wireless motion sensors that are strategically placed around the residence and can detect
motion on a 24-hour basis.
One authority on these new technologies, David J. Stern (2007), describes their operation further. Data from these sensors are
wirelessly sent to a receiver and base station that periodically transmits the information to a centralized server through standard
telephone lines. Sophisticated algorithms analyze the data, compiling data on each individual’s normal patterns of behavior, including
bathroom usage, sleep disturbance, meal preparation, medication interaction, and general levels of activity including fall detection.
Deviations from these norms can be important warning signs of emerging health problems and can enable caregivers to intervene
early.
In addition to widely used fire, security, and home gas detectors, other sensors can monitor appliances to detect whether a
household appliance is turned on or off and can sometimes switch the appliance on and off for the resident. Typical applications for
affixing programmable sensors can include lamps, television sets, irons, and kitchen stoves. Such sensors might be very valuable for
ensuring the safety of elderly, forgetful persons who live alone. One excellent example of today’s sensor use for assistance with the
elderly are sensors placed in or on stovetops to alert the user when he or she is standing too close to the equipment or when the kettle
or pot has boiled over. Benefits realized from these technologies include enabling people to live independently with an improved
quality of life. They can also provide peace of mind for other family members living at a distance.
Medication Management Devices
Medication management devices address a well-recognized major problem in health care: medication management and compliance.
According to the American Heart Association (2007), 49% of Americans use prescription drugs. In fact, 32 million people are taking
three or more medications daily, with even more medications typically being taken by those 65 years of age or older.
What has become a national problem in health care today is the failure of patients to take medications as prescribed. Failure to do
so can have devastating consequences, particularly for those patients living with chronic illnesses. The National Pharmaceutical
Council (2013) estimates that the cost of noncompliance with prescribed medications is $290 billion per year: 3 out of 4 people do not
take their medications as prescribed and 1 out of 3 people never fills his or her prescription.
To address some of these very pressing problems, a host of telecommunications-ready medication devices have become available,
and many more are in development. Some are as simple as a watch that reminds a person to take medications, others are pill organizers
with audible reminders, and some can be programmed to dispense prefilled containers with medications and alert a patient or caregiver
of a missed dose. Furthermore, some of these medication tools send data from the device back to a central server so that patient’s
medication compliance can be tracked. These telecommunications-ready devices can organize, manage, dispense, or remind, and they
will play an increasingly important role in helping people live independently and manage their disease processes through medication
compliance. For more information on these devices, see the Informatics Tools to Promote Patient Safety and Clinical Outcomes
chapter.
Special Needs Telecommunications-Ready Devices
Special needs telecommunications ready-devices can include preprogrammed, multifunctional infusion pumps for meeting a range of
infusion needs, including medications for pain management and other infusion delivery needs, such as hydration and nutrition, peak
flow meters, electrocardiogram monitors, and so on. Many such tools are in development to meet the more demanding and challenging
needs of today’s at-home patients. The common goal of these tools is to increase communications between nurse and patient and to
increase the nurse’s knowledge of the patient’s status in a timely manner.
Home Telehealth Software*
As important as the gathering of data is the organizing of information to support decision making by clinical professionals. The
telehealth software supporting home telehealth programs has become much more sophisticated in recent years, allowing for greater
numbers of patients to be better managed by a single clinician. Areas of significant improvement in software include trending, triage,
communications protocols, access, and sharing.
Trending
One of the key advantages of home telemonitoring is the creation of a digital health record that allows information to be recorded over
time. If a patient takes his or her weight and blood pressure daily, most software will graphically display these data over time so that
subtle trends can be observed. This type of trend data is much more useful in identifying emerging or developing conditions than
snapshot data that are collected every 6–8 weeks at a physician’s office. Trend information can also be developed for groups of
patients or populations, allowing for population-based analyses of interventions. For example, one might gather trend information on
patients with COPD, patients of a particular physician, or all patients receiving a certain medication.
Triage
Most home telemonitoring systems set an acceptable range of values for an individual patient when he or she is enrolled in the
monitoring program. For example, if oxygen saturation, blood pressure, or weight values go above or below predetermined amounts,
then the software alerts the appropriate party. More sophisticated software looks at readings from multiple pieces of equipment on a
single patient and can give higher priority to patients at risk of an acute episode. This helps clinicians better organize their work and
arrange for appropriate interventions.
Communications
Advanced telemonitoring software utilizes sophisticated electronic notification protocols. It is often predetermined when information
will be communicated and to whom it is sent. Sophisticated protocols can be developed related to both routine and alert information,
thereby more effectively organizing communications with physicians, nurses, and caregivers. Some systems also have the capacity to
communicate back to the patient or seek additional information under predetermined circumstances.
Data Access and Information Sharing
Many telemonitoring systems house information in Web-based formats. This allows for easy access to the data from any location that
has access to the Internet. Multiple parties can simultaneously share and view data. Data can also be conveniently transmitted to other
clinicians and are updated almost immediately when new values are received. Web-based records are fully HIPAA compliant when
appropriate protections and controls are in place.
Home Telehealth Practice and Protocols
The tools of telehealth described previously are devices that enable remote care delivery, enhance patient care, and improve outcomes.
They should be incorporated into nursing practice because nurses would typically use, for example, a blood pressure cuff. It is
important to note that the data received from these tools are useless without some type of clinical oversight. Such tools do not replace
the nurse, but rather give the nurse the ability to make more informed clinical decisions based on reliable data and a comprehensive
picture of the patient’s status. In home care, they also direct the clinical resources to patients based on need. The resulting patient-
centered approach to care delivers improved patient outcomes and clinical efficiencies.
Home telehealth is indeed a practice, albeit one that represents a change in the current clinical model of practice for home care. Use
of the telehealth tools is integrated into the practice to improve patient outcomes. As with any tool, however, the effectiveness is
directly proportional to the appropriateness of the tool’s application and use. Home telehealth programs differ depending on the type of
technology used and the foci of the telehealth programs. However, every program should have telehealth use criteria established. The
guidelines discussed here are broad-based, generic clinical guidelines for the deployment of home telehealth developed by the Home
Telehealth Special Interest Group of the American Telemedicine Association (ATA, 2002); though developed more than a decade ago,
they are still applicable today.
The first guideline focuses on ensuring that the home is suitable for home telehealth delivery, and the second focuses on ensuring
that patients sign an informed consent form before receiving telehealth services. During the initial face-to-face visit, an assessment
should be conducted to determine access to utilities and to identify safety concerns related to the installation of the equipment.
Informed written consent must be obtained from the client or designee before beginning the use of telehealth consultations. The
consent should be part of the plan of care and stored in the clinical record.
These and similar guidelines issued by other health organizations, including the American Nurses Association (2001) and the
Community Health Accreditation Program (whose guidelines are not publicly available to nonmembers), can apply to both interactive
home telehealth and telemonitored activity. Patient criteria should be governed by established inclusion and exclusion guidelines,
detailing who is eligible and appropriate for each type of technology. Other criteria include establishing policies and procedures that
address patient enrollment, education, and equipment setup; patient and caregiver and home assessment; patient informed consent;
and privacy and confidentiality rights. In addition, a clinical plan of care that is specific to patient needs should be developed.
Telehealth pathways and protocols ensure more focused work with patients and allow for targeted interventions. A sample pathway to
use for a patient who is being remotely monitored is provided in Figure 19-2. Home health agencies may use such samples as
templates to design their own in-house, customized guidelines for home telenursing practice.
Figure 19-2 Sample clinical pathway
Source: Courtesy of Honeywell International Inc.
In addition, in Figure 19-3, a sample protocol is provided for telemonitoring patients living with hypertension. As with use of the
pathway in Figure 19-2, nursing agencies may use this sample as a template for better managing these patients with chronic disease
and for designing other in-house protocols for telemanaging patients living with other chronic diseases. These protocols provide a
focused start for nurses to learn and use home telehealth correctly and consistently.
Clearly, by using the protocol for patients who regularly use telehealth equipment for tracking their status, nurses receive a good
deal more targeted information than can possibly be obtained during scheduled, in-person visits. As a result, the use of telehealth tools,
together with clinical oversight and practice, allows for more efficient and effective clinical management by allowing the patient’s
needs to drive the care. As home telehealth protocols are used more extensively, the improved clinical and operational efficiencies may
ultimately affect the home care agency’s bottom lines.
One must understand this clinically driven, as-needed approach to care services more fully so that it is not misunderstood as
providing less care. In the following case study, a proactive, patient-centered approach enabled a home healthcare agency to identify
early exacerbations in a patient’s condition and take appropriate action.
CASE STUDY: HOME TELEMONITORING OF MULTIPLE ILLNESSES
The patient is a 71-year-old male who has stage 4 cardiomyopathy/pulmonary hypertension, atrial fibrillation, COPD, and type 2 diabetes mellitus.
He has been an active patient with a home healthcare agency for several years, with an admitting diagnosis of CHF.
Initially, the patient was seen three times a week by an RN for CHF assessment and management. The patient’s history included frequent
hospitalizations for exacerbation of CHF and uncontrolled atrial fibrillation. He experienced a total of four hospitalizations in the year before
placement of a telemonitoring system in his home, after about 6 months of receiving conventional home care.
Ever since the patient was placed on a telemonitoring system for daily tracking more than 8 months ago, he has not been rehospitalized. The
telemonitoring interactions with his nurse have made him very conscious of the role that his medications, diet, and fluid restrictions play in his
overall health status.
In addition, the telemonitor has proved its benefits to local physicians. The patient’s family physician, cardiologist, and pulmonologist all were
able to provide better care for the patient by examining the tabular and graphical trends that were elicited from the daily vital signs monitor. This
information aided in the titration and addition of the various medications needed to control the patient’s CHF and atrial fibrillation. The physicians
were able to ascertain the response to the medication adjustments and other treatment modalities, such as oxygen titration. At the start of care, the
patient’s weight was 196 pounds; it is now at a stable 187 pounds, with the symptoms more controlled than they have ever been.
The patient’s nurses, meanwhile, have peace of mind knowing they can keep an eye on their patient daily while making additional visits as
needed, with the documentation being provided by the system to justify the additional nursing visit. This tool can also be incorporated into the
nurses’ care plan, enabling a higher standard of care to the patient.
At present, the patient is being case managed by nursing staff visits that occur once per month. He now enjoys a newfound peace of mind and
security and an improved state of health, something this patient has not experienced in more than a year.
Figure 19-3 Protocol for CHF
Source: Courtesy of Honeywell International Inc.
Research Brief
Nurses’ Scope of practice in a Call Center
Butler, Danby, Emmison, and Thorpe (2009) examined nursing scope of practice issues for nurses who staff a child health hotline. They used a
qualitative research approach to examine call transcripts and found that nurses used three main strategies to avoid providing medical advice and
information: (1) informing callers that they were nurses and thus specifying boundaries of expertise; (2) prompting parents to recognize their own
personal authority to make decisions regarding their child’s health (termed “privileging parental authority”); and (3) reframing medical questions and
medical information seeking as child development issues to keep within the boundaries of nursing expertise. Butler et al. emphasize the need for
specific institutional guidelines and policies and training to ensure that call-center nurses are not practicing telehealth beyond the scope of their
practice license.
Source: The full article appears in Butler, C., Danby, S., Emmison, M., & Thorpe, K. (2009). Managing medical advice seeking in calls to Child
Health Line. Sociology of Health & Illness, 31(6), 817–834. Retrieved from Health Module (Document ID: 1884062501).
Legal, Ethical, and Regulatory Issues
Telehealth is affected by certain legal, ethical, and regulatory issues of which nurses should be aware. In the United States, interstate
practice of telenursing, for example, requires attending nurses to be licensed to practice in all of the states in which they provide
telehealth services by directly interacting with patients. This is particularly important when nurses work for health systems that are
located near state borders and draw patients from both states. The Telehealth Resource Center provides a nice overview of Licensure
and Scope of Practice issues; access it at http://www.telehealthresourcecenter.org/toolbox-module/licensure-and-scope-practice.
Legal issues, such as scope of practice, accountability for practice, and the potential for and definition of malpractice are still
largely unresolved concerns that are difficult to address, according to attorney and medical doctor Barry Cepelewicz (2003). Nurses
must be vigilant about keeping extensive documentation of their visits on and off site. Charges of negligence, for instance, can be
countered by nurses’ providing documented evidence of visits and interventions made to their patients, particularly when the nurses
were located off site. See the Research Brief on nurses’ scope of practice in a call center.
Patient confidentiality and the privacy and safety of clinical data must be given special consideration. Informed consent releases to
receive telehealth services are a critical first step. Demiris, Doorenbos, and Towle (2009) suggest that informed consent be treated as a
process and not a one-time event. They argue that because telehealth is a completely new experience, patients and families should have
the opportunity to revise their consent after they fully understand its implications, especially the intrusiveness of home monitoring. In
addition, they suggest that informed consent be obtained from all persons living in the household because there are potential privacy
considerations for all who live in the home. When the patient is presented with the informed consent form, the nurse must ensure the
patient that physiologic data, such as blood pressure readings that will be transmitted over telephone lines or other public
communication means, will be kept confidential and protected. In addition, for safety considerations, pointed efforts must be
continually undertaken by the nurses’ agencies to upgrade their information systems to ensure that a high level of data security is
provided at all times. Telehealth providers must adhere to all data privacy and confidentiality guidelines and remain vigilant to ensure
that all involved parties, including the technical staff assistants, have appropriate training in privacy and confidentiality issues.
A Day in the Life of a Home Telenurse
Within the range of available telehealth tools, telenurses must choose those tools that are appropriate to their patients’ needs and
capabilities. A very important consideration is whether the patient can perform what is expected. Take, for instance, an automated
weight scale to be used daily to track the weight of an elderly patient with CHF. Simple procedures for patients using these devices are
typically unplugging the telephone from the household telephone jack, plugging the scale into the telephone jack, stepping on the
scale, pushing a send button, and replugging the telephone. Weight readings received at a central nursing station are accurate and
timed on the patient’s stored record.
Certainly, this procedure is automated and low touch. However, there is also ample room for patient–nurse contact, most
particularly if a weight gain is noted by the patient’s nurse. The following is an example of usual telephone contact procedures made
by one telenurse, Mary Bondmass, who responds to her CHF patients’ 2-pound weight gain in the past 24 hours with what she calls
“teaching opportunities” (quoted in Kinsella, 1998).
Typically, Bondmass reports, she asks patients who showed a weight increase: “What did you have for dinner last night?”
Then, “Tell me what you’ve been eating this week.” Hmm, Chinese take-out again.
She might then say, “Sounds like a lot of salt,” which would then lead into the “teaching opportunity” about salt intake and
fluid retention that the patient should have heard at least once before. Mary, the nurse, might say: “Look at your ankles,”
which may be swollen with retained fluids; or, if she noted a shortness of breath, indicating fluid in the lungs, she would point
that out. Patients would then get an immediate, practical lesson in cause, effect, and correctable actions for managing and
preventing fluid retention. (p. 47)
This case well illustrates one nurse’s contention (Bernier, 2001) about the value of home telehealth: With home telehealth, nurses
are working “at a distance but not in the dark” (p. 640). Nurses can see how patients are managing from the data sent and identify the
interventions that might be needed to correct problems.
Many telenurses have their patients’ weight information and alerts of gains automatically transferred to their pagers and use the
telephone to call the patient and reinforce details for, say, CHF patients’ newly learned dietary routines. At that time, the nurses may
also request an order from a physician for a diuretic for the patient, doing so in a timelier manner than would likely be the case with
less frequent patient–nurse, in-person contact. In fact, with relatively long periods (days) between in-person visits, many patients seek
help at emergency rooms for troubled breathing and pain—symptoms that might well be identified and corrected more speedily
through telehealth interventions.
The Patient’s Role in Telehealth
The range and sophistication of home telehealth tools are expanding regularly, and a concern of nurses when choosing appropriate
tools for their patients is to ask, Will my patient use this device? Elderly patients may find the monitoring technology that may speak
to them in their homes and videocameras to assist in wound care tracking, for example, to be a daunting introduction to home health
care. To assuage the possible discomfort, these and other such tools have undergone much iteration so that they are easier to use and
patients’ ability to turn them on and off is ensured. When patients are scheduled for a televisit, these devices can be turned on and
used. This use by patients is critical, of course, so that the necessary information about them will be gathered and transmitted, and so
that their needs can be acted on by telenurses.
Demiris et al. (2009) emphasize consideration of usability issues when using telehealth applications with elders who may have
sensory, cognitive, and motor disabilities. They suggest rigorous usability testing to maximize the quality of the user experience and
special attention to design details for Web-based interfaces, such as font choices and color schemes to improve readability. Similarly,
Kaplan and Litweka (2008) emphasize the need for ethical design principles:
How provider-centric or patient-centric is the technology?
Does the shift to remote services promote rationality and efficiency at the expense of values traditionally at the heart of
caregiving?
How does the design affect home life and family dynamics?
To what extent should technology usage involve attempts to manipulate users into different behaviors?
How might the replacement of human contact by new technologies be ameliorated?
To what extent is the deployment of technology an end in itself, aimed not toward the improvement of health or well-being, but
to create market needs?
How do we identify the boundaries between genuine solutions and futility in light of technologies that may shift them? (p. 404)
The importance of ensuring patient satisfaction with home health service delivery was predicted to be a megatrend in 2007
(Remington, 2007). Data gathered from the Hospital Consumer Assessment of Health Providers and System survey tool will measure
consumer satisfaction, which in turn will enable patient satisfaction to become a performance measurement for every provider across
the healthcare delivery system. For this reason, nurses must pay attention to the many examples of studies in home telehealth
indicating a great deal of patient satisfaction with telehealth, particularly those that note a preference for telehealth, not conventional
care. In one investigation, patients reported that the telehealth monitor helped them change the way they performed self-care (Chetney,
2006). Other studies have noted that patients seen with telehealth have a better understanding of their own disease process and describe
the ease with which patients are able to manage self-care and so enjoy an improved quality of life (Sanderson, 2007). When patients
achieve a good understanding of and perform self-care by the end of their home health admission periods, an important goal of home
telehealth has been attained.
However, when one moves beyond issues of technical ease of use and looks more closely at patients’ preferences for privacy or at
their desires to use the telehealth devices according to their own schedules, the growing trend of consumer-directed health care
becomes a concern. This is the new reality in health care. In part, the baby boom generation, many of whom are educated and
comfortable with technology, are driving this trend toward as-needed, when-wanted care. Challenges of scheduling and possibly even
reengineering telehealthcare services may be a concern (or an opportunity) for tomorrow’s home telehealthcare nurses. Clinicians are
only now beginning to learn how to care differently for this new generation of patients, who want telehealth care to be delivered on
their own terms. Kaplan and Litweka (2008) emphasize that we must consider cultural and community meanings associated with
technology when we suggest telehealth as a care delivery model.
Telehealth Research
Telehealth research focuses primarily on clinical outcomes, such as effectiveness of telehealth compared to usual care, cost
effectiveness of telehealth intervention, and patient and family satisfaction. For example, Dansky, Vasey, and Bowles (2008) studied
the effects of home telehealth care on several clinical outcomes in patients with heart failure. They found that patients who received
home telehealth care had fewer hospitalizations and costly emergency room visits during the study period and a greater reduction in
heart failure symptoms than the control group. These authors suggest that the frequent monitoring of symptoms afforded by telehealth
allowed for more frequent encounters than home visits, providing more timely intervention when clinical status changed, and more
frequent teaching and support by nurses.
Similarly, Jia, Chuang, Wu, Wang, and Chumbler (2009) studied the long-term effects of telehealth on preventable hospital use
and found a statistically significant reduction in hospital use for the first 18 months of follow-up, but noted that the effects diminished
over the 4-year study period. There were no differences found between telehealth and standard care groups in a study of rates of
infection, rejection, and hospitalization in a group of transplant recipients, leading the researchers to conclude that telehealth is as
effective as usual care for posttransplant follow-up (Leimig, Gower, Thompson, & Winsett, 2008). Telehealth was also shown to be
effective in providing support and problem-solving assistance for family caregivers of patients who experienced polytrauma (Bendixen
et al., 2008) and persons with spinal cord injuries (Elliott, Brossart, Berry, & Fine, 2008).
In a qualitative study of patient and physician perspectives on treating rural depression, Swinton, Robinson, and Bischoff (2009)
concluded, “Although an acceptable solution, both patients and PCPs expressed reservations about using telehealth for the treatment of
depression because they felt that technology-mediated communication would not lend itself to establishing and maintaining the type of
provider–patient relationship that would allow treatment to be effective” (p. 178). However, given the access issues associated with
rural communities, telehealth provided an opportunity for intervention in cases where traditional care was challenging. Telehealth was
also shown to be an effective alternative to usual care in conducting hearing screenings at rural elementary schools, thereby extending
the reach and controlling the costs of conducting hearing screenings (Lancaster, Krumm, Ribera, & Klich, 2008).
BOX 19-1 TELEHEALTH RESEARCH AND INFORMATION CENTERS
Center for Telehealth and E-Health Law: http://ctel.org/
International Society for Telemedicine and eHealth http://www.isfteh.org/
Telehealth Resource Centers: http://telehealthresourcecenter.org/
Telemedicine Information Exchange: http://www.tmhguide.org/site/epage/93994_871.htm
UTMB Center for Telehealth Research and Policy at University of Texas Medical Branch: http://telehealth.utmb.edu/
Virginia Telehealth Network: http://ehealthvirginia.org/
Polisena, Coyl, Coyl, and McGill (2009) conducted a meta-analysis of 22 studies that compared telehealth to usual care in patients
with chronic diseases; their work included a cost-effectiveness analysis. These authors concluded that in general telehealth can be cost-
saving for the health system and insurance providers, but caution that because the overall methodological quality of the studies was
low, the societal impact of telehealth remains uncertain.
Research into telehealth interventions clearly demonstrates that telehealth is at least as effective as usual care in managing chronic
conditions in the home, and in many cases is more cost-effective than home visits. Demiris et al. (2009) suggest that more studies are
needed to focus on the patient–provider relationship changes associated with such care, especially the loss of human touch associated
with telehealth interventions. They also caution that researchers who are studying telehealth in remote populations must consider long-
term sustainability of telehealth support beyond the study period to ensure that the “research does not exacerbate existing disparities”
in access to technologies (p. 132). To follow progress in telehealth research, bookmark the sites provided in Box 19-1.
The Foundation of Knowledge Model and Home Telehealth
There is much to learn about usual home telehealthcare service delivery, particularly to the elderly and chronically ill patients. Using
the Foundation of Knowledge model is key to learning how to use telehealthcare tools with typical patients (elderly patients, patients
needing pointed care) and operate effectively as telenurses. To understand the mechanics and effectiveness of home telehealth delivery
within the Foundation of Knowledge model, one must begin with a typical home telehealth case through which one can explore the
telenurse’s role in this model.
CASE STUDY: THE ROLE OF A HOME TELEHEALTH NURSE
Mrs. A. is an 84-year-old woman who was recently discharged from the hospital with a diagnosis that includes an exacerbation of
CHF. She also has diabetes and hypertension. Mrs. A. was discharged from the hospital on multiple medications and lives alone.
Home care services were initiated with skilled nursing care visits, some home health aide support, and orders to include daily
telemonitoring of her vital signs. The telehealth device will remotely monitor Mrs. A.’s blood pressure, heart rate, oxygen
saturation, and weight. In addition, the patient will answer customized questions about her disease on a daily basis. This
information will then be transmitted daily to the home care agency, where the telenurse can determine appropriate clinical actions
based on the data trends and preset baseline alerts that indicate when set parameters have been exceeded.
Knowledge Acquisition
In the previous case study, knowledge acquisition involves the telenurse receiving the information from the telehealth devices via a
variety of communication modes. For example, the telenurse receives the patient’s vital signs taken in the home and the patient’s
responses to customized questions. All of this information is transmitted to a remote server or site (a central station or website) that is
easily accessible to the telenurse.
Knowledge Processing
As a result of the telenurse’s knowledge acquisition, the next step to be followed is knowledge processing (i.e., understanding a set of
information and ways it can be applied to a specific task). In the case study, the telenurse assesses the patient’s vital signs along with
subjective data received from the patient as a result of customized questions that Mrs. A. is asked. For example, she might be asked if
she feels more short of breath today compared to a normal day. The telenurse then combines this information with the overall patient
history and diagnosis to get an up-to-date view of the patient’s status and considers where this information fits into the clinical picture
being presented for this patient.
As an example, the telenurse notes: Postacute heart failure patient shows trended data with weight gain of 5 lb over 2 days,
elevated blood pressure, decreased oxygen saturation, and answers yes to questions about increased shortness of breath and increased
fatigue. After processing all of the current information, the telenurse is able to target the appropriate next steps involving knowledge
generation and knowledge dissemination.
Knowledge Generation
By using her own nursing skills and clinical knowledge of the disease process, the telenurse considers all of the data as they apply to
Mrs. A. and decides the best course of action to take and acts on the data. The telenurse may, in addition, ask a variety of questions to
ensure that a complete and accurate decision about next steps for the patient is made. These questions might include the following:
Do I need to gather additional data?
Do I need to call the patient?
Do I need to call the physician and inquire about a change in the current plan of care?
Knowledge Dissemination
Finally, the telenurse determines how the knowledge will be used and disseminated. Various questions that were posed in the
knowledge generation stage are acted on, including the following possibilities:
Calling the doctor
Obtaining a change in medication order
Calling the patient and instructing her in a medication change
Reviewing activities that could have led to the changes (e.g., eating salty foods)
Educating the patient on the disease process, symptom management, and self-management techniques
Continuing to monitor the patient on an ongoing basis
Parting Thoughts for the Future and a View Toward What the Future Holds
Consumers will drive the way health care is delivered in the future. Consider that tomorrow’s healthcare facility might have no walls.
The evolving role of the Internet, personal health records, and telehealth all will support a more integrated healthcare model. This
convergence of trends and solutions will continue to expand with the introduction of new business practice models, such as retail
clinics and concierge care.
The care continuum will need to be supported by a clinical and caregiver structure that uses the data collected from patients to
make better and more informed healthcare decisions. Health parameter data could be used by the end user for personal direct care
decision making, or it may be used by a member of the healthcare community to determine appropriate healthcare interventions.
The technology of today will be different from the technology of tomorrow, as access to broadband communications systems,
acceptance of technology, and mobility and data transfer continue to evolve. As a result of emerging needs, many companies will enter
the market and offer a wide range of information technology tools, ranging from embedded and worn sensors to remote monitoring
devices. There will continue to be different user interfaces to receive customized health services, such as the television, cell phones,
and perhaps someday the iPod.
Clearly, by making key information readily accessible, solutions across all areas (home health, hospitals, and a range of other
settings) will facilitate collaboration in care delivery and health information. Products that integrate into consumers’ and patients’
everyday lives to improve the quality of life will continue to emerge. Telehealth and telenursing will play an important role in
improving quality of life and care for the patients served.
Foremost, nurses must be open to change and willing to embrace ever-evolving practice models. Tools should always be used to
improve care delivery models so as to make more targeted contact with and about patients. In an ideal world, there will be seamless
integration of clinical data systems and robust data exchange to provide quality care for patients no matter their location.
Summary
Telehealth is a rapidly developing mode of health service delivery in which nurses can expect to play a significant role. The most
promising area of concentration for nurses is in home telehealth care, an area that is expected to provide extensive care to the
burgeoning numbers of American elderly persons living with challenging chronic diseases and conditions. Many telecommunications-
ready tools to assist nurses in delivering this care are currently available, and their effectiveness in maintaining or improving patients’
health outcomes is well documented. Today’s nurses are providing telehealth services to typical home care patients (elderly patients,
patients needing regular and targeted care) and operating effectively as telenurses. The practice of telehealth will provide opportunities
for telenurses to become key players in care management across the healthcare continuum.
THOUGHT-PROVOKING
QUESTIONS
1. Will the increased use of telehealth technology tools be viewed as dehumanizing patient care, or will it be viewed as a means to promote more
contact with healthcare providers and as new ways for people to stay connected (as in online disease support groups), thereby creating better
long-term disease management and patient satisfaction?
2. Which types of resistance to new technologies might be evident among patients, caregivers, and nurses? Which evidence and strategies might
help to diminish these resistances?
3. As telehealth technology advances toward seamless data access regardless of distance or health system, how can patient privacy rights and the
confidentiality of personal medical data be protected?
4. Consider a recent patient care scenario and describe how it could have been managed at a distance.
• Which training would be needed?
• Which equipment would be used?
• How would the patient and his or her family respond to home telemonitoring?
References
Aarkstore Enterprise. (2010). Telemedicine market shares, strategies, and forecasts, worldwide, 2010 to 2016.
http://www.articlebuster.com/2010/03/telemedicine-market-shares-strategiesand-forecasts-worldwide-2010-to-2016-aarkstore-enterprise/
Allan, R. (2006, June 29). A brief history of telemedicine: Electronic design. http://electronicdesign.com/components/brief-history-telemedicine
American Association for Retired Persons (AARP). (1996). Understanding senior housing into the next century: Survey of consumer preferences,
concerns, and needs. Washington, DC: Author.
American Association for Retired Persons (AARP). (2009). Chronic conditions among older Americans.
http://assets.aarp.org/rgcenter/health/beyond_50_hcr_conditions
American Heart Association. (2007). Statistics you need to know: Statistics on medication. http://www.americanheart.org/presenter.jhtml?identifier=107
American Nurses Association (ANA). (2001). Developing telehealth protocols: A blueprint for success. Washington, DC: Author.
American Telemedicine Association (ATA). (2002). Home telehealth clinical guidelines. http://www.americantelemed.org/docs/default-
source/standards/home-telehealthclinical-guidelines ?sfvrsn=2
American Telemedicine Association (ATA). (2010). Telemedicine defined. http://www.americantelemed.org/i4a/pages/index.cfm?pageid=3333
Bendixen, R., Levy, C., Lutz, B., Horn, K., Chronister, K., & Mann, W. (2008). A telerehabilitation model for victims of polytrauma. Rehabilitation
Nursing, 33(5), 215–220. Retrieved from ProQuest Nursing & Allied Health Source (Document ID: 1550211771).
Bernier, L. (2001). Assessing respiratory status from a distance. Home Health Care Nurse, 19, 632–640.
Buerhaus, P., Auerbach, D., & Staiger, D. (2009). The recent surge in nurse employment: Causes and implications. Health Affairs, 28(4), 657–668.
Centers for Disease Control and Prevention (CDC). (2005). Chronic disease overview. http://www.cdc.gov/chronicdisease/overview/index.htm
Cepelewicz, B. (2003). Legal issues in telemedicine. In A. Kinsella (Ed.), Home healthcare: Wired and ready for telemedicine, the nurses’ and nursing
students’ edition (pp. AI–IX). Kensington, MD: Information for Tomorrow.
Chetney, R. (2006, July/August). What do patients really think about telehealth? In-depth interviews with patients and their caregivers. Remington
Report, 26, 28–29.
Craig, J., & Patterson, V. (2005). Introduction to the practice of telemedicine. Journal of Telemedicine and Telecare, 11(1), 3–9. Retrieved from
ProQuest Psychology Journals database (Document ID: 805776671).
Dansky, K., Vasey, J., & Bowles, K. (2008). Impact of telehealth on clinical outcomes in patients with heart failure. Clinical Nursing Research, 17(3),
182. Retrieved from Health Module (Document ID: 1521037211).
Demiris, G., Doorenbos, A., & Towle, C. (2009). Ethical considerations regarding the use of technology for older adults: The case of telehealth.
Research in Gerontological Nursing, 2(2), 128–136. Retrieved from ProQuest Nursing & Allied Health Source (Document ID: 1757456631).
Elliott, T., Brossart, D., Berry, J., & Fine, P. (2008). Problem-solving training via videoconferencing for family caregivers of persons with spinal cord
injuries: A randomized controlled trial. Behaviour Research and Therapy, 46(11), 1220. Retrieved from Psychology Module (Document ID: 1588
750351).
Goran, S. F. (2011). A new view: Tele-intensive care unit competencies. Critical Care Nurse, 31(5), 17–29. doi: 10.4037/ccn2011552
IMS Research. (2013). Telehealth to reach 1.8 million patients by 2017. http://www.imsresearch.com/press-
release/Telehealth_to_Reach_18_Million_Patients_by_2017
Institute of Aging, University of California at San Francisco. (1996). Chronic care in America: A 21st century challenge. Princeton, NJ: Robert Wood
Johnson Foundation.
Jia, H., Chuang, H., Wu, S., Wang, X., & Chumbler, N. (2009). Long-term effect of home telehealth services on preventable hospitalization use. Journal
of Rehabilitation Research and Development, 46(5), 557–566. Retrieved from Health Module (Document ID: 1884235751). Kaplan, B., & Litweka,
S. (2008). Ethical challenges of telemedicine and telehealth. Cambridge Quarterly of Healthcare Ethics, 17(4), 401–416. Retrieved from Health
Module (Document ID: 1540615491).
Kinsella, A. (1998). Home healthcare: Wired and ready for telemedicine, the second generation. Sunriver, OR: Information for Tomorrow.
Kinsella, A. (2002a). Predicting home healthcare needs: A next step in patient monitoring. Home Health Care Nurse, 20, 725–729.
Kinsella, A. (2002b). Wound care and telehealth. Telehealth Practice Report, 3, 9.
Kinsella, A. (2004). Obesity and new applications for home telehealth care. Home Health Care Technology Report, 1(3), 36–45.
Lancaster, P., Krumm, M., Ribera, J., & Klich, R. (2008). Remote hearing screenings via telehealth in a rural elementary school. American Journal of
Audiology, 17(2), 114–122. Retrieved from ProQuest Nursing & Allied Health Source (Document ID: 1623340331).
Leimig, R., Gower, G., Thompson, D., & Winsett, R. (2008). Infection, rejection, and hospitalizations in transplant recipients using telehealth. Progress
in Transplantation, 18(2), 97–102. Retrieved from ProQuest Nursing & Allied Health Source (Document ID: 1537443161).
National Pharmaceutical Council. (2013). Medication compliance/adherence. http://www.npcnow.org/issue/medication-complianceadherence
Pennsylvania Homecare Association & Pennsylvania State University. (2004). 2003–2004 telehealth project evaluation year two: The impact of
telehealth on nursing workload and retention [Unpublished report].
Polisena, J., Coyle, D., Coyle, K., & McGill, S. (2009). Home telehealth for chronic disease management: A systematic review and an analysis of
economic evaluations. International Journal of Technology Assessment in Health Care, 25(3), 339–49. Retrieved from ProQuest Nursing & Allied
Health Source (Document ID: 1797842261).
Prial, S., & Hoss, S. (2009). Overview of home telehealth. In D. McGonigle & K. Mastrian (Eds.), Nursing informatics and the foundation of knowledge
(pp. 265–281). Sudbury, MA: Jones and Bartlett.
Quality Insights of Pennsylvania. (2005). Home telehealth reference 2005 [Unpublished report]. Remington, L. (2007, January/February). Healthcare
megatrends, predictions and forecasts.
Remington Report, 15, 5–10.
Sanderson, S. (2007, July/August). Cardiopulmonary disease management: A patient-focused approach to home health care. Remington Report, 15, 46–
47.
Skiba, D. J. (1998). Health-oriented telecommunications in nursing informatics. In M. J. Ball et al. (Eds.), Where caring and technology meet (pp. 40–
53). New York, NY: Springer.
St George, I., Baker, J., Karabatsos, G., Brimble, R., Wilson, A., & Cullen, M. (2009). How safe is telenursing from home? Collegian: Journal of the
Royal College of Nursing Australia, 16(3), 119–123.
Stern, D. J. (2007, January/February). Intuitive system monitors resident behavior patterns. Assisted Living Consult, 3(1), 21–25.
Strauss, A. (2010). Robot doctors bring specialty care to rural areas. http://suite101.com/a/robot-doctors-bring-specialty-care-to-rural-areas-a231775
Swinton, J., Robinson, W., & Bischoff, R. (2009). Telehealth and rural depression: Physician and patient perspectives. Families, Systems & Health,
27(2), 172. Retrieved from Health Module (Document ID: 1793129471).
U.S. Department of Commerce, Bureau of the Census. (2006). Oldest baby boomers turn 60! http://www.census.gov/newsroom/releases/pdf/cb06-
ffse01-2
U.S. Department of Commerce, Bureau of the Census. (2010). The next four decades: The older population in the United States: 2010 to 2050.
http://www.census.gov/prod/2010pubs/p25-1138
U.S. Department of Health and Human Services, Health Resources Services Administration (HRSA), Bureau of Health Professions. (2002). Projected
supply, demand, and shortages of registered nurses: 2000–2020.
http://www.ahcancal.org/research_data/staffing/Documents/Registered_Nurse_Supply_Demand
U.S. Department of Health and Human Services, Health Resources Services Administration (HRSA), Bureau of Health Professions. (2004). The
registered nurse population: Findings from the 2004 National Sample Survey of RNs.
http://bhpr.hrsa.gov/healthworkforce/rnsurveys/rnsurvey2004
Venable, S. (2005). A call to action: Georgia must adopt a new standard of care, licensure, reimbursement, and privacy laws for telemedicine. Emory
Law Journal, 54(2), 1183–1217. Retrieved from Law Module database (Document ID: 875322011).
Williams, L., Hubbard, K. E., Daye, O., & Barden, C. (2012). Tele-ICU enhancements: Telenursing in the intensive care unit: Transforming nursing
practice. Critical Care Nurse, 32(6), 62–69. doi: 10.4037/ccn2012525
* This section is adapted from Prial & Hoss, 2009.
* This section is adapted from Prial & Hoss, 2009.
Section V
Education Applications of Nursing Informatics
Chapter 20 Nursing Informatics and Nursing Education
Chapter 21 Simulation in Nursing Informatics Education
Chapter 22 Games, Simulations, and Virtual Worlds for Educators
Nursing informatics (NI) provides more tools and capabilities than can at times be imagined. Just as NI has changed the way nursing is
administered and practiced, so it has also dramatically affected educational practices.
Nursing education is evolving with the increased integration of NI tools to promote learning. The tools that are available must be
used prudently by reflecting on and applying knowledge on teaching styles, learning styles, and other pedagogic concerns. As
informatics capabilities continue to expand, a phenomenal amount of potential for virtual reality–embedded education looms on the
horizon. Once the purview of gamers and geeks, virtual reality has exploded onto the academic scene. The use of virtual reality has the
potential for cross-pollination between fields of inquiry across the curriculum, the university, and even learning systems. Many
university departments will experiment with virtual reality in hopes of staying current and appealing to their young and demanding
“Generation Next” constituency. However, much of society loves the feel of books too much to dismiss them as archaic. There is room
for both books and technology in education. Students, educators, and administrators will ultimately return to a modified form of face-
to-face classroom teaching, even with the availability of newer and more adventuresome teaching technologies. Furthermore, after fast,
highburn technologies stop flooding the marketplace and big business provides opportunities for proprietary online universities,
modified traditions will take their place, creating new spaces for nontraditional students and members of the Net Generation, with both
being anxious for technology use in the classroom for very different reasons.
The material in this book is placed within the context of the Foundation of Knowledge model (Figure V-1) to meet the needs of
healthcare delivery systems, organizations, patients, and nurses. Nursing education promotes scholarship and evidence-based teaching
and learning. Through the sound integration of information management and technology tools, teaching and learning strategies
promote the social and intellectual growth of the learner. As teachers and learners quest for knowledge, the need for pursuit of lifelong
learning is instilled. Teachers and learners involved in the process of education are also involved with all levels of the model.
Typically, they acquire and process data and information and generate and disseminate knowledge within the frame of reference of
their educational institution. Their knowledge generation remains on a limited, individual/course/school basis unless they become
involved with developing publications and educational research that informs others in the nursing profession.
The reader of this section is challenged to ask the following questions: (1) How can I apply the knowledge I gain from my
education to benefit my patients and enhance my practice? (2) How can I help my colleagues, patients, and fellow students understand
and use the current technologies to promote learning? (3) How can I use my wisdom to help create the theories, tools, and knowledge
of the future?
Figure V-1 Foundation of Knowledge Model
Source: Designed by Alicia Mastrian.
Chapter 20
Nursing informatics and nursing education
Heather E. McKinney, Sylvia DeSantis, Dee McGonigle, and Kathleen Mastrian
OBJECTIVES
1. Describe nursing education in relation to the Foundation of Knowledge model.
2. Explore knowledge acquisition and sharing.
3. Assess technology tools and delivery modalities used in nursing education.
4. Compare and contrast knowledge assessment methods.
Key Terms
Advocate
Asynchronous
Audiopod
Avatars
Blended hybrid
Blog
Case study
Collaboration
Compact disk read-only memory (CD-ROM)
Computer-assisted instruction (CAI)
Computer based
Continuing education
Copyright
Digital pen
Digital versatile disk or digital video disk (DVD)
Distance education
E-learning
Electronic mailing list
E-mail
Face to face
Fair use
Foundation of Knowledge model
High fidelity
Hybrid Hypertext
Information literacy
Instant message (IM) iPod
MP3 aggregator
Multimedia
Net Generation
Online chat
Personal digital assistant (PDA)
Podcast
Portal
Portfolio
Problem based
Real time
Really simple syndication (RSS)
Resource description framework (RDF)
Role playing
Scenario
Second Life
Simulation
Smartphone
Tutorial
Videopod
Virtual reality
Web based
Web enhanced
Webcast Webinar
Wi-Fi
Wiki
Introduction: Nursing Education and the Foundation of Knowledge Model
Nursing informatics facilitates the integration of information, data, and knowledge to support nurses, patients, and other providers in
their various settings and decision-making roles (Carty & Ong, 2006). The Foundation of Knowledge model specifically prompts
nurses to extend their theoretical and metaphorical knowledge into practical, holistic determinations based on a variety of factors and
contexts. Because competencies in informatics include but are not limited to information literacy, computer literacy, and the ability to
use strategies and system applications to manage data, knowledge, and information (American Nurses Association, 2000), the ability
of nursing students to use computer-mediated communication skills is essential to their success in the nursing field and as a means to
improve patient safety.
The rise of telecommunications, computer-mediated communications, and virtual technologies has opened up opportunities for
improving communication and extending care within the healthcare industry (Barnes & Rudge, 2005). Proponents of instructional
applications of computer technology view it as a way to erase geographic boundaries for students, enhance the presentation of content,
improve learning outcomes, and even tailor instruction to individual learning needs. When carefully matched with curricular
objectives, technology becomes an efficient and affordable avenue through which nursing faculty may provide useful knowledge to
their students, thereby facilitating the learning process (Hebda, Czar, & Mascara, 2005). Now going far beyond the simple applications
of word processing software or spreadsheets, technology applications have evolved greatly, taking advantage of modern capabilities to
provide nursing and related healthcare students with simulations, complex multimedia, virtual reality–assisted clinical scenarios, and a
host of information and literature-gathering Internet tools.
Knowledge Acquisition and Sharing
The shift from computer literacy to information literacy and management has drawn attention to interactivity and design as the most
important components of interactive Web-enhanced and Web-based courses in providing effective learning environments. Thurmond
(as cited in Carty & Ong, 2006, p. 523) discusses the four types of interactions related to Web-enhanced courses: (1) learner–learner,
(2) learner–content, (3) learner–instructor, and (4) learner–interface interactions. In traditional learner–learner exchanges, students
interact with one another to troubleshoot, work out challenges, and exchange solutions generated from different perspectives.
Traditional and familiar, both learner–content and learner–instructor interactions expect students to work directly with course content
or the faculty member and then participate in relevant course activities, such as tests and reviews. Learner–interface interaction
includes the ways students access their coursework and their ultimate success or failure in finding, retrieving, and using what they need
(Carty & Ong, 2006). When Web enhanced, these interactions include online chats, forum discussions, participation in electronic
mailing list groups, instant messaging, blogging, and use of e-mail, all of which ask the student to engage, digest, use, and disseminate
information in new ways.
Hardware and Software Considerations
In the 21st century, nursing informatics has begun to rely heavily on technology usability, functionality, and accessibility. Computer-
assisted instruction (CAI) has had an enormous impact on nursing informatics, with many CAI programs offering individualized
instruction in the form of customizable scenarios, frameworks, and programs for study. Additionally, CAI contributes to better
understanding of material by supporting all learning styles, types, and paces. Consequently, nursing skills have presented endless
potential for software development, making the effective use of software and hardware by educators and students a prime necessity
(Riley, 1996).
Software comprises the instructions that direct a computer’s hardware to work, whereas hardware consists of physical computer
components, such as a mouse, keyboard, and monitor. Software essentially translates commands into computer language, allowing the
hardware to perform its functions. Without hardware and software, computer technologies are moot; moreover, without software,
hardware does not function (McHugh, 2006). Applications software refers to the various programs individuals use to communicate
with others, do work, play games, or watch multimedia on a computer. The most common software package sold with computers is an
office package, which generally includes a word processing program, spreadsheet capability, a presentation graphics program (e.g.,
PowerPoint), and some kind of database management system. Software packages are available on compact disk read-only memory
(CD-ROM), on digital versatile disk or digital video disk (DVD), or through the Internet, allowing the user to download the
software directly from a vendor’s website (McHugh, 2006).
When evaluating software or hardware for purchase, careful assessment of the products and services will help an educator,
administrator, or student make the best choices. Most important when evaluating software is to understand how well the software’s
functionality matches the learning goals and objectives. Although many programs are available for assisting a nurse practitioner who is
evaluating software for particular learning purposes, the main criteria concern content (Is the information accurate? Is it relevant?),
format (How is information visually presented? Is it in frames? Does it come with graphics?), documentation style (What is the tone?
Is it scholarly and applicable?), and strategies (Is the software useful for all students, including remedial students and accelerated
students?) (Edwards & Drury, 2000).
Hardware decisions depend on the way a computer system will be used, in addition to considerations related to cost, ease of use,
and durability (Clochesy, 2004). Systems purchased for personal use may differ dramatically from those purchased for online learning
laboratories or smart classrooms. Because the technology inherent to workstations, servers, and computers in general tends to change
quite rapidly, discussing large system decisions with an information technology expert is likely to yield a betterinformed decision.
Some factors to consider include where the system will be stationed (e.g., at home for personal use or in a learning laboratory for use
by many students), how many desktops there will be (e.g., one or a few dozen), if it will be networked to a school’s internal system, if
printing will be available, and which level of security is needed (Hebda et al., 2005).
Delivery Modalities
Nursing educators are discovering that today’s students may not always respond in the same ways the educators did during their own
tenure as students. Technologysavvy students from the millennial age demand instant information delivered in an entertaining fashion
—an expectation built on extensive exposure to e-mail, text messaging, online chatting, and the Internet (Ridley, 2007). Additionally,
many nursing departments are facing an increase in student enrollment and a corresponding growth in faculty. Although new nursing
faculty bring significant clinical experience to their academic positions, also apparent for some is an underlying tension and
unfamiliarity with technologic advances, outcomes-based accreditation initiatives, and teaching itself. Schools of nursing are
scrambling to provide professional development for busy nursing faculty and introducing them to best practices in teaching (Shaffer,
Lackey, & Bolling, 2006).
Learning is a multispatial function, and in the age of technology innovation, instructional delivery can inhabit many forms in both
physical and virtual spaces. Spaces in academia are no longer defined by a class or its content, but instead by the learning the class is
trying to promote. To this end, learning spaces should support multiple modes of learning and delivery, including reflection,
discussion, and experience, and should facilitate face-to-face and online interaction within and beyond classrooms. Truly innovative
delivery, whether face-to-face classroom interaction, online engagement, or a blended hybrid of technology and traditional classroom
teaching, supports learning activities rather than standing independently of them (Oblinger, 2005).
Face-to-Face Delivery
Ridley (2007) suggests that although it is the most widely used teaching method among nurse educators, traditional face-to-face
lecture yields only a 5% information retention rate over a 24-hour period, a rate that compares unfavorably with demonstration (30%),
discussion groups (50%), practice activities (75%), and peer teaching (90%) (as cited in Sousa, 1995).
Additionally, the inability of physical space to keep pace with evolution of learning models inhibits the benefits gained from face-
to-face interaction between teacher and student. For example, collaborative learning grinds to a halt when class is held in a room with
chairs bolted to the floor, facing a lectern (Oblinger, 2005); this kind of spatial arrangement prohibits a sense of classroom community
by inhibiting easy peer interaction, reducing students’ ability to see one another, and concentrating all attention on the professor.
Conversely, in a collaborative learning environment, the professor guides conversation and sets up discussion, acting less as
classroom authority and more as facilitator, helping students maintain focus, gently guiding discussion, and ultimately empowering
students to push knowledge boundaries in a safe and secure atmosphere of peer support. This inductive, epistemological approach
promotes active, critical thinking skills and assists students in learning not just facts, but how to learn. As future healthcare
professionals determined to rely on quantification and rationale, nursing students will benefit from face-to-face classroom interaction
that hones their ability to manufacture new personal truths through interaction with people and ideas in ways that cannot always be
measured and counted.
Ridley (2007) suggests that such interactive, cooperative learning strategies might include gaming, role playing, and problem-
based learning. Because games are nonthreatening and fun, they promote critical thinking and teamwork by pushing students to work
together in groups to find answers and achieve success. Role playing is similar in that it allows students to try on real-life scenarios by
filling either prescripted or ad-libbed roles (doctor, nurse, patient, clinician, and so forth) without the fear or pressure of putting
another’s life at risk while trying to determine the best course of action or find a solution for a fictitious patient’s health issue.
Problem-based learning, a well-accepted form of interactive learning, takes assignments out of a contextual vacuum and applies real-
life scenarios to problems or challenges. Students work in groups to solve the dilemma presented by real patient cases and build on
prior knowledge, using higher-level thinking skills and progressive inquiry to resolve the problem (Ridley, 2007).
Online Delivery
E-learning, online learning, and Web-based education have caused a significant shift in student–teacher relationships in nursing
education. Indeed, according to Phillips (1999), “the Internet will offer a chance to make real profits from education, while offering
students a choice about who will educate them and at what price” (para. 14). According to Oblinger (2005), not only are learning
spaces no longer physical or formal, especially on campuses with wireless capabilities, but nursing students also expect to make use of
wide ranges of cutting-edge technology during their academic tenure, exchanging the traditional “sage on the stage” for a
technologically savvy “guide on the side” (Leasure, Davis, & Thievon, 2000) who gives up the role of gatekeeper and instead
promotes and facilitates dialogue as central to teaching–learning (Aquino-Russell, Maillard Strüby, & Reviczky, 2007).
Student-centered and no longer limited to the domain of the classroom, laboratory, or even a patient’s bedside, online learning
allows educators to translate theory into practice, creating a virtual classroom space that promotes collaboration, engagement,
discussion, and analysis. Detractors of online learning initiatives suggest, however, that sharing an online space undermines the
student–teacher relationship, makes building peer relationships difficult, and generally disrupts the normal classroom dynamic, thereby
creating an unfamiliar, uncomfortable atmosphere. Despite these concerns, studies show that not only do Web-based courses continue
to gain in popularity, but they also enhance learning in ways that encourage students to share personal experiences and support.
Researchers cite many factors that make online learning laudable, with accessibility and convenience being two of the most frequently
cited issues (Aquino-Russell et al., 2007).
The asynchronous and time-independent elements of Web-based courses respond to the huge need for flexible class times among
today’s growing population of nontraditional learners. Additionally, Web-based and place-independent learning allows participation
by anyone, anywhere in the world, with access. Related to this issue is the democratizing effect of online learning, such that all
students have the same opportunity to participate without judgment. Web-based classes provide an easily accessible permanent record,
a convenience for both teachers and learners (Aquino-Russell et al., 2007).
It is important to use tools that facilitate learning, such as the introduction of social media into nursing education. Twitter
(www.twitter.com) can be used to focus and hone student perspectives. Each posting or tweet cannot exceed 140 characters. As they
critically think about what they want to add to the discussion, students must act as wordsmiths to express their views succinctly given
the character limitation; that is, they must present their viewpoints concisely. Other social media that can be used in education include
the following sites:
Flickr (www.flickr.com), for photo sharing
Glogster (www.glogster.com), a graphical blog
Pixton (www.pixton.com), to create comics or cartoons
Prezi (www.prezi.com), to create zooming presentations
Slideshare (www.slideshare.net), a community for sharing presentations
YouTube (www.youtube.com), to watch and share videos
Wordle (www.wordle.net), to create word clouds from text
These sites can be used by students to facilitate their presentations and team collaboration. The use of social media not only exposes
the students to their use but also promotes the development of skills that will support professional collaboration as students enter the
practice arena.
Online learning has the capability to reenvision classroom interaction and, depending on the specific delivery mode, can even
change basic pedagogic concepts. The best online delivery (whether through a chat, blog, or class), just like traditional classes in a
physical space, adheres to solid teaching parameters that ensure student investment and success. These include providing students with
a clear set of learning activities and expected outcomes; relating content to real situations using case studies and simulation (problem-
based learning); building in collaborative activities, such as team projects; building in an instructor’s personality and presence; and
setting up technology such that users build their confidence rather than experience failure and frustration (Winfield, Mealy, &
Scheibel, 1998). Refer to Box 20-1 for more information.
Hybrid or Blended Delivery
Traditional courses are more frequently being offered as online, virtual classes (i.e., distance education)—learning that occurs
elsewhere than in the traditional classroom and consequently requires special course design, planning, techniques, and communication.
A hybrid of this delivery mode includes learning in which traditional classroom time is enhanced or broken up with online
components, thereby creating a class in which blended hybrid learning occurs. Forms of hybrid learning include Webenhanced
learning and learning that takes place in and makes use of smart classrooms (e.g., teaching in a wired room equipped with classroom
learning technologies, such as the Blackboard Learning System).
Web-enhanced instruction, such as asking students to blog responses to a reading or class discussion, allows technically
ambivalent institutions to participate in the technology revolution without a huge budgetary expenditure and also addresses the
preference of some faculty for a way to include innovation and technology in classes without giving up traditional classroom
engagement. Syllabus magazine has reported that most distance learning courses use up to five media to reach students, including
chats, telephone calls, forums, student webpages, and group pages, in addition to print materials from laboratory manual study guides
and textbooks, and samples of student work, quizzes, and tests (Hibbison, 2001). Faculty are reaching for multiple media and hybrid
approaches to encourage student-centered learning, maximize teaching effectiveness, and take advantage of the best on-campus and
online teaching and learning (Black & Watties-Daniels, 2006). Box 20-2 provides more information on this trend.
BOX 20-1 A DAY IN THE LIFE
Eeric Doerfler
Some days, I feel as if I cannot get away from technology. I teach three classes that I keep tabs on through a course management system. It is a great
system, but when the network is down, it is hard to get anything done. I am a teaching assistant in another university, and that program also is all
online. Every workday, I spend time in the electronic world of my students. Of my own courses I can say this: At least every 2 weeks I see their
nonvirtual faces. I have a hand-held computer for my schedule and my address book. I use software for my finances, and that synchronizes to the
handheld computer. I type my patient notes into a word processor. When someone calls with a problem, I can boot up my laptop and get a reminder
of what I did and why.
That is the upside. Although I sometimes feel too dependent on complex machines for what was once simpler, the access I have and the options I
am offered have changed my daily life. Online discussion boards offer reticent students a less threatening environment in which to participate, so I
see more class participation. Posting articles online reduces my department’s copying costs and logistical aggravation. If I see a good article, I do not
need to print it, send it down to the copy center, and wait. My calendars are backed up; if one computer does not work, another one does, and there is
no way for me ever to lose my address book, check register, or other key documents!
It is more expensive, however, to live this way. Computers, peripherals, and ink cartridges all cost money. Life was cheaper when I bought a new
day planner each December and retired the last version. Electronic health records almost drove my practice budget into deficit until I went back to
typing them into the laptop and saving and printing them. As I make my way through each day, I do sometimes find myself wondering how and
when this can all become less bothersome, disjointed, and complex, and more simple, interconnected, private, and reliable, without driving me and
all of us into the poorhouse! Although I teach informatics, as I go along I find that less technology is more, and I have started to walk by all the latest
gadgets, waiting for the inevitable evolution that will marry me to my machines without spoiling my humanity or my peace of mind.
Smart classrooms, also known as digital and multimedia classrooms, integrate computer and audiovisual technologies by providing
a ceiling-mounted projector with an access point at the front of the room, an instructor podium or workstation, sound, and network
access. An enhanced smart classroom also provides networked student workstations instead of traditional desks, allowing students to
follow along online and perform network or Web searches, chat, blog, or myriad other activities as dictated by the professor. For
example, at the Penn State School of Nursing, users can access announcements, course materials, faculty information, websites, and
other tools through Blackboard, enabling the nursing faculty to extend learning beyond the physical classroom walls.
Surveys conducted by WebCT, an online virtual learning environment system created in 1995 and now slowly being phased out in
favor of other systems, confirm the growing realization among faculty that traditional campus courses can benefit from the interactive
components of a website, forums, and chat and, conversely, that online courses can benefit from face-to-face contact. In another
survey, faculty showed a strong preference for Web-enhanced classroom instruction over either traditional classroomonly instruction
or online-only distance education, observing that student achievement is maximized in courses that combine online and classroom
elements (Hibbison, 2001).
BOX 20-2 OPEN SOURCE AND MOODLE, A COURSE MANAGEMENT SYSTEM
Dee McGonigle and Kathleen Mastrian
An open source approach is being used by many organizations in both business and health care. Open source refers to standards and guidelines for
writing software open source code. The power comes in the fact that it is often free to use. “Open source is a development method for software that
harnesses the power of distributed peer review and transparency of process. The promise of open source is better quality, higher reliability, more
flexibility, lower cost, and an end to predatory vendor lock-in” (Open Source Initiative, 2007, para. 1). Graham (2005) adds, “Ten years ago there
seemed a real danger Microsoft would extend its monopoly to servers. It seems safe to say now that open source has prevented that. A recent survey
found 52% of companies are replacing Windows servers with Linux servers” (para. 1). Open source is changing the way healthcare and education
organizations alike conduct their business. Educators are looking to open source software to help manage their online courses because they can
achieve flexibility, reliability, and lower costs through this approach.
Modular Object-Oriented Dynamic Learning Environment (Moodle) is an open source course management system or learning management
system. “About Moodle” (2008) describes Moodle as open source software for “producing Internet-based courses and web sites” (para. 1). “What Is
Moodle?” (n.d.) describes it as a “learning management system that lets you provide documents, graded assignments, quizzes, discussion forums, etc.
to your students with an easy to learn and use interface. Moodle is developed by a worldwide effort of over 75,000 students, faculty, and staff at over
6500 institutions around the world” (para. 1). Munoz and Van Duzer (2005) compared Blackboard and Moodle, describing Moodle as “Open source
(free!). Customizable by programming staff. Flexible for the instructor and developer. Supported by programmers world-wide” (para. 2).
The appeal of open source code and the promise of free or low-cost globally supported software continues to grow. As more and more people
turn to open source approaches, the more robust they will become, because open source initiatives represent a community effort between users and
programmers.
REFERENCES
About Moodle. (2008). http://docs.moodle.org/en/About_Moodle
Graham, P. (2005). What business can learn from open source. http://www.paulgraham.com/opensource.html
Munoz, K., & Van Duzer, J. (2005). Blackboard vs. Moodle: A comparison of satisfaction with online teaching and learning tools. Retrieved July
2010 from http://www.humboldt.edu/~jdv1/moodle/all.htm
Open Source Initiative. (2007). Home. http://www.opensource.org/
What Is Moodle? (n.d.). Retrieved July 2010 from http://www.humboldt.edu/~moodle/whatis.html
Technology Tools
Members of the Net Generation or Millennial Generation (students who have grown up inside a wired world of instant access and
online everything) are connected, digital, experiential, and social. Working in teams comes naturally to this peer group, and interacting
in peer-to-peer situations is a familiar and common learning mode. These students desire information immediately, are skilled
multitaskers, and are, according to Oblinger, cited in a 2005 Educause learning initiative, “no longer the people our educational system
was designed to teach” (Prensky, 2001). Certain social trends emerging from the morass of both traditional and innovative technology
tools include the use of technologies attempting to meet the needs of these new learners; through the use of software, hardware,
drivers, dedicated servers, plug-ins, and an Internet connection, students can chat, collaborate, play games, or interact electronically
with a peer in some way, all with little to no learning curve or effort. Because visual media are now the vernacular of this highly digital
culture, students and faculty are also embracing technology tools that allow for the creation and interpretation of visual images
(Oblinger, 2005). Such tools might take the shape of interactive tutorials, a created city within a virtual reality landscape, or even a
multimedia action maze that prompts users to choose different outcomes within a scenario. Regardless of the particular tool employed,
technology can perform only as well as the pedagogy that drives it, thus creating a need for integration, support, and sustainability
within nursing education programs willing to implement new instructional and assessment strategies (Bassendowski, 2005). Box 20-3
offers more information on one technology used within this milieu.
BOX 20-3 DIGITAL PEN TECHNOLOGY TOOL
Dee McGonigle, Kathleen Mastrian, and Nedra Farcus
Digital pens are actual writing implements that can also digitally capture handwriting or drawings. These batteryoperated devices generally come
with a universal serial bus (USB) cradle that permits uploading captured materials to the desktop, laptop, or palmtop computer. Scribes can use them
as a ballpoint pen and write on regular paper just as they would with a normal pen. To capture what one is writing or drawing digitally, it is necessary
to write on digital paper. This paper differs from regular paper in that it contains small dots that permit the digital pen to see what the user is writing
or drawing so it can be captured digitally. Some manufacturers are trying to create digital pens that use regular paper. If the user wants to capture
what he or she is working on, the pen must be instructed to save the work. There are memory limitations when using this technology; the memory
will generally hold around 40 pages of captured digital paper.
Digital pens have been around for a while, and some of the earlier models are probably still collecting dust in someone’s office. A new
resurgence in the digital pen, however, is reflective of the advances in technology. As Fried (2008) notes:
One thing all the new products have going for them is that they come at a time where Windows’ support for digital ink has never been better.
Some of these new marvels use Bluetooth wireless technology to send captured data directly to a computer. The file formats vary by
manufacturer but are typically GIF or JPEG, making the files easily shared and supported.
Think of the educational applications of being able to capture 40 pages of material and upload it to a computer for share and exchange. What
about the impact on patient care? The city of Stockholm is slated to use digital pens in its healthcare delivery system. According to an article (“City
of Stockholm to Use Anoto’s Digital Pen to Improve Elderly Care Services,” 2007),
The Anoto digital pens will primarily be used to facilitate documentation and information transfer, in order to improve the quality of elderly
homecare in the city. (para. 4)
The technology has already been deployed successfully in Solna and Sundbyberg, two towns north of Stockholm, where homecare nurses use
the digital pen to register their arrival and departure times on each visit and tick off the services delivered to each patient, on a digital form.
(para. 5)
The Logitech digital pen system can be used to document assessments. A digital camera in the pen captures a watermark pattern when the user
writes on interactive paper. The pen is then set in a USB cradle, and the data are uploaded into the computer, where they are converted to text. The
use of such a digital pen system enables students to complete an assessment in the home with pen and paper and share the information with their
professor or a research team for analysis within a short period of time.
The digital pen is being used for many applications in education and healthcare. Would this be something you would use?
REFERENCES
City of Stockholm to use Anoto’s digital pen to improve elderly care services. (2007, September). Wireless News, 1. Retrieved from ABI/INFORM
Trade & Industry database (Document ID: 1343068491).
Fried, I. (2008). Is the digital pen mightier? http://www.usatoday.com/tech/products/cnet/2007-08-24-digital-pen_N.htm
Tutorials
Academic institutions face a multitude of challenges in trying to satisfy the information needs of users who are inundated daily by tons
of information as part of their regular Web use. In addition to facing a technologic revolution, it seems that academia is facing a
pedagogic revolution, as educators attempt to meet students’ needs in innovative and engaging ways. One solution to providing
students with information and the skills needed to find, evaluate, understand, and apply this information comes in the form of online
tutorials (Bracke & Dickstein, 2002).
Modern tutorials mimic lectures by guiding users through a series of objectives or tasks, usually allowing the user to do the work at
his or her own pace (Edwards & Drury, 2000). Tutorials generally stand alone as autonomous multimedia that may use animation, text,
graphics, sound, questions, and different kinds of interactivity to engage and intrigue the user. They tend to promote active learning by
prompting the user to answer sets of questions, follow clickable hypertext, or complete quizzes. For example, users might be asked to
fill in worksheets after reviewing anatomy concepts, take a quiz, post an answer to a question, or even click through a scenario by
choosing the best course of action in a mock clinical situation.
Some tutorials, such as those used by medical students at the Morgan Stanley Children’s Hospital of New York, are designed to be
brief (10 minutes), interactive, very focused, and immediately relevant. In this case, medical students bustling through a busy clinical
rotation who accessed the tutorials actually raised examination grades (Pusic, Pachev, & MacDonald, 2007).
Because most students benefit from being able to contextualize a lesson’s framework and purpose, the most effective tutorials
provide users with understandable navigation, such as a table of contents at its beginning, or additional navigational aids, such as
icons, buttons, or text that indicate where and how they need to progress (Dewald, 1999). For example, Penn State University Libraries
offers a nursing tutorial specifically designed to teach the tenets of evidence-based practice
(http://www.libraries.psu.edu/psul/tutorials/ebpt.html). Although any piece of the tutorial may be accessed at any time, the tutorial
prompts the user to follow a linear path in applying the four basic steps of evidence-based practice to make the process easier and more
understandable.
Effective tutorials surpass the simple presentation of information in a Web-based format; they instead address certain pedagogic
and student-centered needs by identifying and taking into consideration specific factors, such as instructional content, the educator’s
purpose and teaching goal, the initiative’s overall purpose, the potential need for special conceptual input, the learners’ ultimate
objectives in completing the tutorial, and the standards that determine what qualifies as successful completion of the tutorial (DeSantis,
2002).
Although most tutorials are created to stand alone, some may also benefit and supplement face-to-face instruction, such as the
interactive information skills tutorial developed at the Institute for Health and Social Care Research in Salford, United Kingdom. This
tutorial divides a traditional lecture series into chunks, incorporating questions that would normally arise during the session into the
text, and providing hyperlinks. This organization allows users to browse to different parts of the tutorial, open a database in a new
window to perform a practice search, and access other features (Grant & Brettle, 2006). Tutorials in all their iterations urge students to
hone and develop effective critical thinking skills.
Simulations
Used within healthcare circles for more than 15 years, the use of simulations in nursing education have experienced a recent upsurge
in popularity, in part due to the more widespread availability of high-quality simulation equipment and a reduction in price for this
technology. Ranked by fidelity, or the level of realism the equipment resembles, simulation may take various forms: from computer-
based simulation, in which software is used to simulate a subject or situation (e.g., an interactive tutorial featuring a nurse–patient
situation), to full-scale simulation, in which all the elements of a healthcare situation are recreated using real physiology, people, and
interaction to resemble an environment as closely as possible to immerse students in the experience (Seropian, Brown, Gavilanes, &
Driggers, 2004).
Task and skill training ranks as the most popular form of simulation. During this type of simulation, students hone repetitive skills
through interaction with a wide range of equipment, including such products as low-fidelity plastic intravenous arms and high-fidelity
virtual reality trainers. The expectation is that students will learn to respond to a situation through a repeated practice-and-learn model
of knowledge acquisition; however, because even basic nursing skills, such as administering an injection, require technical skill and
interpersonal skill, task and skill training enjoys only limited usefulness within the Foundation of Knowledge model without additional
clinical exposure or high-fidelity simulation. Even reasonable anatomic fidelity cannot simulate accurately the intensity and vagaries
inherent in clinical situations, thus making it difficult for the student accurately to assess and integrate his or her clinical judgment,
knowledge, and acquisition of skills (Seropian et al., 2004).
The most useful teaching simulations combine high-fidelity equipment with realtime demonstrations of simulated medical
emergencies, such as those enacted by the patient simulator laboratory at the Patient Safety Institute, a training subcenter of the North
Shore–Long Island Jewish Health System. Home to programmable human simulator mannequins (including an infant) that can exhibit
a variety of symptoms resembling various patient scenarios, the patient simulator laboratory tests and trains nurses and staff in
responding to simulated medical emergencies using real-time demonstrations and gives immediate feedback on their video-recorded
activities. Intended to help improve both clinical and decision-making skills, the mannequins mimic human responses, such as
breathing, coughing, and speaking, and are anatomically accessible in their ability to be intubated, catheterized, and auscultated
(Spillane, 2006).
The richest and most educationally useful simulation training for students results from a combination of best practices, current
pedagogy, appropriate choice of simulation, clinical experience, and theory. See the Simulation in Nursing Informatics Education
chapter for a comprehensive discussion of simulation.
Virtual Reality
In traditional virtual reality, the user receives multiple sensory inputs, either mediated or generated by a computer, through visual
stimulation (glasses, goggles, and screens), audio input (earphones, microphones, and synthesizers), and touch (smells, gloves, and
bodysuits). A form of simulation training that was once considered a science fiction technology of the future, virtual reality healthcare
training has been widely used by medical students and surgeons in training in recent years, as it allows individuals to practice an
operation before working on a real patient (Turley, 2000). The current spotlight in virtual reality systems focuses on Second Life, an
online virtual world created by San Francisco-based Linden Lab in 2003 that has multiple teaching applications.
Virtual worlds are online environments in which the residents are avatars who, in turn, represent individuals participating online.
Virtual world participants have an opportunity to design nearly every aspect of their environment, from their avatars’ clothing, gender,
and appearance (EDUCAUSE, 2006), to more complex elements that openly lend themselves to teaching approaches supported by the
Foundation of Knowledge model, such as how the avatars communicate, move, interact, and create. Because virtual worlds are so
versatile in their setup and preselected environments are highly customizable, healthcare instructors have the opportunity to create
learner-led scenarios, rather than relying on outcome-based models of knowledge development (EDUCAUSE, 2006), thereby creating
opportunities for “mediated immersion” (Dede, 2005), a “neomillennial learning style” (Skiba, 2007b, p. 156) that promotes the ability
to negotiate multiple media and simulation-based virtual settings and communal learning, providing an important and valuable balance
among experiential learning and guided mentoring (Dede, 2005).
No longer limited to the purview of computer geeks, virtual worlds like Second Life have drawn the attention of large
organizations, such as the American Cancer Society and the Centers for Disease Control and Prevention (Skiba, 2007b). Educational
institutions have also recognized the benefits of educating students in a virtual world that not only resembles the real world but can
also be modified to enhance learning. At Chamberlain College of Nursing, Dr. Dee McGonigle works in Second Life with graduate
nursing students in their practicum experiences across the education, executive, nursing informatics, and healthcare policy tracks.
Because this environment lends itself to collaboration, nursing, engineering, and business students are able to participate in cooperative
interdisciplinary learning episodes.
Because virtual worlds foster unintentional learning through gamer-like technology in which students discover and create
knowledge to accomplish something, rather than experiencing traditional outcome-based learning, their experiences result in greater
comprehension and deeper knowledge. In a virtual clinical scenario, for example, a simulated, immersive environment presents
invaluable learning opportunities for the student who is assuming the role of healthcare provider. Faculty can monitor the interaction
and interrupt as necessary to provide advice or suggestions, while students negotiate the real and virtual world components of the
scenario and their avatar patients, thereby becoming aware of how, why, and when to apply specific skills within a clinical setting
(EDUCAUSE, 2006).
Because so much of the data nurses rely on are complex, and so many patient cues are lacking in concrete language or responses,
virtual worlds’ animated, immersive, three-dimensional environment allows students to practice skills, try new ideas, and learn from
their mistakes while receiving feedback from educators within a globally networked classroom environment. Some students may
struggle to participate in virtual communities for various reasons. Increasing comfort with multidisciplinary learning among students
and educators improves patient safety and encourages the refinement of best practices for effective integration of these tools into
mainstream education (EDUCAUSE, 2006).
Internet Tools: Webcasts, Searching, Instant Messaging, Chats and Online Discussions, Electronic
Mailing Lists, and Portals
The general consensus in nursing education suggests that any technology that allows users to interact and engage both materials and
one another is useful. More specifically, the Foundation of Knowledge model qualifies this observation with the caveat that technology
must display user-friendly capabilities to provide benefits to its users, thereby allowing students not just to find information and one
another online, but also to engage, challenge, and institute their discoveries. Providing nursing students with easy-to-use, free Internet
tools for reaching the first step in this process (gaining access to materials and peers) has been addressed by the proliferation of
communication technologies available to any user with an Internet connection. Beyond the gadgetry, with the development of new
strategies, practices, applications, and resources in technology comes the need for instructional strategies that not only appeal to this
newer generation of students but also enhance learning. Such strategies, when coupled with easily accessible and highly functional
tools, encourage nursing students to see beyond the right answer and to seek out information that encourages them in developing
approaches to issues and resolutions for problems (Bassendowski, 2005).
Webcasts and Webinars
Webcasts are broadcasts that are typically live presentations delivered by way of the Web. Webcasts offer great potential for helping
students and faculty to engage both information and one another globally, by tapping into students’ multiple intelligences to enable
them to access what they need. Because of the growing ease of producing streaming video and subsequently delivering it via larger
bandwidth, Webcasts have grown in popularity and are especially favored by programs that feature distance education components.
Although some institutions create their own Webcast delivery system, most users rely on a few standard providers that, in turn, present
the Webcast online. Although these presentations are often delivered live, allowing audience members to participate in the broadcast,
many instructors use Webcasts as an access point for prerecorded archives of lectures and presentations by experts whom their
students would not otherwise have the opportunity to see or hear. Studies show that students enthusiastically embrace Webcast
technology, accessing archived presentations more repeatedly than traditionally filmed sessions of guest lecturers; this dynamic level
of engagement aids students in better grasping the subject manner. Like much dynamic technology, Webcasts are an innovative
component that keeps students engaged but tends to work best when learners are provided with learning outcomes before viewing
them (Bell, 2003).
Webinars are Web-based seminars that use web conferencing software that allows educators to share their computer screen and
files and interact with their students. According to Moreau (2013), “depending on what type of webinar service you decide to use,
there are interactive sections that the audience can use to ask questions” (para. 5). Webinars are typically delivered live but can be
recorded. Moreau equates this presentation form to Skype. In our geographically dispersed, online world of learners, webinars provide
another avenue for sharing and collaborating.
There is a key difference between webcasts and webinars. Webcasts present material to the audience with limited or no
interactivity. By comparison, webinars are generally live, interactive, educational sessions. Both of these venues provide access to the
educator and, depending on the level of interactivity, sometimes to other learners.
Searching
One of the most common and proliferate search tools in technology today is the wiki. Wikis are websites or hypertext document
collections that allow users to edit and add content in an open-ended forum. The appeal of (and objection to) wikis resides in their
ability to let anyone with an interest and an Internet connection participate in a once-exclusive community of knowledge creators and
seekers. As an environment that encourages practice and learning, wikis support learning communities where students collaborate
online (Skiba, 2007a). Higher education has evolved from a place of straightforward knowledge transmission to a place where one
strives to become a member of an expert community, and wikis promise to create opportunities for individuals to participate in this
community in heretofore untapped ways.
The most objectionable aspects of wikis are their lack of organizing principle (many are organized alphabetically) and the ability
for anyone to edit entries, the latter of which creates new intellectual property right challenges. Wikipedia, for example, is the
bestknown wiki project on the Web; it is an online encyclopedia of sorts whose open access policy regarding its content keeps
educators and professionals wary of inaccurate information to be found there (Skiba, 2007a). For more information on the appropriate
use of Wikipedia, see the online tutorial: http://www.libraries.psu.edu/psul/tutorials/wikipedia.html. For additional information on
performing searches for information, see the discussion of search engines in the Research: Data Collection, Processing, and Analysis
chapter.
Instant Messaging
Instant messaging, one of many collaborative Web chat tools available to any user with a computer and Internet access, continues to
establish itself as a working, useful tool for informatics learning, providing instant access to and communication among people,
information, and technology. Although some instant message (IM) services provide voice and video messages, all instant messaging
clients (of which the three leading are Yahoo! Messenger [Y!M], AOL Instant Messenger [AIM], and MSN Messenger) provide real-
time text, allowing users to interact in the form of an on-screen conversation through a technology that is free, is already quite popular
with users, is Web based, does not require additional hardware or software, and has a very low learning curve for those few to whom it
is unfamiliar. Beyond having a real-time conversation, instant messaging (IMing) an individual allows the user to share links, pictures,
and files. This kind of easy accessibility allows students, when logged on, to collaborate; seek real-time help from professors or
librarians; and engage others working on questions, studying for clinical examinations, or reviewing information or notes (Chase,
2007).
Chats and Online Discussions (Blogs)
Real-time chats occur all over the Internet, at each hour of every day. The best-known chat tools are instant messenger clients, but
chatting also refers to real-time discussion venues in which users meet in virtual chat rooms to engage in conversations by posting
messages; this provides a comfortable, recognizable way of communicating for Net Generation students used to surfing the Web and
interacting online. In a chat, students can meet, discuss, and engage one another over any given topic. Chats take various forms, the
most complex of which involve highly evolved virtual communities in which users step into various rooms where they interact with
other individuals who are in the room at the same time. Initially the exclusive purview of gamers or hardcore programmers creating
private online communities, chat rooms now exist for a wide variety of topics and interests.
Web logs, also known as “blogs,” have emerged as low-investment and easy-to-use writing tools that, through their very setup and
appearance, enhance health professionals’ communication, writing, reading, information-gathering, and collaboration skills (Maag,
2005). Blogs are a kind of online journal, created by individuals who then invite comments from visitors to that Web space. Compared
to technically complex online projects, such as tutorials and various multimedia, blogs are immediate, free to set up and access with an
Internet connection, and easily negotiable by even the technically ambivalent. By their very nature, blogs present a built-in discussion
area to the user, so they are especially useful for study groups interested in reflecting on material and evaluating ideas in a collective,
collaborative way (Shaffer et al., 2006).
Electronic Mailing Lists
One low-investment information-gathering tool for use by nursing professionals is membership in an electronic mailing list. These
electronic discussion groups use e-mail to communicate and promote communication and collaboration with others interested in a
particular field of study (Hebda et al., 2005). Electronic mailing lists have very few requirements to participate—usually just a free
subscription and e-mail capability. Such lists are available on any subject, but most share common features, such as the need to
subscribe and then log in to participate. The moderators of an electronic mailing list have specific instructions on how to post messages
and how to set subscription controls. Posting information means that when a user replies to a topic thread, he or she generally has sent
information to every member of the list. Like other technologies, the capabilities of electronic mailing lists continue to change and
expand, providing ongoing viability for use in nursing education.
Portals
Similar to electronic mailing lists in the way they deliver specific information to one’s e-mail, a portal allows the personalization of a
specific website. Portals organize information from webpages into simple menus so that users may choose what they want to view and
how they want to view it (Hebda et al., 2005). For example, WebMD is one of the most popular and best-known portals, allowing
users to create accounts, bookmark their favorite information, and sign up for e-mail notifications. Portals, like most Web
technologies, require an Internet connection and a free subscription that allows the user to log in.
Podcasts: Audiopods and Videopods
Podcasts are audio recordings linked to the Web that are then downloaded to an MP3 player (Gordon, 2007) or computer where the
listener accesses the recording or video. An outgrowth of the Apple iPod market, podcasts are developed and delivered by way of the
Internet and require minimal investment—namely, a microphone, an Internet connection, and (often free) editing software. Although
most listeners use an MP3 player (of which iPod is but one brand name) to access podcasts, a desktop computer works just as well
(Oblinger, 2005).
Beyond possibilities for global accessibility to whatever information the user may record, podcasts allow for automatic updates in
the form of a really simple syndication (RSS) (also known as “resource description framework (RDF) site summary”) feed that
lets subscribers receive automatic notification whenever a podcast is updated (Gordon, 2007). Refer to Box 20-4 for more information.
BOX 20-4 PODCASTS
Jackie Ritzko
A basic Web search using the search term free nursing podcast produced over 1 million hits on one search engine. But what does this mean in the
context of nursing informatics? The implication is that there are many resources on the Internet that somehow involve podcasts with a nursing focus.
How these sites might be of use to a professional in the nursing field is the focus of this feature. Before any discussion of the educational uses of a
technology tool can take place, however, there needs to be an understanding of the hardware, software, training, and support that are required to use
the tool.
Podcast is a term coined from the words iPod and broadcast. iPod is the name given to a family of portable MP3 players from Apple Computer.
MP3 is a common file format for electronic audio files. Audio files—in particular, MP3 files—can contain verbal speech, music, or a combination of
both. MP3 files can be played or listened to using an MP3 player. MP3 players can be portable devices, such as the iPod, or an MP3 player can
simply be software that is installed and used on a computer. Thus a podcast is simply an MP3 file that can be played on an MP3 player. Broadcast, in
its simplest usage, refers to the ability to send out. In terms of podcasts, broadcasting is the ability to share MP3 files in such a way that the files are
delivered to the user whenever new versions are available through a subscription. This ability to share resources and access the most up-to-date
resources is a great advantage, especially for the educational community.
We will now discuss podcasts in terms of function, ranging from the more basic to the more advanced functions: finding podcasts, listening to
podcasts, creating podcasts, and sharing podcasts. Finding podcasts at a minimal level requires only an Internet connection and a web browser. As
noted earlier, a basic Web search for the term nursing podcast found many sites. Performing a basic Web search, however, may provide a user with
only limited search capabilities. An MP3 aggregator is a program that can facilitate the process of finding, subscribing to, and downloading
podcasts. One popular aggregator is Apple Computer’s iTunes, which is a free program available as a download from apple.com. Although iTunes is
widely used, it is not the only program of this type. A program such as iTunes gives the user the ability to search for podcasts based on many criteria,
including category, author, or title. The iTunes program provides access to audio downloads that may be either songs or podcasts. In both cases,
users may find downloads that are free and those that require payment.
Because podcasts are MP3 audio files, an MP3 player is needed to listen to a podcast. As noted, this can be done on a computer with an MP3
player or on a portable MP3 player. Podcasts can be downloaded in two ways: manually or (2) by subscribing to a podcast. In the case of a
subscription, once a new track is added to the podcast, iTunes automatically delivers it to a computer. Continuing to use iTunes as an example, once
a podcast is found, it can also be manually downloaded from iTunes to a computer. Once on the computer, it can be listened to or transferred to a
portable device.
Users may also choose to produce or record podcasts. As with most technology solutions, hardware and software requirements typically must be
met to create podcasts. The hardware for recording a podcast can vary. In a stationary setup, a microphone can be connected to a desktop or laptop
computer. Stand-alone audio recorders can also record podcasts, and some MP3 players contain built-in recorders. Free recording software is
available for most computer platforms.
Sometimes a podcast is created for the sole use of the creator. More often, however, a podcast is created with the intention of it being shared with
and listened to by others. Podcasts can be stored on web servers for distribution and can also be shared via tools, such as iTunes. Within iTunes, for
example, educational institutions are able to host podcasts in the area known as iTunesU.
Podcasts have many uses in education in general and in nursing education in particular. Informal learning can take place when a nursing student
listens to nursing podcasts. Listening to or creating podcasts may be a formal class assignment, providing new ways to interact with course material.
Short discussions of what is new in the field may appear as podcasts on the Internet, in particular on news and research sites. Learners may rely
on the portability of MP3 players to take learning with them on the road. Commuters and walkers and joggers are often seen listening to MP3
players. Because creating podcasts is relatively easy and inexpensive, such presentations can be produced by students as review files for common
terms or used as ways for students to self-assess their ability to discuss topics. The uses of podcasts from an educational perspective are limitless.
Bringing the discussion of podcasts back to the Foundation of Knowledge model, for each task or process in the model, one can see how podcasts
fit with that concept. Podcasts can be used to acquire new knowledge from sources on the Web. Listening to podcasts provides learners with another
tool for learning in addition to readings and lectures, thereby addressing a wider audience whose members have varying learning styles. Because
podcast creation is simple and inexpensive, podcasts are an ideal way to generate and disseminate knowledge.
Audiopods
Audiopod is a term used to describe a traditional or audio-based podcast. Participating in podcasting can exercise not just basic
technology skills, but also writing, editing, and speaking skills. Writing scripts for a podcast can be an excellent exercise in critical
thinking and information delivery, whereas the technology itself allows global access to information by faculty, teachers, and students
anywhere at any time (Gordon, 2007). Both faculty and students can create audiopods with little difficulty, and most use podcasts to
share additional class materials, updates, and even entire lectures (Oblinger, 2005).
Videopods
Similar to an audiopod in setup and accessibility, a videopod is a podcast that provides video in addition to audio functionality.
Faculty might use videopodcasts to demonstrate concepts, interview experts in the field, and even assess student progress (Gordon,
2007). Libraries and other institutions have even begun using the videopod as a learning alternative to the ubiquitous and often mocked
information video, finding that highly mobile students are more readily embracing this technology (Oblinger, 2005).
Multimedia
As technologically savvy students continue to demand accessible, interactive learning tools to keep them engaged, an increasing
number of instructors are experimenting with and incorporating multimedia into their courses. Generally, multimedia refers to a
computer-based technology that incorporates traditional forms of communication to create a seamless and interactive learning
environment, such as interactive tutorials, streaming video, and problem-solving programs. Nursing education has long relied on
traditional multimedia, such as slide presentations, overhead projections, and training videos, for continuing education (CE) of staff,
classroom learning, and patient education (Edwards & Drury, 2000). Now, however, new advances in multimedia allow faculty to add
such innovations as simulations and virtual reality to their healthcare training, providing a way for students to learn procedural skills,
such as insertion of needles and physical assessment, without any risks to an actual patient.
Research suggests that the seeing, hearing, doing, and interacting afforded by multimedia facilitate learning retention, with
multimedia being at least as effective as traditional instruction, but offering the benefit of greater learner satisfaction. Authoring
software—that is, programs that allow users of varied technical skill to design and create webpages and movies—has greatly
facilitated the use of multimedia by faculty (Hebda et al., 2005). Nevertheless, the most effective multimedia relies on the careful and
pedagogically appropriate combination of textual material, graphics, video, animation, and sound (Edwards & Drury, 2000)—a
distinctly separate skill set from teaching and instructing. Some schools of nursing have instructional designers on staff to assist
nursing faculty with the development of multimedia to support learning.
Beyond providing a flexible method of delivery for instructional information, multimedia promises to motivate students by
requiring them to analyze evidence in ways that require higher-order thinking and problem-solving skills. Similarly, faculty can begin
to think about their classes in new ways and accommodate different student learning styles (Oblinger, 2005). Box 20-5 provides more
information on one type of multimedia device, and Box 20-6 looks at the capabilities of smartphones.
Promoting Active and Collaborative Learning
Because of the shift within the teaching–learning context from the individual seeking answers to the group trying to construct new
knowledge from available information, the most effective learning solutions require new digital communication skills, new
pedagogies, and new practices (Costa, 2007). A collaborative, student-centered approach uses the best tenets of inductive teaching by
imposing more responsibility on students for their own learning than is assumed in the traditional lecture-based deductive approach.
These constructivist methods are built on the widely accepted principle that students are constantly constructing their own realities
rather than simply absorbing versions presented by their teachers. Collaborative methods often involve students’ discussion of
questions and in-class problem solving, with much of the work (in and out of class) done by students in groups rather than individually
(Felder & Prince, 2007).
Johnson and Johnson (1990) have identified five significant elements for successful collaborative learning that are still pertinent
today:
1. Face-to-face interaction between students, allowing them to build on one another’s strengths
BOX 20-5 PERSONAL DIGITAL ASSISTANT
Dee McGonigle and Kathleen Mastrian
The personal digital assistant (PDA) is a handheld or palmtop computer. PDAs have grown in popularity because of their small size and great
power: They are usually designed to fit in a pocket, making them easily portable, yet offer impressive performance capabilities that allow one to
store, access, and organize information, such as calendar entries, documents, spreadsheets, databases, notes, and to-do lists. The capabilities and
access are limited by the PDA’s processing speed and memory; in turn, the faster and more robust the memory capabilities, the higher the cost of the
unit.
When considering the purchase of a PDA, it is important that the prospective buyer reflect on his or her current and future needs. The user will
want to be able to expand capabilities as needed without having to replace the PDA. Therefore, before selecting a device, it is important to review all
of the PDAs currently available. Interactive comparisons of various PDAs are available (CNET, 2008b), as are reviews of various models (CNET,
2008a). Softpedia (2007a) provides the latest news on handhelds and is typically updated daily, with an opportunity to download a user manual and
view PDA reviews (Softpedia, 2007b).
Most of today’s PDAs use either the Linux, Pocket PC (Windows), or PalmOS (Palm) operating system. Because they can be used in networked
environments including wireless configurations, PDAs can theoretically keep the clinician in constant contact with patient information, colleagues,
and other necessary resources.
The applications available provide a wide range of functionality, ranging from simple to highly complex software tools. With a PDA, nurses can
use dosage calculators, drug and specialty databases, educational applications, or clinical forecasting tools; take dictation; or practice telenursing.
Because the PDA is compact and a stylus or scroll bars are used to manage information (i.e., input, access, and output functions), many add-ons are
available to enhance functionality and ease of use.
HOW TO USE A PDA
The PDA typically contains the following equipment: a display or touch screen, infrared (IR) port, USB connector, secure digital memory slot, clock
icon, menu icon, sync icon, find icon, web browser button, navigator button or pad, calendar icon, home icon, contacts icon, scroll button or bar,
headphone jack, internal Bluetooth, internal Wi-Fi, speaker, multiconnector, reset button, stylus, microphone, or cell phone capabilities. The
configuration of the PDA affects its performance and use.
The navigation buttons and scroll functions allow the user to see, access, and open applications, files, and documents. Depending on the PDA
selected, how the user inputs data and commands varies. Some devices offer handwriting and voice recognition capabilities, but generally a mixture
of a plastic stylus, touch screen, and handwriting recognition software is used. The handwriting software, which recognizes the characters the user
writes and converts them to letters and numbers, is Graffiti on Palms and Block Recognizer for Pocket PCs. The Transcriber software used on the
Pocket PC recognizes legible normal handwriting, printing, or a combination of writing and printing. Thus users do not have to remember the
character codes for letters and numbers. Users can also input data using the soft keyboard, a small keyboard on the screen, or such add-ons as a small
or full-size peripheral keyboard using a Bluetooth or USB connection.
Most current PDAs come with an expansion slot enabling the owner to increase storage space or memory. Secure digital is a common type of
card that provides additional memory relatively inexpensively. For those considering purchase of a PDA, an expansion slot is a must-have item in
today’s environment.
PDAs usually come equipped with office applications, sync software, Bluetooth, cables, and an IR port. The office applications could include
word processing, spreadsheets, database, or presentation software. The sync (synchronize) software functions to sync or match and update
information on both a computer and a PDA. Bluetooth is a form of wireless connectivity that is commonly used in cell phones. Although it usually
does not provide Internet access, it does facilitate file transfer between a PDA and a computer. One can purchase an adaptor if one has a PDA
without Bluetooth. Cables are an inexpensive way to connect to the PDA to a computer, essentially plugging the PDA into a computer. The downside
of using cables is that the user must be near the computer to connect. The IR beams of light are used as a unidirectional means to transfer select data
or entire programs wirelessly to other PDAs. The data and information are not exchanged or traded, but rather are transferred in only one direction.
To obtain something in hard copy, the user can beam a document to an IR-enabled printer. Most commonly, people beam their business cards. For
example, if the user attended a career fair or conference and visited exhibitor booths, he or she could beam a business card into a vendor’s PDA. To
beam across platforms, additional software is required.
As PDAs continue to evolve, so do their capabilities and connectivity. The Internet connectivity (web browser and e-mail) provides the clinician
with communication capabilities and constant access to real-time online data and information. Because the PDA is portable, connectivity must be
ready whenever and wherever it is needed. One way to establish connectivity is through the use of Wi-Fi compatibility, which provides for use at hot
spots throughout the country, such as cafes, coffee shops, hotels, restaurants, and universities. Wi-Fi connectivity can also use the existing wireless
network in an agency or office. This feature can be added to a PDA by purchasing a Wi-Fi adaptor.
Some cell phones, called smartphones, have limited PDA capabilities, whereas some PDAs are telephone enabled. Smartphones have limited PC
functionality; they have an operating system and facilitate the use of e-mail and other applications. Based on the user’s practice setting, the addition
of the phone features could be an important consideration.
APPLICATIONS
Using a PDA in nursing practice offers tremendous advantages. These devices can be used to track patients, as point-of-care devices, or as
calculators. The PDA could take dictation at the bedside or on the go as the user travels between patients or appointments. As reference tools, PDAs
can provide ready access to clinical or drug databases; electronic textbooks and reference materials; online journals in real time, such as MEDLINE
and the Online Journal of Nursing Informatics; and educational tools, such as study guides and care planning documents as those found in Dykes
Library (2008) and PDA Cortex (n.d.). Users can transfer information within a network even when they are in the field. Using the network, they can
send a note to a case manager, update a physician on the status of patients they visited in their homes, or even send prescriptions to the pharmacy.
PDAs also allow users to maintain their schedules or calendars and receive reminder alarms. One can even use a PDA for professional development,
such as continuing education offerings or furthering academic education online. Whether one is an avid PDA user, is beginning to use a PDA, or is
just contemplating purchasing one of these devices, it is best to get involved and participate in the electronic mailing list that discusses PDA use in
nursing (http://www.rnpalm.com/nursing_pdas_listserv.htm).
PDAs can also be used to enhance health care for patients. Nurses can monitor patients and send surveys and questionnaires; patients can submit
responses to their healthcare provider or to the healthcare institution. PDAs can enhance patient access to clinicians, especially for patients who are
mobile; the PDAs can go where they go, and patients can keep in touch with their healthcare providers via e-mail, telephone, fax, instant messaging,
or text messaging. Using these devices, patients can easily maintain their appointment and medication schedules and receive patient education
materials and access clinician-recommended websites and electronic mailing lists.
http://www.rnpalm.com/nursing_pdas_listserv.htm
The educational arena has embraced this technology. Nursing instructors are communicating and sharing information with their learners and
clinical settings using PDA technologies. By using wireless connectivity in class, instructors can receive instant feedback as to the understanding of
their learners. PDAs can even assist with discussions and provide study aids, interactive exercises, and quizzes. For nursing students, the reference
materials and podcasts available for coursework can be stored on a PDA for easy access. Students can upload and download clinical documents and
information to communicate with an instructor and clinical setting staff. In the clinical setting, the easy access to robust reference tools and materials,
such as dosage calculators, helps to reduce errors. The educational applications continue to expand as more software and capabilities become
available.
This is certainly not an exhaustive list of PDA applications or equipment, and the truth of this information era is that the present will be the past
by the time this text is published. PDAs continue to evolve and become smaller and more robust (About.com, n.d.; Seko, 2005; Softpedia, 2007a,
2007b). As the future unfolds, so too will applications of PDAs in nursing.
REFERENCES
About.com. (n.d.). Palmtops/PDAs: Fossil wrist PDA FX2008 with Palm OS review.
http://palmtops.about.com/od/palmhardware/fr/FossilWristPDA.htm
CNET. (2008a). CNET reviews: What to look for in handhelds. http://reviews.cnet.com/4520-3127_7-5021319-1.html?
tag=wtlf%5C%22%3Ehttp://computers.cnet.com/hardware/0-1087-8-20549052-1.html?tag=wtlf
CNET. (2008b). PDA—CNET reviews. PDAs matching “PDA.” http://reviews.cnet.com/4244-5_7-0.html?query=PDA&tag=srch&target
Dykes Library. (2008). Popular freeware for PDAs. Retrieved November 2007 from http://library.kumc.edu/resources/PDAFreebies.htm PDA
Cortex. (n.d.). The Journal of Mobile Informatics and PDA resources for healthcare professionals. http://www.rnpalm.com/
Seko, S. (2005). IR watch Ver 0.3: The version only for WristPDA. http://www.pamupamu.com/soft/irmoniw/irw.htm
Softpedia. (2007a). Handhelds area and latest handheld devices (RSS). http://handheld.softpedia.com/#devices
Softpedia. (2007b). Handhelds area and PDA manual 1.0 download (for the Pocket PC). http://handheld.softpedia.com/get/Documents-E-Books/Pda-
Manual-36435.shtml
2. Mutual learning goals that, in turn, prompt students to exhibit positive interdependence rather than individualized competition
3. Equal participation in the work process and personal accountability for the work one contributes
4. Regular debriefing sessions as a group after meetings or presentations during which time feedback is shared and observations
analyzed
5. Use of cooperative group process skills learned in the classroom
Although collaborative learning relies heavily on student investment and participation, institutions must ultimately create the best
physical and electronic settings where collaboration is encouraged. This can be achieved with a sound educational and technologic
infrastructure, reliance on proved working models, adaptable physical spaces, and even pedagogic support in the form of preceptors or
mentors.
Especially useful for nursing students is the collaborative fieldwork model in which two or more students share a clinical setting
and the same fieldwork educator. In this model, learning happens in a reciprocal fashion, with students constructing knowledge by
watching each other and exchanging ideas. The most effective fieldwork experiences are highly structured with clear outlines of
responsibilities, duties, and expectations, ensuring that the experience matches the learner’s expectations. All activities performed by
students, such as conducting evaluations, are done jointly, so that peers provide each other with objective feedback, leading to eventual
increased self-confidence (Costa, 2007). In this way, suggests Costa, individuals with different viewpoints and experiences create a
space where new knowledge emerges and existing knowledge can be restructured (as cited in Cockrell, Caplow, & Donaldson, 2000).
BOX 20-6 SMARTPHONES AND OTHER SMART DEVICES IN NURSING EDUCATION
Dee McGonigle and Kathleen Mastrian
Smartphones are another tool for the educational arena. As Yu (2012) stated, “Smart phone technology, with its pervasive acceptance and powerful
functionality, is inevitably changing peoples’ behaviors” (para. 6).
Our educational uses of a technology such as smartphones cannot only affect the learning episode but also influence how we prepare students to
embrace and use technologies appropriately. As educators, we want our students to remain competitive in a highly technologically dependent world.
Nursing is a data- and informationdriven profession, and nurses must rely on technologies to provide the data and information necessary to provide
quality care to our patients and evolve the nursing profession as whole.
If our students have smartphones, we can share text, graphics such as PowerPoint presentations, podcasts, and other audio/video media with them
prior to the learning episode. When all students have access to the same information, it enhances the dialogue and topical discussion centered on that
information. Nursing educators can use smartphones to distribute announcements, reminders, and even pertinent notes that the students need.
Students can also be polled using these devices. Smartphones can even replace huge textbooks with electronic files. Smart devices and their
calendars and messaging features help both educators and students organize our lives and keep our hectic schedules straight. The use of smart
technologies can facilitate interactions around the world. Students can consult with other students and experts from anywhere on the globe. As this
brief list of applications suggests, we have only just begun to think of ways in which to incorporate smartphones into our learning episodes.
In conclusion, we would like to leave you thinking about a money-saving alternative for many schools. Instead of requiring a laptop computer,
what if your school required every student to have a smartphone? Could we replace the expensive computer labs on our campuses while better
connecting our online and blended students to their educational milieu?
REFERENCE
Yu, F. (2012). Mobile/smart phone use in higher education. http://www.swdsi.org/swdsi2012/proceedings_2012/papers/Papers/PA144
Libraries have also begun to recognize their role in students’ success with and predisposition toward collaborative learning by
creating redesigned spaces that reflect students’ need to huddle in small groups, sit closely together without barriers, chat about their
work, and view digital information without physical hindrances, such as carrels or work stalls. A leader in this movement has been
Indiana State University, whose new information commons features completely overhauled furniture, software, monitors, processing
power, and wireless access to the university’s network. Students can now collect as a group at kidney-shaped tables; better see the
information loaded on the flat-screen monitors; make use of brainstorming, design, and planning software; and discuss their work in a
chat-friendly zone. Some faculty members have even scheduled classes at the learning stations, and students, including those in
nursing, have responded enthusiastically to the evolved space (Gabbard, Kaiser, & Kaunelis, 2007).
In addition to adaptable physical spaces that encourage discussion and group work, students require a supportive infrastructure that
provides essential elements necessary to successful research and scholarship. These include professional development support in the
form of workshops that help students acquire or refresh skill sets; presentation opportunities; and hardware, software, and resource
support. One such example involves the participation by nursing students at the University of Texas Medical Branch School of
Nursing in the Scholarly Talk About Research Series (STARS), in which students and faculty give presentations of their work before
presenting those materials at professional conferences (Froman, Hall, Shah, Bernstein, & Galloway, 2003), thereby eliciting collegial
feedback, collaborative troubleshooting, and shared research ideas.
Simply adopting a collaborative, inductive method of learning, however, will not necessarily lead to better learning and more
satisfied students. As with any form of instruction, collaborative teaching methods need skilled and careful implementation. Because
students are often resistant to instruction that makes them more responsible for their own learning, those who attempt to implement an
inductive learning method should adhere to best practices, such as providing adequate scaffolding—that is, extensive support and
guidance when students are first introduced to the method and gradual withdrawal of that support as students gain more experience and
confidence in its use (Felder & Prince, 2007).
Nursing preceptors and mentors, for example, can provide this kind of scaffolded support as clinically active role models
(Armitage & Burnard, 1991) and problem-solving advocates and collaborators (Gagen & Bowie, 2005). As individuals who are
primarily concerned with the teaching and learning aspects of the relationship, preceptors help students learn by acting as clinical
practitioner role models from whom the students can copy appropriate skills and behaviors. Kramer (1974) introduced the concept of
nurse preceptor to address the theory and practice gap—that is, the difference between what is taught in class and what actually
happens in nursing practice. Preceptors enhance clinical competence through direct role modeling, which is especially valuable in a
field where competence and clinical ability are paramount (Armitage & Burnard, 1991). Mentors, similar to preceptors, provide
equally valuable assistance to nursing students in the form of a facilitator. Mentors are most often used in nursing and education to
support new professionals who are trying to fulfill the rigors of a new position while negotiating the stress inherent to a new
environment (Gagen & Bowie, 2005). According to Jokelainen, Turunen, Tossavainen, Jamookeeah, and Coco (2011), “student
mentoring in nursing clinical placements integrates environmental, collegial, pedagogical and clinical attributes” (para. 6). Mentors
tend to address student needs through open conversation, student advocacy, feedback on student progress, facilitation, teaching, and
general support (Neary, 2000).
Generally, these and other forms of institutional support promote students’ adoption of a meaning-oriented approach to learning, as
opposed to a surface or memorization-intensive approach. Collaborative, inductive learning promotes intellectual development that
challenges the dualistic thinking that characterizes many entering college students, which holds that all knowledge is certain,
professors have it, and the task of students is to absorb and repeat it (Felder & Prince, 2007, p. 55). Further, this kind of learning helps
students acquire the self-directed learning and critical thinking skills that characterize the best scientists and engineers (Felder &
Prince, 2007). The active, engaging elements of collaborative learning increase self-confidence, promote autonomy in students, and
foster a commitment to lifelong learning (Costa, 2007)—all necessities for the success of a new millennial information-literate student.
Knowledge Assessment Methods
An integral and critical component of educational programming is the evaluation of different learning methods. An evaluation process
relies on actions taken to measure outcomes, such as the attainment of learning. Process or formative evaluation takes place over the
life span of a program and is ongoing, whereas summative evaluation is enacted at the end of a program or learning activity.
Both administrators and nursing educators across various programs are responsible for evaluating their programs; beyond gathering
feedback from participants and learners, an important but ultimately one-dimensional evaluative tool and effective program evaluation
should be considered in relation to successes, failures, and means by which program revision might reinstate a program’s effectiveness
(Menix, 2007). Dickerson (2005) suggests that evaluation constitutes a substantial part of the work of the nurse educator as defined in
the Scope and Standards of Practice for Nursing Professional Development (Dickerson, as cited in American Nurses Association,
2000).
Papers and Projects
When one is looking for feedback and assessment of a particular learning activity, data are best collected during and at the completion
of the activity. During the activity, different factors assist in determining how well a teaching strategy is working; these factors include
participant engagement (Are students listening? Is there note taking? Does anyone have questions or comments?), ease of use of
equipment and resources, and time required to complete an activity. Much in the way nursing practitioners modify care plans in the
midst of an unsuccessful treatment, so should educators change course when it seems the learning experience is not progressing as
planned (Dickerson, 2005).
Evaluation by participants at the end of a project is also valuable, because learners can provide information related to the
attainment of objectives and the teaching effectiveness of both the faculty and the learning materials while the experience is still fresh
and memorable. Learner input, although sometimes considered biased and unreliable (e.g., student evaluations refuted by many faculty
as useless), actually represents useful feedback on the success of a project or assignment (Dickerson, 2005). Faculty at the University
of Colorado at Boulder address this issue by choosing samples of student work for evaluation by faculty or external reviewers to see
how well students are meeting program skills and goals. Because these papers and projects are graded by an instructor as part of the
course, this form of embedded assignment ensures that the students are still expending effort without having to do extra assessment-
specific assignments (University of Colorado at Boulder, Office of Planning, Budget and Analysis, 2001).
When a paper assignment or project does fail, the outcome provides a valuable learning opportunity for all educators to look
closely at the pedagogy, structure, goals, outcomes, and expectations of success of the proposed activity and to determine what needs
revising or reevaluating to achieve what the educator would term “successful” completion. Although many educators and researchers
evaluate programs for effectiveness and to maintain certain standards (Dickerson, 2005), most innovative is the educator who
evaluates and observes superficial failures as learning opportunities to improve pedagogy and delivery.
Discussions
Although traditional in-class discussion seems almost outdated in light of the stunning advances in technology that allow learners to
participate in innovative activities ranging from computer-guided online quizzes to interactive simulations, the educator as facilitator
still holds value and importance in a traditional or even blended classroom. In one study, students exposed to competency-based,
written self-learning modules and students exposed to competency-based, didactic lecture modules performed equally. A sample was
selected from a group of registered nurses (RNs) who attended a mandatory yearly review of standards from the Occupational Safety
and Health Administration (OSHA) and The Joint Commission (an accrediting organization for healthcare organizations). The 67
subjects were given pretests, were exposed to the same content material through two different kinds of presentations, and were then
posttested. Results indicated no significant differences among scores of either group. This suggests that advantages of one teaching
method versus another ultimately lie in the educator (Schlomer, Anderson, & Shaw, 1997). Self-direction, independence, and a
willingness to collaborate all characterize the Millennial Generation learner, but without guidance, pedagogic grounding, and even a
basic contextualized introduction to concepts and materials, even self-motivated, highly information-literate learners will flounder
without assistance.
Testing
With the legal and financial implications of employee and student performance evolving into a major concern for all providers and
healthcare organizations, multiple requirements for competence in nursing practices within the healthcare system have been
established by national agencies and associations, such as those suggested by the American Nurses Association. Of specific concern to
educators and administrators is the gap exhibited by test takers who, although scoring exceptionally well on examinations, are unable
to perform a clinical procedure or recognize a warning sign in a patient experiencing difficulty. Use of traditional testing as an
evaluative tool has been in existence for decades, although research shows that criterion-based performance better measures skill levels
while also identifying deficiencies (Redman, Lenburg, & Walker, 1999).
As a result, some schools, such as the University of Colorado’s school of nursing, have redesigned their curricula and the
competency-based curricular outcomes for their educational programs. These substantial programmatic changes have resulted in a
unique approach to nursing at both the undergraduate and graduate levels, with a new focus on such competency-based outcomes as
effective reflective practice (Redman et al., 1999, p. 3), generation of nursing knowledge, and leadership and social change for
improved health of individuals, communities, populations, and global environments (p. 3).
Additionally, faculty at the University of Colorado at Boulder use embedded testing to assess outcomes, with goals being built into
examinations or other assignments. Student performance regarding these items is part of an outcomes assessment, and it is graded
accordingly by an instructor as part of the course (University of Colorado at Boulder, Office of Planning, Budget and Analysis, 2001).
Such changes promise to deliver substantial changes in the way nursing is taught, perceived, and executed.
Case Scenarios
Professional organizations are increasingly recommending performance-based assessments of students in professional degree
programs, and enacting case scenarios provides an opportunity for students to practice procedural responses and improve patient
safety. Case scenarios, a form of problem-based learning, are now available through simulation software and virtual reality
programming as well. This kind of testing, in which students must respond within context to a perceived situation rather than a
theoretical or fact-based question, allows educators to gauge procedural knowledge; it allows them to determine well how a student
executes a skill or applies concepts and principles to specific situations (Garavalia, 2002). For example, in a clinical context, a student
could explain how to change a dressing, insert an intravenous line, or intubate an individual, but such knowledge is declarative rather
than procedural and, therefore, for some evaluators, not as valuable. Conditional knowledge is often also reflected in procedural
knowledge, demonstrating a student’s ability to know when and why action is or is not taken, and how.
The best assessment strategies are identified through consideration of the cognitive level of the objective, the nature of the task,
and the best context for the assessment. Performance assessments can be used throughout the curriculum, not just as the final
component of a practical or clinical experience. The most important consideration is ensuring that the assessment tool and task match
the instruction and the stated learning objective (Garavalia, 2002).
Other: Portfolios
Viewed in the 1980s as realistic evaluative tools of student accomplishment and learning, portfolios in nursing education are growing
in popularity as useful tools for documenting students’ exposure to educational experiences. A nursing portfolio allows a student to
document a variety of sometimes unquantifiable skills, such as creativity, communication, and critical thinking. Further, portfolios can
reflect achievement of goals, self-evaluation, and professional development, also providing a way for returning students to log and
document past work or life experiences in a creative but structured way without taking a standardized test. The usefulness of a
portfolio for an undergraduate in a nursing program depends on a structured system of organization: an identification page with
resume, a table of contents, separate and clearly marked sections, and so forth. In this way, portfolios can monitor program outcomes,
positively influence employment and graduate school admission, and provide a clear snapshot of a student’s strengths and abilities
(Alexander, Baldwin, & McDaniel, 2002). See the Administrative Information Systems chapter for a comprehensive discussion of
professional collaboration tools.
Knowledge Dissemination and Sharing
Sharing stories and experience from a clinical point of view accomplishes much more than simply promoting camaraderie or empathy
(although this kind of engagement is infinitely valuable in its own way); sharing experiences of clinical learning can help convey life-
saving information to other clinicians in a way that is more memorable and palatable and less imposing than warnings delivered
outside a social context. Clinical and caring knowledge, often rooted in everyday exchanges, become socially embedded such that
those with experience in particular clinical settings share common knowledge and understanding. The social embeddedness of caring
and clinical knowledge is a result of shared and shaped collective understanding of practice and sometimes provides an alternative
view.
The power of pooled knowledge in combination with knowledge produced in dialogue with others helps to limit tunnel vision and
is a powerful strategy for maximizing the clinical knowledge of a group. Whether the nurse is networking, presenting, or seeking CE
or recertification, an understanding of socially embedded knowledge coupled with the multiple perspectives of skilled practitioners
allows for a rich and vibrant opportunity to apply nursing skill effectively (Benner, Tanner, & Chelsa, 1997).
Networking
Considered crucial to career development because of opportunities for collaboration and information exchange, networking encourages
professional support by making successful professionals accessible to their colleagues. Further, developing interactive professional
networks between academic and clinical nurses can benefit practice in diabetes, stroke, and mental health care, and in community
nursing—a field where practitioners are encouraged to collaborate (Gillibrand, Burton, & Watkins, 2002).
The value of networking to members of male-dominated professions, such as law, business, and medicine, resides in opportunities
to make contact with fellow professionals and, in turn, further one’s career. This observation is especially poignant for nursing, a
predominantly female profession that, until recently, has rarely reaped the benefits of formal networking (Nicholl & Tracey, 2007).
Because nurses tend to gather their information from personal networks, such as colleagues or professional meetings, the increased
availability of technology to assist in networking has greatly facilitated information exchange. Blogs, e-mail, websites, electronic
mailing lists, and other communicative technologies have opened up an endless stream of collaboration and networking possibilities,
allowing nurses to access and learn from colleagues’ experiences. Using the Internet allows for the discovery of information heretofore
unavailable through traditional information sources (Pravikoff & Levy, 2006), helping nursing professionals decide whether, for
example, pursuing research opportunities or collaboration on specific professional projects seems viable.
Formal networks, such as the International Nurse Practitioner/Advanced Practice Nursing Network (INP/APNN), unveiled in 2000,
promote the exchange of knowledge, resources, and expertise in an effort to enhance the presence of nursing in primary health care.
Created in response to the globalization of nurse practitioner and advanced practice nursing network (APNN) roles, the network
enables the enhancement and advancement of practice both for countries just beginning to initiate advanced practice nursing (APN)
roles and for those with experienced practitioners (Affara, Cross, & Schober, 2001).
Membership and participation in professional associations also provide ways to network and advance one’s profession.
Professional associations represent venues through which members may set standards for professional practice, establish codes of
ethics, become involved in advocacy, engage in CE opportunities, access job banks, subscribe to professional journals, and act as a
common voice for the profession. For example, the American Nursing Association of Occupational Health Nurses is instrumental in
maintaining healthcare issues on the political agenda. Research shows that nurses sometimes hesitate to join professional organizations
because of barriers associated with cost, distance to meetings, lack of activities in their geographic area, and inability to attend
meetings. Because networking creates fertile areas for the development of new ideas, partnership, jobs, and strategies, both national
and state associations would benefit from creating greater opportunities for healthcare practitioners to earn CE credit and network with
others in their field (Thackeray, Neiger, & Roe, 2005).
Presenting and Publishing
Much in the way the American Association of Colleges of Nursing (AACN) maintains standards for nursing education, professional
journals also hold their contributors to similarly rigorous standards and provide a valuable venue in which nursing professionals might
share ideology and innovations in the field. With the proliferation of online journals and the availability of nursing information via
multiple media, publishing remains an excellent way to participate in the dissemination of professional information. Both nursing
magazines and journals reach considerable audiences; journal distinctions lie in their authorship and audience. Although journal
articles are written by and for scholars, with refereed or peer-reviewed journals requiring a blind review by a group of reviewers to
eliminate bias, magazine articles may be written by a professional in the field, an editor, freelancer, or other author. Publishing
provides excellent opportunities to extend knowledge and share research.
Similar to publishing, making presentations at contemporary professional conferences allows nursing educators and students to
gain experience and share scholarship with colleagues. Presentations must meet certain standards for an audience to find them credible
and effective. Because an audience retains 50% of what they see and hear in a presentation versus 20% if they only hear it, experts
suggest the use of audiovisual aids to create the most effective professional presentations (Bergren, 2000). A noteworthy presentation
could involve multiple levels of complexity, from a simple PowerPoint slide to an animated tutorial. Because technology and well-
designed visuals cannot make up for lack of preparedness or research, however, presenters should be aware of their target audience and
details of the research being presented. Regardless of the medium or presentation style, audiovisual presentations should be designed
consistently and simply, using colors and fonts that are easy to read and understand and audience-appropriate language.
Conferences often host poster presentations that enable contributors to share research findings, innovations, and exemplar
programs in a low-investment but visually captivating way. Because posters are primarily visual, with little or no verbal
supplementation, most important for consideration are room elements, such as size, space limitations, and lighting. The best nursing
practitioner posters feature consistent visual components, such as appropriately sized, readable font and simple colors, and are based
on a research concept or clinical objective (Berg, 2005). A high-tech alternative to a paper poster is an electronic poster—that is, a
continuously running PowerPoint presentation either projected for larger audiences or left to run from a laptop or desktop for smaller
audiences (Bergren, 2000). Both publishing and presenting provide opportunities for the nursing practitioner to disseminate new
knowledge and stay abreast of information in the field.
Continuing Education and Recertification
Nationally, nursing employers and institutions have, because of budgetary constraints, begun to eliminate CE programs traditionally
reliant on classes, conferences, and workshops; consequently, reliance on outside agencies and technology has increased to meet this
need. The traditional approach to obtaining CE credits has included home study offered by professional journals and organizations in
which the client reads articles, answers related questions, and sends in the test form and fee. Although fairly straightforward, this
technique provides little in the way of peer interaction (Hebda et al., 2005).
With the ubiquitous technology influx and the accessibility it affords, obtaining CE credits through e-learning is considered a
beneficial delivery method for mandatory educational programs and other programs that provide employees with opportunities to
maintain or improve skills. Benefits of e-learning for CE training include the ability to access information at any time (thus creating a
flexible schedule) and experience instant feedback and individualized instruction by seeking out specific, additional information as
needed.
E-learning can also benefit administrative support of CE credits by providing instantly accessible computerized records and other
tracking features, such as records of success and completion, associated costs of program development, and staff productivity.
Allowing nursing professionals to complete mandatory training on demand represents a huge benefit of e-learning, with the best
programs allowing for customization to accommodate program revisions and regulation changes (Hebda et al., 2005).
In some cases, acquiring CE credits may also help the nurse achieve recertification. Available through myriad professional
organizations, recertification ensures that nurses are staying current in their fields and some specialties; for example, the field of
pediatric nursing requires annual recertification to maintain professional status. During recertification, the Pediatric Nursing and
Certification Board offers each certified pediatric nurse (CPN) options for ensuring she or he is maintaining national standards within
the specialty of pediatric nursing (Pediatric Nursing Certification Board, 2007).
As an added benefit, some hospitals provide higher salaries to nurses who maintain certification. Additionally, 90% of nurse
managers indicate they would prefer to hire a certified nurse over a noncertified nurse. The trend in Magnet hospitals to encourage,
reward, and promote certified nurses is spreading to other facilities and healthcare settings, with retirement centers and home health
agencies now beginning to seek certified nurses because of the perceived extra benefit to their customers and the marketing advantage
obtained from hiring nurses with guaranteed levels of competence (Peterson, 2007).
One such program that facilitates educational programs for medical professionals around the United States and helps nurses reach
recertification goals is the innovative Wake Area Health Education Center’s RN refresher program, which is designed to return RNs to
practice. Nurses participate in a series of medical and surgical didactic models, engage in clinical practicums, and work one-on-one
with an RN preceptor who provides instruction and evaluation of skills over the course of 160 hours. The most notable aspect of this
program is the adoption of handheld devices for use by the refresher students who, as a result, more actively use the medical library
and experience great self-confidence with both knowing how to use technology and returning to nursing practice (Colevins, Bond &
Clark 2006). The program is still active today. For information on CE, visit the American Nurses Association CE page at
http://nursingworld.org/MainMenuCategories/CertificationandAccreditation/Continuing-Professional-Development.
The Future
Virtual reality–embedded education holds phenomenal promise for the future. Once the purview of gamers and geeks, virtual reality
has exploded onto the academic scene and offers the potential for cross-pollination between fields of inquiry across the curriculum,
university, and even learning systems. All kinds of university departments will undoubtedly experiment with virtual reality in hopes of
staying current and pleasing their young and demanding constituency; however, much in the way society loves the feel of a book too
much to make it archaic, so too will students, educators, and administrators ultimately return to some form of face-to-face classroom
teaching, even as newer and more adventuresome teaching technologies emerge. Further, once fast, high-burn technologies stop
flooding the marketplace and making education an even bigger business for proprietary online universities, modified traditions will
take their place, creating new spaces for nontraditional students and the Internet generation—both groups that are anxious for
technology use in the classroom for very different reasons.
Exploring Information Fair Use and Copyright Restrictions
Fair use refers to a legal concept that permits the use of copyrighted works for specific purposes without obtaining permission from
the author or without paying for the use of the work. Originally, fair use evolved for written work and allowed for uses that include
journalists reporting the news, teaching, or scholarly research. As digital technology and the Web burst upon the scene, fair use
expanded to apply to the copying and redistribution of digital media including photographs, graphics, music, videos, audio, and
software or computer programs.
Four factors must be considered in determining whether a particular use is fair. These factors are derived directly from the fair use
provision (http://www.copyright.gov/fls/fl102.html) of Section 107 of the U.S. Copyright Law (U.S. Copyright Office, n.d.):
1. The purpose and character of the use, including whether such use is of commercial nature or is for nonprofit educational
purposes
2. The nature of the copyrighted work
3. The amount and substantiality of the portion used in relation to the copyrighted work as a whole
4. The effect of the use upon the potential market for, or value of, the copyrighted work
The first factor tends to favor educational institutions and nonprofit entities. The second factor relates to the nature of the work:
Courts have consistently protected creative works and those that have not yet been published. The third factor that must be considered
relates to the amount of use. Typically you should determine how much of the overall work you are using; if it represents the core or
essence of the work, you should not replicate or use it. The fourth and final factor relates to the effect on the creator’s market share. If
you are using a substantial portion of a text or software work that is offered for sale, you can adversely affect its owner’s earning
potential. The term itself should make you reflect on all of these factors and decide whether your proposed use is fair.
Copyright refers to the exclusive right of the creator of a work to distribute, sell, publish, copy, lease, or display that work in
whatever manner he or she so chooses. Copyright laws are not only misinterpreted but are constantly being challenged by our
advancing technological capabilities. Even though you use American Psychological Association (APA) formatting, for example, and
cite the authors, you might be overstepping your rights and infringing on the author’s copyright; you might not be accused of
plagiarism, but you should always cite where you obtained your information or digital media.
All users of others’ work, in whatever medium, must fully understand, be well aware of, and comply with copyright and fair use
principles. Typically, you should try to think of what is reasonable use and always make sure that you cite the authors. Reflect on all
four fair use factors before making your decision to use another’s work for educational purposes. Remember, also, that you are serving
as a role model regarding copyright and fair use behaviors for your students.
Summary
In an ideal world, nurses will work against the assumption that technology runs itself and take proactive roles in helping to design the
education necessary best to prepare them for real-world scenarios. Consider that flash is not substance, and drama is not depth;
technology performs only as well as the pedagogy that undergirds and sustains it. Plan for and use technology with care so that its best
features consequently enrich yours as an educator or learner.
THOUGHT-PROVOKING
QUESTIONS
1. What are some of the forces behind the push toward a more wired learning experience in nursing education?
2. Which technology do you find most beneficial to use in your practice or education setting? Why do you find this tool useful? From your
perspective, how could you enhance this tool?
3. Jean, a diabetes nurse educator, recently read an article in an online journal that she accessed through her health agency’s database subscription.
The article provided a comprehensive checklist for managing diabetes in older adults, which Jean prints out and distributes to her patients in a
diabetes education class. Does this constitute fair use or is it a copyright violation? Explain your answer.
References
Affara, F., Cross, S., & Schober, M. (2001). Discovering resources: Making global connections, international networking. Journal of the American
Academy of Nurse Practitioners, 13(10), 445–448.
Alexander, J. G., Baldwin, M. S., & McDaniel, G. S. (2002). The nursing portfolio: A reflection of a professional. Journal of Continuing Education in
Nursing, 33(2), 55–59.
American Nurses Association. (2000). Scope and standards of practice for nursing professional development. Washington, DC: Author.
Aquino-Russell, C., Maillard Strüby, F. V., & Reviczky, K. (2007). Living attentive presence and changing perspectives with a Web-based nursing
theory course. Nursing Science Quarterly, 20(2), 128–134.
Armitage, P., & Burnard, P. (1991). Mentors or preceptors? Narrowing the theory–practice gap. Nurse Education Today, 11, 225–229.
Barnes, L., & Rudge, T. (2005). Virtual reality or real virtuality: The space of flows and nursing practice. Nursing Inquiry, 12(4), 306–315.
Bassendowski, S. L. (2005). NursingQuest: Supporting an analysis of nursing issues. Journal of Nursing Education, 46(2), 92–95.
Bell, S. (2003). Cyber-guest lecturers: Using webcasts as a teaching tool. TechTrends, 47(4), 10–14.
Benner, P., Tanner, C., & Chelsa, C. (1997). The social fabric of nursing knowledge. American Journal of Nursing, 97(7), 16BBB–16DDD.
Berg, J. A. (2005). Creating a professional poster presentation: Focus on nurse practitioners. Journal of the American Academy of Nurse Practitioners,
17(7), 245–249.
Bergren, M. D. (2000). Power up your presentation with PowerPoint. Journal of School Nursing, 16(4), 44–47.
Black, C. D., & Watties-Daniels, A. D. (2006). Cutting edge technology to enhance nursing classroom instruction at Coppin State University. ABNF
Journal, 17(3), 103–106.
Bracke, P. J., & Dickstein, R. (2002). Web tutorials and scalable instruction: Testing the waters. Reference Services Review, 30(4), 330–338.
Carty, B., & Ong, I. (2006). The nursing curriculum in the information age. In V. Saba & K. McCormick (Eds.), Essentials of nursing informatics (4th
ed., pp. 517–532). New York, NY: McGraw-Hill.
Chase, D. (2007). Transformative sharing with instant messaging, wikis, interactive maps, and Flickr. Computers in Libraries, 27(1), 7–56.
Clochesy, J. M. (2004). Hardware and software options. In J. Fitzpatrick & S. Montgomery (Eds.), Internet for nursing research (pp. 120–128). New
York, NY: Springer.
Cockrell, K., Caplow, J., & Donaldson, J. (2000). A context for learning: Collaborative groups in the problem-based learning environment. Review of
Higher Education, 23(3), 347–363.
http://www.copyright.gov/fls/fl102.html
Colevins, H., Bond, D., & Clark, K. (2006). Nurse refresher students get a hand from handhelds. Computers in Libraries, 6–7, 46–48.
Costa, D. M. (2007). The collaborative fieldwork model. OT Practice, 12(1), 25–26.
Dede, C. (2005). Planning for neomillennial learning styles. EDUCAUSE Quarterly, 28(1). DeSantis, S. (2002). Re-envisioning the pedagogical bridge:
The new instructional designer. Presented at the Pennsylvania Association for Educational Communications and Technology, Hershey, PA.
Dewald, N. H. (1999). Transporting good library instruction practices into the Web environment: An analysis of online tutorials. Journal of Academic
Librarianship, 25(1), 26–32.
Dickerson, P. S. (2005). Evaluation: Part I. evaluating learning activities. Journal of Continuing Education in Nursing, 36, 191–192.
EDUCAUSE Learning Initiative. (2006, June). 7 things you should know about … virtual worlds. http://www.educause.edu/library/resources/7-things-
you-should-know-about-virtual-worlds
Edwards, M. J. A., & Drury, R. M. (2000). Using computers in basic nursing education, continuing education, and patient education. In M. J. Ball, K. J.
Hannah, S. K. Newbold, & J. V. Douglas (Eds.),
Nursing informatics: Where caring and technology meet (pp. 49–68). New York, NY: Springer. Felder, R., & Prince, M. (2007). The case for inductive
teaching. Prism, 17(2), 55.
Froman, R. D., Hall, A. W., Shah, A., Bernstein, J. M., & Galloway, R. Y. (2003). A methodology for supporting research and scholarship. Nursing
Outlook, 51(2), 84–89.
Gabbard, R. B., Kaiser, A., & Kaunelis, D. (2007). Redesigning a library space for collaborative learning. Computers in Libraries, 27(5), 6–12.
Gagen, L., & Bowie, S. (2005). Effective mentoring: A case for training mentors for novice teachers. Journal of Physical Education, Recreation &
Dance, 76(7), 40–45.
Garavalia, L. S. (2002). Selecting appropriate assessment methods: Asking the right questions. American Journal of Pharmaceutical Education, 66,
108–112.
Gillibrand, W. P., Burton, C., & Watkins, G. G. (2002). Clinical networks for nursing research. International Nursing Review, 49(3), 188–193.
Gordon, A. M. (2007). Sound off! The possibilities of podcasting. Book Links, 16–18.
Grant, M. J., & Brettle, A. J. (2006). Developing and evaluating an interactive information skills tutorial. Health Information and Libraries Journal,
23(2), 79–88.
Hebda, T., Czar, P., & Mascara, C. (2005). Handbook of informatics for nurses & health care professionals (3rd ed.). Upper Saddle River, NJ: Prentice
Hall.
Hibbison, E. (2001). Hybrid courses. http://vccslitonline.cc.va.us/mrcte/many_media.htm Johnson, D. W., & Johnson, R. T. (1990). Learning together
and alone: Cooperative, competitive and individualistic learning. Boston, MA: Allyn & Bacon.
Jokelainen, M., Turunen, H., Tossavainen, K., Jamookeeah, D., & Coco, K. (2011, March 24). A systematic review of mentoring nursing students in
clinical placements. Journal of Clinical Nursing, 20, 2854–2867. doi: 10.1111/j.1365-2702.2010.03571.x
Kramer, M. (1974). Reality shock. St. Louis, MO: Mosby.
Leasure, A., Davis, L., & Thievon, S. (2000). Comparison of student outcomes and preferences in a traditional vs. World Wide Web–based
baccalaureate nursing research course. Journal of Nursing Education, 39, 149–154.
Maag, M. (2005). The potential use of “blogs” in nursing education. CIN: Computers, Informatics, Nursing, 23(1), 16–26.
McHugh, M. L. (2006). Computer hardware. In V. Saba & K. McCormick (Eds.), Essentials of nursing informatics (4th ed., pp. 517–532). New York,
NY: McGraw-Hill.
Menix, K. D. (2007). Evaluation of learning and program effectiveness. Continuing Education in Nursing, 38(5), 201–208.
Moreau, E. (2013). What is a webinar? http://webtrends.about.com/od/office20/a/What-Is-AWebinar.htm
Neary, M. (2000). Supporting students’ learning and professional development through the process of continuous assessment and mentorship. Nurse
Education Today, 20, 463–474.
Nicholl, H., & Tracey, C. (2007). Networking for nurses. Nursing Management, 13(9), 26–29. Oblinger, D. G. (2005). Learners, learning, & technology:
The EDUCAUSE learning initiative. EDUCAUSE Review, 66–75.
Pediatric Nursing Certification Board. (2007). About recertification. http://www.pncb.org/ptistore/control/certs/cpn-cpnp/index
Peterson, T. (2007, November 19). Here’s the skinny: What you need to do to become and stay certified. http://www.medscape.com/viewarticle/562945
Phillips, V. (1999). Internet changing economics of higher education. http://www.cnn.com/TECH/computing/9905/05/neted.idg/index.html
Pravikoff, D. S., & Levy, J. R. (2006). Computerized information resources. In V. Saba & McCormick (Eds.), Essentials of nursing informatics (4th ed.,
pp. 517–532). New York, NY: McGraw-Hill.
Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, 9(5). http://www.marcprensky.com/writing/Prensky%20-
%20Digital%20Natives,%20Digital%20Immigrants%20-%20Part1
Pusic, M. V., Pachev, G. S., & MacDonald, W. A. (2007). Embedding medical student computer tutorials into a busy emergency room department.
Academic Emergency Medicine, 14(2), 138–148.
Redman, R., Lenburg, C. B., & Walker, P. H. (1999). Competency assessment: Methods for development and implementation in nursing education.
Online Journal of Nursing. http://www.nursingworld.org/nursingcompetencies/
Ridley, R. T. (2007). Interactive teaching: A concept analysis. Journal of Nursing Education, 46(5), 206–209.
Riley, J. B. (1996). Educational applications. In V. K. Saba & K. A. McCormick (Eds.), Essentials of computers for nurses (2nd ed., pp. 527–573). New
York, NY: McGraw-Hill.
Schlomer, R. S., Anderson, M. A., & Shaw, R. (1997). Teaching strategies and knowledge retention. Journal of Nursing Staff Development, 13(5), 249–
253.
Seropian, M. A., Brown, K., Gavilanes, J. S., & Driggers, B. (2004). Simulation: Not just a manikin. Journal of Nursing Education, 43(4), 164–170.
Shaffer, S. C., Lackey, S. P., & Bolling, G. W. (2006). Blogging as venue for nurse faculty development. Nursing Education Perspectives, 27(3), 126–
128.
Skiba, D. J. (2007a). Do your students wiki? Nursing Education Perspectives, 26(2), 120–121. Skiba, D. J. (2007b). Nursing education 2.0: Second Life.
Nursing Education Perspectives, 28(3), 156–157.
Sousa, D. A. (1995). How the brain learns. Reston, VA: National Association of Secondary School Principals.
Spillane, J. (2006). Virtual reality takes on patient safety. Nursing Spectrum (New York/New Jersey Metro Edition), 18A(7), 13.
Thackeray, R., Neiger, B. L., & Roe, K. M. (2005). Certified health education specialists’ participation in professional associations: Implications for
marketing and membership. American Journal of Health Education, 36(6), 337–344.
Turley, J. P. (2000). Nursing’s future: Ubiquitous computing, virtual reality, and augmented reality. In M. J. Ball, K. J. Hannah, S. K. Newbold, & J. V.
Douglas (Eds.), Nursing informatics: Where caring and technology meet (pp. 49–68). New York, NY: Springer.
University of Colorado at Boulder, Office of Planning, Budget and Analysis. (2001). Assessment methods used by academic departments and programs.
http://www.colorado.edu/pba/outcomes/ovview/mwithin.htm
U.S. Copyright Office. (n.d.). Copyright law of the United States of America and related laws contained in Title 17 of the United States code.
http://www.copyright.gov/title17/92chap1.html#107
Winfield, W., Mealy, M., & Scheibel, P. (1998). In Distance Learning ’98: Proceedings of the annual conference on distance teaching and learning.
Madison, WI, August 5–7, 1998.
Chapter 21
Simulation in Nursing Informatics Education
Nickolaus Miehl
OBJECTIVES
1. Describe the role of simulation in nursing informatics education.
2. Differentiate between the types of simulated electronic health records available for use.
3. Identify the limitations of using a live clinical information system for educational purposes.
Key Terms
Database
Dynamic webpage shells
Fidelity
Server
Simulated documentation
Simulation
Simulation scenario
Simulator
Introduction
The patient call bell is ringing; you enter the room to find the patient verbalizing complaints of chest tightness. In a moment, the
patient becomes unresponsive and a code is called. The team quickly responds, initiating resuscitation measures per advanced cardiac
life support (ACLS) protocol. You review the electronic health records (EHRs) with the attending physician while simultaneously
discussing your assessment before the code. After a short while, the resuscitation efforts are successful and the patient is stable enough
for transfer to the intensive care unit. You complete your documentation in the patient’s EHR, the simulation scenario ends, and the
debriefing begins. The instructor provides feedback on not only actions taken within the simulation scenario, but also on the use of the
EHR as an important resource for patient information and documentation.
Perhaps it is the first day of a new course and rather than a lecture-based class with an accompanying textbook, the instructor uses
an active learning approach with case studies delivered through an EHR interface to facilitate the learning and application of clinical
concepts. In this example, rather than being part of an entire simulation scenario, the EHR itself is the learning tool providing learners
with a handson learning opportunity centered on accessing and using the information contained within the patient record.
Nursing Informatics Competencies in Nursing Education
In the late 1990s, it was identified that healthcare professionals needed to possess both skill and knowledge of informatics (American
Association of Colleges of Nursing, 1997; Gassert, 1998; Pew Health Professions Commission, 1998). Additionally, information
technology has been identified as a key measure in improving patient safety and quality of care (American Academy of Nursing, 2003;
Institute of Medicine, 2000). In response to this increasing demand for practitioners to become skilled in this area, coupled with the
absence of research-based informatics competencies, a Delphi study was used to identify informatics competencies for nurses at four
different levels of practice (Staggers, Gassert, & Curran, 2002). In essence, this seminal study created informatics competencies for
entry-level nurses through informatics specialists and innovators, with a focus on computer skills, informatics knowledge, and
informatics skills.
Although informatics competencies for nurses have been identified, the degree to which schools of nursing have woven them into
the curriculum varies greatly (Carty & Ong, 2006). In a study conducted by Fetter (2009), a survey of graduating senior nursing
students ranked the following competencies with which they had no experience or minimal skill: (1) using applications to document,
(2) creating an electroinic care plan, (3) valuing informatics knowledge for practice, (4) valuing informatics knowledge for skill
development, and (5) using applications for data entry.
The question then becomes, Which best practices will ensure that students become prepared in informatics? In a position statement
by the National League for Nursing (2008), results from a survey of nursing educators and administrators indicated that only 50–60%
of respondents said that informatics was integrated throughout the curriculum and that experience with nursing informatics was
provided during clinical rotations. Findings also suggested that little clinically related informatics content and few such learning
experiences were provided in nursing programs. Use of personal digital assistants containing care planning software and clinical
information systems were least likely to be incorporated into the courses. With emerging technologies in nursing and healthcare
education, the use of simulation to allow students to use informatics in an active manner, in an authentic and realistic learning context,
is one potential approach to remedy these shortcomings.
A Case for Simulation
In general terms, a simulator can perhaps best be described as a tool designed to emulate some aspect of the clinical practice
environment, which may be focused on a single task or designed to mimic a complete patient care situation (Gaba & DeAnda, 1998).
In its essence, it is any device that is used to create a realistic learning experience for the learner but that removes the risk
associated with learning during hands-on patient care. A simulator offers the unique ability to create a realistic learning environment
that is safe, structured, and supportive for the learner (Bligh & Bleakley, 2006).
Simulators encompass a broad range of devices, such as partial task trainers (e.g., an IV insertion arm); screen-based simulations,
including simulated EHRs, simulated documentation, and simulated environments replicating a realistic patient care area; and complex
computer-driven human patient simulation manikins. Although each of these simulation modalities can be used alone, collectively they
can be powerful learning tools when used together to create a realistic patient care scenario. The goal of simulation, according to Gaba
(2004) is a seamless immersion into the simulated practice environment during which learners are drawn into the reality of the
environment or task at hand. Hertel and Millis (2002) note that this is a cooperative process whereby learners come together in an
authentic setting and begin to learn from one another.
Considering the realistic nature of simulation and its hands-on active approach to learning, it seems that the use of simulation
modalities can be a powerful tool in moving student nurses—indeed, any practitioners—toward achieving the informatics
competencies. Recall the examples given at the beginning of this chapter. In the first example, the EHR is part of a larger simulation
scenario that mimics a real-life clinical case. In the second example, the EHR itself functions as a simulator and becomes a true-to-life
learning tool. In either case, simulation is used to incorporate nursing informatics into the context of patient care, thereby giving
students an authentic learning experience that can be applied in clinical practice.
Incorporating EHRs into the Learning Environment
There are two main approaches to the incorporation of an EHR into the learning environment, whether used within a simulated clinical
environment as part of a patient care scenario or as a stand-alone learning tool. First, the EHR can be created specifically for
simulation purposes; options range from a well-developed Microsoft Access database to commercially available products designed
specifically for simulation purposes. Second, the simulation may use a real EHR system, either within a hospital-based simulation
center or through a partnership with a healthcare facility or an EHR vendor.
There are certain advantages and disadvantages to each simulated documentation solution. As Brown (2005) notes, whereas
“live” documentation systems provide learners with a realistic experience and can be incorporated into the learning environment, they
also present certain drawbacks: (1) they are designed for the patient care environment, not the learning environment, and therefore lack
an efficient feedback mechanism for learners; (2) they are designed to work in real time, not simulated time, creating issues with data
recall, especially when a record may be used repeatedly over a period of months or years; and (3) if a system is overly complex, it may
unintentionally focus the learning on the specific system, rather than the process of data retrieval and documentation. Refer to the
Research Brief for more on the challenges of teaching clinical documentation skills in an EHR.
Research Brief
Faculty perceptions of the challenges of teaching undergraduate students proper clinical documentation in both paper-based and electronic systems
are described in a qualitative research study by Mahon, Nickitas, and Nokes (2010). In this study, participants (N = 25) were interviewed using both
openand closed-ended questions, and results were analyzed using a constant comparative method. The most common method of teaching
documentation skills was some variation of demonstration–return demonstration method. Faculty were concerned about the amount of time taken
honing documentation skills in the actual clinical area, indicating a median of 2 hours of an 8-hour clinical day was taken up with this task, and
shared that there was seemingly little documentation taught in the classroom or laboratory. Faculty relied heavily on experts in the clinical setting
and used their documentation as models for students to emulate. In the case of the electronic health record documentation, on-site nursing experts
proficient in the use of the system were especially useful as role models. However, faculty remained concerned that using the electronic system and
the endless drop-down menus might actually interfere with the development of nursing expertise and critical thinking.
One critical issue that was shared by faculty with regard to electronic documentation was that the clinical facility provided the instructor with
only one access code, so that all of the students in the clinical group used the same code to document, and there was limited access to computers on
the clinical unit. The faculty was very concerned about the legal and ethical issues for appropriate documentation and for the provision of care, such
as on-time medication administration in a group of 8–10 students with one access code.
The authors suggest the need to integrate information competencies throughout the curricula and to provide opportunities for faculty development
in informatics. They suggest that “faculty competencies in the area of informatics must be identified and standardized” and that faculty must learn to
“model self-efficacy: the patience, support and persistence that characterize individual development within a professional discipline” (p. 620).
Source: The full article appears in Mahon, P. Y., Nickitas, D. M., & Nokes, K. M. (2010). Faculty perceptions of student documentation skills
during the transition from paper-based to electronic health records systems. Journal of Nursing Education, 49(11), 615. Retrieved from
http://searchproquest.com/docview/762454353?accountid=13158
A qualitative research study by Kennedy, Pallikkathayil, and Warren (2009) describes the experiences and development of nursing process skills
in nursing students (N = 5) using the Simulated E-hEalth Delivery System (SEEDS) learning innovation. In the SEEDS learning innovation, students
were given written case studies and asked to enter the patient data in a simulated electronic health record and generate a care plan for the patient and
family. The authors concluded, “The technology provided an interactive venue for developing nursing process skills by linking assessment data from
case studies with foundational concepts in nursing” (p. 99). “The exercise was authentic, dynamic, and learner centered” (p. 99). As a result of the
themes discovered in this qualitative study, the authors proposed two hypotheses for future research to explore learning outcomes resulting from the
use of a simulated e-health system:
During postclinical conferences, technologically competent students who use a modified electronic health record for documentation of patient data
and care planning for the next day of practice will engage in more interactive discussion with other students and the teacher, experience greater
learner satisfaction, and demonstrate better nursing process skills than will students who participate in the traditional postclinical group discussion
about patient care.
Following didactic instruction about nursing care of a patient with a specific disease, technologically competent students who use a modified
electronic health record with embedded decision support to enter patient assessment data from a case study about the disease and then develop a
care plan will communicate greater learner satisfaction, demonstrate better nursing process skills, and attain higher test scores on the specific topic
than will students who organize the assessment data and develop a care plan with paper and pencil (p. 100).
Source: The full article appears in Kennedy, D., Pallikkathayil, L., & Warren, J. J. (2009). Using a modified electronic health record to develop
nursing process skills. Journal of Nursing Education, 48(2), 96. Retrieved from http://search.proquest.com/docview/203936907?accountid=13158
One system designed specifically for simulation is the Internetbased medical chart (IMC), as described by Brown (2005). This
system requires four components: (1) a database, (2) dynamic webpage shells, (3) a server, and (4) computers with access to the
Internet. With this system, a Microsoft Access database is created to hold administrative information about the simulation scenario
and other pertinent overview information accessible only by the instructor, as well as simulated patient data, simulated patient
documentation, student documentation entries, and learner feedback from instructors. Each time a learner logs into the IMC system via
the Internet, the server custom-creates the requested page using the existing database information, user-specific information, and the
webpage shells to create a realistic EHR for use by the student. Although this type of system offers a great deal of flexibility, because
it is custom created by the end user (but is certainly a cost-effective solution), it requires that the simulation instructor have a strong
background in computer science and information technology to create and maintain the database and supporting materials.
One example of a commercially available solution is Elsevier’s Simulation Learning System (2010). This system includes all of the
elements needed for preparing, programming, running, and debriefing a simulation scenario, including a fully functional EHR. The
EHR is linked to the simulation scenario and contains all of the pertinent patient information for learners to access before or during the
simulation scenario. This system also incorporates the ability for learners to document just as they would in an actual clinical setting,
with the capability of submitting the documentation to the instructor for evaluation and feedback. A major strength of this type of
system is that it is a prepackaged Web-based solution that does not need to be created from scratch.
Additionally, Elsevier’s Simulation Learning System includes all of the necessary tools for the instructor or simulation center staff
to build the simulation scenario, including (but not limited to) programming guides, staging and scripting information for the scenario,
and debriefing guides. One potential disadvantage with any commercially available solution, however, is the cost to purchase it, which
varies depending on the product and vendor.
Although the main disadvantages of a live system were discussed at the beginning of this section, the use of a real EHR system
clearly provides learners with a truly authentic experience. One innovative solution to bridge this gap was developed out of an
academic–business partnership between the Cerner Corporation and the University of Kansas School of Nursing. The Simulated E-
hEalth Delivery System (SEEDS) incorporated the use of Cerner Corporation’s clinical information system and PowerChart
application (Connors, Weaver, Warren, & Miller, 2002). This system was specifically adapted for educational purposes to address the
learner’s informatics needs. Similar to the IMC system discussed previously, instructors developed the patient data stored within the
Cerner Corporation’s clinical information system database, creating virtual patients within the system. Students could navigate through
the system and view pertinent patient data and then document assessment information and create a plan of care within the PowerChart
application. Additionally, the instructor could access student documentation for evaluation and feedback. Refer to the Research Brief
for a discussion of a study on the use of the SEEDS approach.
This system, now known as Cerner’s Academic Education Solution, has been introduced at both the University of Missouri–
Kansas City and the University of Utah; a consortium has been created between the schools to collaborate on development and
implementation strategies for using this system (Gassert & Sward, 2007). The Academic Education Solution is a fully functional
clinical information system adapted for educational purposes to support and facilitate the development of informatics competencies in
a realistic fashion.
Challenges and Opportunities
The adoption and use of simulation technologies present unique advantages and disadvantages. Using simulated medical records,
either as a stand-alone learning tool or in conjunction with a complete simulation scenario, provides the learner with an opportunity for
a realistic, hands-on learning experience. Major considerations when looking to adopt a simulated EHR include (1) cost, (2) ease of
use for the instructor and learner, (3) technical support from the vendor, (4) time to build or develop the patient database, (5) additional
simulation materials included with the package, (6) flexibility of the system to be customized and used as a stand-alone tool or in the
setting of a full-scale simulation scenario, and (7) overall fidelity or realism.
In 2006, a coalition consisting of experts from the fields of health care, informatics, business and industry, and nursing proposed
the Technology Informatics Guiding Education Reform (TIGER) initiative (TIGER, 2007). The aim of this group is to advance the
integration of informatics core competencies into nursing education so as to provide better and safer care to patients. Seven key steps
were established to meet the 10-year vision of the TIGER initiative. Of particular interest is the call to take an active role in the design
and integration of informatics tools that are “intuitive, affordable, usable, responsive and evidence-based” (p. 5). This approach will
promote truly new and innovative strategies for informatics education and create significant opportunities for collaboration between
industry, academia, and clinical practice.
What Does the Future Hold?
Simulation will clearly play an important role in the development of informatics competencies for student nurses and practitioners.
One theme of simulation-based learning is practicing just as a nurse would in the actual clinical setting. With regard to facilitating the
growth of informatics competencies, it is no different. If there are expectations regarding the use of clinical information systems and
EHRs in the clinical setting, then the opportunity also exists for the incorporation of such tools into the classroom and simulated
clinical setting.
Aside from using the simulated EHR in the setting of a clinical simulation scenario, there are also opportunities to incorporate the
simulated EHR into the classroom in new and innovative ways. As mentioned in the beginning of the chapter in the second scenario,
the EHR can be used as an active learning tool within the classroom. Rather than requiring students to absorb information from a book,
the EHR can become a powerful way for learners to make important connections about caring for patients with a specific disease
process or to learn concepts of pathophysiology or pharmacology.
Summary
Simulated experiences in nursing education provide important opportunities for students to hone critical thinking and clinical skills in
a safe and supportive environment. Simulation scenarios may also provide a better variety of clinical experiences than those available
in a real setting, and also provide nursing faculty the opportunity to track student progress and development against specific learning
objectives. Nurse educators can share simulation scenarios and experiences and thus contribute to the body of nursing education
knowledge to improve education practice.
THOUGHT-PROVOKING
QUESTIONS
1. Consider your experience and learning with regard to EHRs. If you were to design a learning program centered on the use of EHRs, what would
it look like? Consider the viewpoint of a student, a clinician, and a healthcare administrator.
2. Think about the clinical courses you have taken as a student. Which opportunities and challenges exist regarding the use of an EHR as a major
learning tool in conjunction with, or perhaps even replacing, the required textbook?
3. If you were to design a simulation scenario incorporating the use of an EHR, which informatics competencies would you focus on and why?
References
American Academy of Nursing. (2003). Proceedings of the American Academy of Nursing conference on using innovative technology to decrease
nursing demand and enhance patient care delivery. Nursing Outlook, 51, 1–41.
American Association of Colleges of Nursing. (1997). A vision of baccalaureate and graduate nursing education: The next decade. Washington, DC:
Author.
Bligh, J., & Bleakley, A. (2006). Distributing menus to hungry learners: Can learning by simulation become simulation of learning? Medical Teacher,
28(7), 606–613.
Brown, M. C. (2005). Internet-based medical chart for documentation and evaluation of simulated patient care activities. American Journal of
Pharmaceutical Education, 69(1–5), 204.
Carty, R., & Ong, I. (2006). The nursing curriculum in the information age. In V. K. Saba & K.A. McCormick (Eds.), Essentials of nursing informatics
(4th ed., pp. 517–531). New York, NY: McGraw-Hill.
Connors, H. R., Weaver, C., Warren, J., & Miller, K. L. (2002). An academic–business partnership for advancing clinical informatics. Nursing
Education Perspectives, 23(5), 228–233.
Fetter, M. (2009). Graduating nurses’ self-evaluation of information technology competencies. Journal of Nursing Education, 48(2), 86.
Gaba, D. (2004). The future vision of simulation in health care. Quality and Safety in Healthcare, 13, 2–10.
Gaba, D., & DeAnda, A. A. (1998). A comprehensive anesthesia simulation environment: Recreating the operating room for research and training.
Anesthesiology, 69, 387–393.
Gassert, C. A. (1998). The challenge of meeting patients’ needs with a national nursing informatics agenda. Journal of the American Medical
Association, 3, 263–268.
Gassert, C. A., & Sward, K. A. (2007). Phase I implementation of an academic medical record for integrating information management competencies
into a nursing curriculum. Studies in Health Technology and Informatics, 129(Pt 2), 1392–1395.
Hertel, J. P., & Millis, B. J. (2002). Using simulations to promote learning in higher education: An introduction. Sterling, VA: Stylus.
Institute of Medicine. (2000). To err is human: Building a safer system. Washington, DC: National Academic Press.
National League for Nursing. (2008). Preparing the next generation of nurses to practice in a technology-rich environment: An informatics agenda
[Position statement]. New York, NY: Author.
Pew Health Professions Commission. (1998). Recreating health professional practice for a new century: The fourth report of the Pew Health
Professions Commission. San Francisco, CA: Author.
Simulation Learning System. (2010). http://www.elsevieradvantage.com/article.jsp?pageid=10705 Staggers, N., Gassert, C. A., & Curran, C. (2002). A
Delphi study to determine informatics competencies for nurses at four levels of practice. Nursing Research, 51(6), 383–390.
Technology Informatics Guiding Education Reform (TIGER). (2007). The TIGER initiative: Evidence and informatics transforming nursing 3-year
action steps toward a 10-year vision. http://www.tigersummit.com
Chapter 22
Games, Simulations, and Virtual Worlds for Educators
Brett Bixler
OBJECTIVES
1. Distinguish among learning environments as games, simulations, or virtual worlds.
2. Identify the different genres of games, simulations, and virtual worlds.
3. Compare and contrast games, simulations, and virtual worlds as informatics tools for nursing education.
4. Describe strategies choosing among a game, simulation, or virtual world as the best choice for instructional delivery in a given educational
situation.
Key Terms
3D
Augmented-reality game
Avatar
Feedback
Fidelity
Game
Massive multiplayer online role-playing game
Multiuser dungeon
Nonplayer character
Object-oriented multiuser dungeon
Scaffolding
Simulation
Virtual world
Introduction
The use of educational games, simulations, and virtual worlds for education continues to grow, with a great deal of research effort and
funds directed toward the discoveries of their best uses. Educational games, simulations, and virtual worlds all share some
characteristics, and it is difficult to find a pure experience in any of the genres. Simulations may have gamelike qualities, and virtual
worlds may be used to present a simulation. This chapter introduces the reader to all three genres.
Case Scenario
Joe sits down at the computer and logs into StratWorld, a virtual world that enables the user to create his or her own team, then
compete against other teams created by other StratWorld players. The developers of StratWorld create interesting challenges, part
intellectual and part brute force, which teams strive to solve before other, competing teams solve them first.
After Joe logs on, he is presented with a three-dimensional (3D) view of a forest. Directly in front of him is a 3D figure that looks
very much like Joe, except for broader shoulders, a more rugged face, and better skin complexion. This is Joe’s avatar, his
representation of self in StratWorld. Joe can change his avatar’s appearance as he wishes, but he likes to stick to something close to the
real thing. The members of one of his opposing teams in StratWorld all prefer to appear as masses of glowing tubes. Joe thinks they
are strange.
Today, he is recruiting for his virtual team, so he ducks into his inventory (a place to store stuff his avatar can wear and use) and
dons his manager’s jacket. “Joe’s Team” is proudly displayed on the back. Joe is proud of the jacket; he created the lettering himself in
a graphics program and uploaded it to StratWorld, then added it to a plain jacket and gave a copy to all new members of his team in
StratWorld. As in many virtual worlds, clothes do make the man, woman, or thing.
Using the arrow keys and the mouse to manipulate his avatar, Joe begins to make his avatar walk down the forest trail. He is
looking to recruit an Ogre for his team to beef up their physical offensive capabilities and replace the recent loss of Charlie the
Unicorn. Ogres are big and strong, perfect for the task. Joe is a little nervous. He has never been to this part of StratWorld before, and
explorations in new areas can be fraught with peril. After a brief sojourn, Joe comes upon an Ogre sitting in a daisy-strewn clearing,
picking his teeth with a small sapling ripped from the ground. “Ogre!” Joe shouts. “I need someone to smash through things for my
team. Interested?”
“What in it for me?” the Ogre asks, thumping his chest with the remains of the sorry sapling, splintering it in oblivion. “Darn! That
was good toothpick!” The chirps of birds, the drone of insects in the foliage, all normal background noise here, suddenly stop. Joe
picks up on this environmental clue, and just as in the real world when this happens, knows he is in danger.
Joe ponders the question. He knows he is talking to a nonplayer character, one that seems to have a brain behind the 3D façade,
but in reality has very clever programming attached to it so that it can seem to carry on an intelligent conversation. Some companies
call this artificial intelligence, but both the creators of these environments and the scholars who study them hotly debate the proper use
of that term. Joe also knows that the world is constructed so that he has to balance his profits from game wins with overhead costs,
such as player salaries and equipment maintenance. He needs to make an offer to the Ogre. An offer of too little will insult the Ogre,
and a possible fight between it and Joe is the probable result. Joe’s avatar could die, an inconvenience that will cost him time and loss
of reputation with other StratWorlders. A generous offer will probably be accepted, but might bankrupt Joe over time. Joe needs to
balance his needs and costs, and also think outside the box. It is a complex problem-solving situation!
“Okay, Ogre, here’s the deal. Pay is $900 a month, and …” Joe attempts to continue, but the Ogre quickly rises in an aggressive
manner. “Wait! Let me continue! I know that’s a little less than normal, but I’ll throw in a nice, new sapling each week for tooth
maintenance! How about it?”
The Ogre sits back down and eyes Joe warily. “Just need to pick tooths, not maintain ants. Contract for …” (the Ogre pauses to
quickly count his fingers) “10 months?”
“Sure, sure, but if you are injured, you go to half-pay until you can play again,” Joe answers.
“I still get toothpick each week, even if hurt?” “Absolutely. I mean, yes.”
The Ogre leans forward on its haunches. “Sound good! You go. I follow.” “One more thing, Ogre. What do I call you?” Joes asks.
“Daisy! You managers sure stupid!”
Joe takes Daisy back to Joe’s office by teleporting there, a way to move from one place to another with a simple click of a button.
Joe pulls up a map of StratWorld, locates the land he owns, and clicks on it for instantaneous transport. Joes sends Daisy the Ogre
down to practice smashing down walls in his training field, and then pauses for a moment to admire his recreation of his grandfather’s
old roll-top desk. He recreated it from an old photograph just for his office in StratWorld. Looking out the window, he sees Daisy
running out on the training field. He sits down at his desk to go over his team’s statistics. With the addition of Daisy to the team, Joe
needs to recalculate all his strategies. He needs to determine how he can acquire a sapling each week for Daisy; where will he get one,
and how much will it cost? Then he needs to send out an acceptance to a recent invitation from the game developers to participate in
next week’s challenge. A win will be sweet, but it will be a busy week of preparation!
Before Joe hunkers down to work, he sends an instant message to Kathy, an admirable opponent in StratWorld against whom he
has competed several times. Typing furiously and with a certain glee, he writes, “Hi, Kathy, guess what? I’m gonna DUST your team
in the next challenge!” Kathy’s reply is swift: “Bring it on, Joe, bring it on!”
Case Scenario Discussion
This is a brief description of what occurs in many online virtual environments today. People create a presence in the environment, then
manipulate events for a desired outcome. They explore, build things, interact with others, and try to achieve goals. The story is
fictional; there is no StratWorld, but games do exist where people build teams and compete against one another. So, is StratWorld a
simulation, a virtual world, or a game? What do you think? Think about simulations you have experienced or heard of, games you
have played, and anything you have read about virtual worlds. Try looking up some definitions online. Write down your thoughts and
come up with some justifications that back your decision.
Educational Games
People voluntarily play games because they are fun and embody many motivational aspects (Mastrian, McGonigle, Mahan, & Bixler,
2011). Great games provide an optimally challenging state between boredom and frustration (Csikszentmihalyi, 1990). Games exist
within a set of rules (Kelley, 1988; Salen & Zimmerman, 2003), and players receive feedback from their interactions in the game and
rule space.
An educational game—one designed for learning—is a subset of both play and fun, and is sometimes referred to as a “serious
game” (Zyda, 2005). It is a melding of educational content, learning principles, and computer games (Prensky, 2001). Educational
games should emphasize the value of the experience (Nemerow, 1996). Mungai, Jones, and Wong (2002) state that the flow of an
educational game may be under the designer’s control more than a noneducational game, and feedback is used to stress competency,
not just achievement. The trick in designing an educational game is to maintain the same fun state found in noneducational games
(Koster, 2004).
Many different types of games exist, and each type has a different potential for educational use (Mastrian et al., 2011). To learn to
respond quickly and hone reflexes, action games may be used. Adventure games may be used to discover the unknown, such as
diagnosing a patient illness. Construction and building games could be used for building complex mental constructs that can be
understood only through knowledge of their constituent parts and the ways in which they interrelate. Strategy games are great for
nursing education teaching moments where careful, up-front planning is critical and on-the-fly adjustments to one’s plan may be
needed to ensure its success.
In role-playing games, the player takes on the role of one or more characters and improves them while progressing through a
storyline. Today, massive multiplayer online role-playing games are very popular, using the Internet to provide a shared,
simultaneous experience for dozens or even hundreds of players. Role-playing games are an excellent way for nursing educators to
guide students through any situation where a sequenced step-by-step introduction to the parts of the job or skill is required.
Fairly new are casual games, also known as mini-games. These games are designed to be played in a short time span, or for a few
minutes a day over several days, weeks, or even months. Many online browser-based games fit this category. Casual games may be
useful for continuous reinforcement of basic concepts, for emulating a slowly changing environment, and for modifying the player’s
attitudes on a given topic over a period of time. To date, these games remain largely untapped as educational tools.
Educational Simulations
A simulation recreates a real-life set of conditions or events with as much fidelity as possible (Alessi, 1988). Aldrich (2010) contends
that simulations develop cognition (learning-to-know skills), ethics and roles (learning-to-be skills), and application capabilities
(learning-to-do skills). Unlike games, simulations are not necessarily designed to be fun.
Simulations may be experiential and task based, where the learner takes on a firstperson role and executes a self-chosen series of
decisions, manipulating the variables in the simulation toward a desired outcome (Gredler, 1996; Weatherford, n.d.).
Simulations may also be symbolic scenarios, where the learner directly manipulates variables, sees the results of changes, and then
makes decisions on how to continue in the simulation. Spreadsheets are often used for this type of simulation. Symbolic simulations
are good choices for discovering principles, misconceptions, and relationships, and for fostering understanding, prediction, and
solution development (Mastrian et al., 2011).
Simulations may use a process known as scaffolding (Guzdial, 1997; Jonassen, 1999) to assist in acquiring the accepted level of
proficiency. An example of scaffolding is when corrective feedback is initially used, correcting user mistakes and ensuring success,
and then the feedback fades away when it is no longer needed.
Medical simulations use realistic 3D computer models of humans to investigate new medical possibilities and to test assumptions
(learning-to-know skills). Simulations of drawing blood and complex medical operations are used to teach learning-to-do skills. Refer
to the Simulation in Nursing Informatics Education chapter for an in-depth discussion of simulations and the nursing informatics skills
required to develop and run simulation scenarios.
Virtual Worlds
Cohen and colleagues (2013) define a virtual world as follows:
Virtual worlds are live, online, interactive 3-dimensional environments in which users interact using speech or text via a
personalised avatar. Access requires a modern computer and Internet connection. Healthcare practitioners are increasingly
utilising virtual worlds and other web-based technologies for educational purposes, including resuscitation training,
conferences, surgical education and team-working for multidisciplinary healthcare providers. (p. 79)
A 3D virtual world often mimics a real-world environment, although it may also include impossible abilities, such as flying
unaided (Mastrian et al., 2011). Users of virtual worlds are often quick to stress that these creations in and of themselves are not
games, although this confusion is easy to understand because virtual worlds share many of the same interface characteristics as 3D
action and role-playing games.
The best use of virtual worlds for educational purposes may occur when there is a need for an immersive experience coupled with a
need for social interaction. For example, in the virtual world of Second Life, the Penn State University Harrisburg Campus has
developed a virtual hacienda for students learning Spanish (Clark, 2009). Students interact with the environment and the objects in the
hacienda while speaking to one another in Spanish, thereby participating in authentic learning activities. Some of the Second Life
scenarios used by Chamberlain College of Nursing include a real human resources representative whom the student must call; the pair
must discuss the situation and the student then determines a solution based on the representative’s input. This activity immerses the
students in their role and fully engages them in the learning episode. Cohen et al. (2013) tested the use of a virtual world as a training
site for emergency preparedness and coordination for first responders in major incidents (such as a terrorist attack), and conclude
“Major incident exercises are complex in nature and expensive and they thereby require novel methodologies to aid training and
preparation. This study has established the feasibility of developing low-cost, immersive, accessible virtual environments for major
incident preparation using a systematic approach. Both the environment and scenarios were deemed realistic and acceptable for
training and testing of existing plans by clinicians (pp. 83–84).
Virtual worlds need not be 3D. Predecessors to the 3D environment include multiuser dungeons, object-oriented multiuser
dungeons, and multiuser shared hallucinations. All of these environments are text based, so that the user receives environmental
information as passages of text, and manipulates objects and talks to others by typing text commands. Multiuser dungeons, object-
oriented multiuser dungeons, and multiuser shared hallucinations are still in use today.
Choosing Among Educational Games, Simulations, and Virtual Worlds
Educational games, simulations, and virtual worlds all have a great deal of overlap. Games may be placed in virtual worlds, and
simulations may have gamelike elements. Yet, these three tools also have distinctive characteristics. By examining several of these key
characteristics (goal orientation, competition, the fun factor, and exploratory learning and social interaction affordances), it becomes
easier to choose the correct tool for teaching purposes.
Games are goal oriented and may be competitive in nature. They should be fun and perhaps a bit fantastical and light-hearted. A
particular game may or may not include exploratory learning and social interaction. Although simulations are also goal oriented, the
competition is generally subdued. Simulations are generally more realistic and are not necessarily fun to use. A particular simulation
may or may not include exploratory learning and social interaction. Virtual worlds in and of themselves do not have goals or
competition; it is up to the player to construct them and add them to the world. Virtual worlds are not by default fun, although they
may include the fantastical. Virtual worlds generally lend themselves to exploratory learning and social interaction.
The Future of Games, Virtual Worlds, and Simulations
The use of games, virtual worlds, and simulations in Western society continues to increase. The combination of best practices
supported by sound research, the evergrowing power of technology, and learners who grew up using these environments will lead to
greater use of these tools for learning (New Media Consortium, 2007).
In addition, games are becoming less expensive to produce and consume. Game development engines, which were long in the
hands of only major game development companies, are now available at a cost that many users and organizations can afford. Some
games come with built-in development tools, an attempt by the game producers to use free labor to extend their product (Dyer-
Witheford & de Peuter, 2009). The growth of indie (independent) game companies is leading to a plethora of cheap, yet high-quality
games.
The same holds true for virtual worlds. New virtual worlds spring up all the time. Many offer free, if limited, accounts, and
educators are exploring these spaces with increasing regularity, building fantastic learning environments.
Companies are tapping mobile devices as another avenue to push out their games. These devices are already used for a variety of
communication and social functions. Why not build on that with casual games that rely on social interactions? Expect to see much
more happening in this space in the near future.
Another related area of growth is augmented-reality games. Augmented reality occurs when one uses a device, such as a
smartphone, to overlay on the real world additional information (Klopfer & Squire, 2008). For example, one might use the camera in
the phone to view the stars at night and see on the phone’s screen both the stars and the constellation labels and linking lines between
the stars in a constellation. Augmented-reality games exploit this concept in gamelike ways, bringing people together physically and
virtually to solve a series of challenges. In education, augmented-reality games may be used to provide a fun way to collect and
analyze data, to collaborate with other students, to access information resources, and to provide a new way to look at the world.
Serious games may also have a place in helping practicing nurses maintain or hone skills. Baker (2009) suggests that gaming has a
place in continuing education.
The science of nursing practices encourages us to ask questions, promote dialogue, share lessons learned willingly and openly, and
make the outcomes of our patients constructive and positive. Rigorous or high-quality evaluation and research into the teaching and
learning techniques offered by serious games can offer insight into future changes not conceived of at this moment. For example, if a
serious game could effectively assist nurses in maintaining the skill set required to care for a patient who is experiencing hypothermia,
would that be worth the investment? If a game could reduce medication errors in the operating room by 50% to 75% compared to what
has been demonstrated in the past, would that have value? If it were found that after implementation of a serious game, a facility
experienced zero errors in right-site, right-procedure, and right-patient events during a 10-year period, would this be of value (Baker,
2009, p. 173)?
Other new games that can be used to educate health professionals have been discussed by Skiba (2008). She describes Fold.IT, a
game that challenges players to fold proteins as part of a research experience for Life and Death in the Age of Malaria, a game that
simulates advice nurses would give to world travelers on health maintenance strategies, and 3DiMD, designed to facilitate skills
training in teamwork for military environments. Two important websites to watch for emerging games are the Serious Games Initiative
(www.seriousgames.org) and Games for Health (www.gamesforhealth.org).
Best uses of all these technologies and approaches remain a bit murky. Fortunately, a great deal of research whose findings will
guide future educators toward the most effective uses of educational games, simulations, and virtual worlds is under way.
In an ideal world, educators would have a plethora of available, well-designed, and educator-certified games to choose from that
mesh with the educational objectives of their classes, courses, and curricula.
Summary
Games, simulations, and virtual worlds have a great deal of potential as education tools that can augment, supplement, or even replace
traditional methods of teaching. As educators become more skilled in the use of these tools, and as designers share their creative works
with others, we can expect to see these tools used more frequently in the coming decade.
THOUGHT-PROVOKING
QUESTIONS
1. Games are supposed to be fun and voluntary. How can educators force a game on a student and expect it to remain fun and engaging?
2. It can take several hours of game play to learn the mechanics of some games, and even longer for the more complex games. If subject-matter
learning can occur only after this initial game mechanics learning occurs, how can educators justify the amount of time a learner must spend
within the game just to get to the point where learning begins?
3. How do educators acquire the training needed not just to get by in these new environments, but rather flourish, thrive, and mold the environments
to their purposes?
References
Aldrich, C. (2010, August). Virtual worlds, simulations and games for online educators. Manga Online Seminar presented at Penn State University,
University Park, PA.
Alessi, S. M. (1988). Fidelity in the design of instructional simulations. Journal of Computer-Based Instruction, 15(2), 40–47.
Baker, J. (2009). Serious games and perioperative nursing. Association of Operating Room Nurses. AORN Journal, 90(2), 173–175. Retrieved from
http://www.seriousgames.org
http://www.gamesforhealth.org
Health Module (Document ID: 1853293981).
Clark, G. (2009). These horses can fly! and other lessons from Second Life: The view from the virtual hacienda. In R. Oxford & J. Oxford (Eds.),
Second language teaching and learning in the Net Generation (pp. 153–172). Honolulu, HI: National Foreign Language Resource Center.
Cohen, D., Sevdalis, N., Taylor, D., Kerr, K., Heys, M., Willett, K., … Darzi, A. (2013). Emergency preparedness in the 21st century: Training and
preparation modules in virtual environments. Resuscitation, 84(1), 78–84. doi:10.1016/j.resuscitation.2012.05.014
Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York, NY: Harper Collins.
Dyer-Witheford, N., & de Peuter, G. (2009). Games of empire: Global capitalism and video games. Minneapolis, MN: University of Minnesota Press.
Gredler, M. E. (1996). Educational games and simulations: A technology in search of a (research) paradigm. In D. H. Jonassen (Ed.), Handbook of
research for educational communications and technology. New York, NY: Simon & Schuster Macmillan, pp. 521–540.
Guzdial, M. (1997). Components of software-realized scaffolding. http://www.cc.gatech.edu/gvu/edtech/SRS.html
Jonassen, D. H. (1999). Designing constructivist learning environments. In C. M. Reigeluth (Ed.), Instructional design theories and models: A new
paradigm of instructional theory (Vol. 2, pp. 215–239). Mahwah, NJ: Lawrence Erlbaum Associates.
Kelley, D. (1988). The art of reasoning. New York, NY: W. W. Norton.
Klopfer, E., & Squire K. (2008). Environmental detectives: The development of an augmented reality platform for environmental simulations. Education
Technology Research and Development, 56, 203–228.
Koster, R. (2004). A theory of fun for game design. Scottsdale, AZ: Paraglyph Press.
Mastrian, K. G., McGonigle, D., Mahan, W. L., & Bixler, B. (2011). Integrating technology in nursing education: Tools for the knowledge era. Sudbury,
MA: Jones & Bartlett Learning.
Mungai, D., Jones, D., & Wong, L. (2002, August). Games to teach by. Paper presented at the 18th Annual Conference on Distance Teaching and
Learning, Madison, WI.
Nemerow, L. G. (1996). Do classroom games improve motivation and learning? Teaching and Change, 3(4), 356–361.
New Media Consortium. (2007). Massively multiplayer educational gaming. The Horizon Report, 2007 Edition. Retrieved October 2010 from
http://www.nmc.org/horizonproject/2007/massively-multiplayer-educational-gaming
Prensky, M. (2001). Digital game-based learning. New York, NY: McGraw Hill.
Salen, K., & Zimmerman, E. (2003). Rules of play: Game design fundamentals. Cambridge, MA: MIT Press.
Skiba, D. (2008). Games for health. Nursing Education Perspectives, 29(4), 230–232. Retrieved from ProQuest Nursing & Allied Health Source
(Document ID: 1538660701).
Weatherford, J. (n.d.). Instructional simulations: An overview. http://www.etc.edu.cn/eet/Articles/instrucsimu/start.htm
Zyda, M. (2005). From visual simulation to virtual reality to games. Computer, 39(9), 25–32.
Section VI
Nursing Informatics: Research Applications
Chapter 23 Research: Data Collection, Processing, and Analysis
Chapter 24 Data Mining as a Research Tool
Chapter 25 Translational Research: Generating Evidence for Practice
Chapter 26 Bioinformatics, Biomedical Informatics, and Computational Biology
Nursing informatics (NI) provides more tools and capabilities than can at times be imagined. Just as NI has changed the way nursing is
administered and practiced, so it has also dramatically altered research practices.
Nursing research has evolved with technology. In the era of evidence-based practice, clinicians must continue to think critically
about their actions. What is the science behind interventions? Things must no longer be done a certain way just because they have
always been done that way. Instead, one should research the problem, use evidence-based resources, critically select electronic and
nonelectronic references, consolidate the research findings and combine and compare the conclusions, present the findings, and
propose a solution. The nurse may be the first to ask why, thereby becoming a key player in making change happen.
NI enhances and facilitates collaboration; improves access to online libraries; provides research tool transparency for collection,
analysis, and dissemination of research knowledge; and facilitates the development of a common data language. It provides
organizational and informational support to advance translational research, helping to fill the gap between research findings and
practice implementation. Repeat studies are needed to provide meaningful meta-analyses and systematic reviews of evidence to
advance practice. Technology advancement in the area of incorporating evidence into clinical tools must continue. Removing the
barriers to knowledge-seeking behavior and providing access to evidential resources promotes knowledge use and, in the end,
improves patient outcomes. In addition, NI provides support for powerful research techniques such as data mining and research
involving biological processes and genomics that hold the promise of discovering new knowledge to improve clinical practices.
The material in this book is placed within the context of the Foundation of Knowledge model (Figure VI-1). Nursing research is
conducted to generate knowledge that is used to meet the needs of healthcare delivery systems, organizations, patients, and nurses. In
relation to the model, the nurse researcher is involved with every aspect—from acquiring (collecting) and processing (analyzing) data
and information, to generating knowledge, to disseminating the results or findings (knowledge). Through this work, the researcher
generates knowledge for the nursing profession. Knowledge generation is extremely important in the advancement of nursing science.
Figure VI-1 Foundation of Knowledge Model
Source: Designed by Alicia Mastrian.
Chapter 23
Research: Data collection, Processing, and Analysis
Heather E. McKinney, Sylvia DeSantis, Kathleen Mastrian, and Dee McGonigle
OBJECTIVES
1. Describe nursing research in relation to the Foundation of Knowledge model.
2. Explore the acquisition of previous knowledge through Internet and library holdings.
3. Explore information fair use and copyright restrictions.
4. Assess informatics tools for collecting data and storage of information.
5. Compare tools for processing and analyzing quantitative and qualitative data.
Key Terms
American Library Association
Copyright
Cumulative Index to Nursing and Allied Health Literature
Educational Resources Information Center
Fair use
Foundation of Knowledge model
Information literacy
MEDLINE
Personal digital assistant
PsycInfo
Introduction: Nursing Research and the Foundation of Knowledge Model
The Foundation of Knowledge model suggests that the most important aspect of information discovery, retrieval, and delivery is the
ability to acquire, process, generate, and disseminate knowledge in ways that help those managing the knowledge reevaluate and
rethink the way they understand and use what they know and have learned. These goals closely reflect the Information Literacy
Competency Standards for Higher Education, published by the American Library Association (ALA) in 2003 in response to
changing perceptions of how information is created, evaluated, and used.
According to the ALA (2000), an information-literate individual is able to do the following:
Determine the extent of information needed
Access the needed information effectively and efficiently
Evaluate information and its sources critically
Incorporate selected information into one’s knowledge base
Use information effectively to accomplish a specific purpose
Understand the economic, legal, and social issues surrounding the use of information and access and use information ethically
and legally
In addition, new challenges arise for individuals seeking to understand and evaluate information because information is available
through multiple media (graphical, aural, and textual). The sheer quantity of information does not by itself create a more informed
citizenry without complementary abilities to use this information effectively. Most significantly, information literacy forms the basis
for lifelong learning, serving as a commonality among all learning environments, disciplines, and levels of education (Association of
College and Research Libraries, 2000).
CASE STUDY
During rounds, Charles encounters a rare condition he has never personally seen and only vaguely remembers hearing about in nursing school. He
takes a few moments to prepare himself by searching the Internet. That evening, he does even more research so that he can assess and treat the
patient safely. He searches clinical databases online and his own school textbooks. Most of the information seems consistent, yet some factors vary.
Charles wants to provide the highest quality of patient care and safety. He wonders which resources are best, which are the most trusted, and which
are the most accurate.
Knowledge Generation Through Nursing Research
Information literacy is an intellectual framework for finding, understanding, evaluating, and using information. These activities are
accomplished in part through fluency with information technology and sound investigative methods, but most importantly through
critical reasoning and discernment. The Association of College and Research Libraries (2000) has suggested that information literacy
initiates, sustains, and extends lifelong learning through abilities that may use technologies but are ultimately independent of them (p.
5).
Because nursing informatics combines all aspects of clinical practice, research, administration, and education (Ball, Hannah, &
Douglas, 2000), the ability to recognize the need for a specific kind of information and then locate, evaluate, and effectively use that
information (ALA, 1989) within the nursing informatics paradigm will catapult nurses ahead of other healthcare professionals in
applying and engaging various facets of technology. However, because so few nurses have formal training in technology but still
represent a disproportionate number of users, the ways in which nursing research integrates healthcare technology within nursing
informatics creates an unseen challenge (McHaney, 2007).
This potentially enormous impact on the future of health care and technology will determine the success of information-literate
nurses: those who have learned how to learn and who understand the intricacies of how knowledge is organized, retrieved, and used in
such a way that others can learn from them (ALA, 1989). Whether a nurse fills the role of administrator, manager, educator, or staff,
integrating technology has become a necessity for every nurse in today’s technologic marketplace (McHaney, 2007).
Acquiring Previously Gained Knowledge Through Internet and Library Holdings
In an environment characterized by rapid technological change coupled with an overwhelming proliferation of information sources,
nurses face an enormous number of options when choosing how and from where to acquire information for their academic studies,
clinical situations, and research. Because information is available through so many venues, libraries, special-interest organizations,
media, community resources, and the Internet in increasingly unfiltered formats, healthcare practitioners must inevitably question the
authenticity, validity, and reliability of information (Association of College and Research Libraries, 2000).
Often, the retrieval of reliable research and information may seem to be a daunting task in light of the seemingly ubiquitous
amount of information found on the Web. Focusing on specific information venues not only aids this search but also assists in
negotiating the endless maze of resources, allowing a nursing practitioner to find the best and most accurate information efficiently.
Professional Online Databases
Professional databases represent a source of online information that is generally invisible to all Internet users except those with
professional or academic affiliations, such as faculty, staff, and students. These databases, which range from specific to general, act as
collection points by aggregating information, such as abstracts and articles from many different journals; two such databases include
the Cumulative Index to Nursing and Allied Health Literature (CINAHL) and MEDLINE. CINAHL, for example, specifically
includes information from all aspects of allied health, nursing, alternative medicine, and community medicine. The MEDLINE
database contains more than 10 million records and is maintained and produced by the National Library of Medicine. Other databases,
such as PsycInfo from the American Psychological Association and the Educational Resources Information Center (ERIC)
database, may also benefit nursing. Many databases also offer full-text capabilities, meaning that entire articles are available online.
The articles and abstracts contained within these databases have already withstood the rigors of publication in professional journals
and, therefore, are considered viable and authentic peer-reviewed sources.
Libraries with subscriptions to databases often employ library professionals who are able to help patrons sift through the vast
amounts of available electronic information; using the expert research capabilities of a health science librarian at one’s local university
is the best way to learn how to conduct database searches that yield the most efficient and useful results. Also useful are websites that
provide tutorials on best searching practices specifically for medically oriented databases, such as the tutorials provided by EBSCO
support to search the CINAHL database (http://support.epnet.com/training/flash_videos/cinahl_basic/cinahl_basic.html and
http://support.epnet.com/training/flash_videos/cinahl_advanced/cinahl_advanced.html).
Search Engines
Search engines allow users to surf the Web and find information on nearly anything, although many researchers steer clear of search
engines because of the vast amounts of unsubstantiated information they are likely to uncover. Because no legitimacy needs to be
provided for any information that appears on the Web, an author can make claims, substantiated or not, and still use the Web as a
publishing venue. Despite the pitfalls associated with search engines in general, they can yield a bounty of useful information when
used with discretion.
Different search engines will produce different results when used for the same research. For example, one popular search engine
ranks its results by number of hits a page or site has received. Whereas the most popular research results are likely to be relevant, the
order in which results appear does not indicate quality or viability of the source.
Different Web address (domain) suffixes (.com, .edu, .org, .gov, and so forth) indicate who is responsible for creating the website.
Although an .edu site is hosted by an educational institution and for that reason may seem legitimate, consider that it could also belong
to a student stating personal opinion, gossip, or guesswork. In contrast, .gov sites are maintained by the government and nearly always
have professional contact information. Web hosts develop new domain suffixes constantly, so although looking at the suffix can be
useful, it should not be the sole deciding factor when choosing to trust information.
One should never blindly trust information found on a webpage. When possible, check the date of the most recent update (How old
is the page?), contact information (Is a bibliography or list of sources provided?), links to external sources (Do they seem relevant?),
and previous attained knowledge from other reputable sources (Is the information too unbelievable?).
Fees and information retrieval charges should be approached with skepticism. Private companies do offer information aggregation
services for a fee. In these cases, users pay a flat monthly fee for access to collections of articles in a particular field. What users
(especially those affiliated with an academic institution) may not realize is that they are likely to have free access to the same, if not
more complete, information through their institution’s library system.
Some legitimate databases and traditional newspapers that maintain a Web presence do provide access for a small fee, but just as
many others simply ask users to register to see articles for free. Many nursing students and professionals affiliated with a university
may find that their university library has already purchased access for the student body.
Electronic Library Catalogs
Nearly all higher education institutions have placed their library catalogs online. Although this is an obvious convenience for many
students, some nursing professionals unused to working completely online may be intimidated by an e-catalog. Library professionals at
the tiniest university and the busiest community college are available to demonstrate how to navigate a basic search of their library’s
catalog. Asking for assistance in learning how to access the vast assortment of journals, books, databases, and other resources available
at one’s college library is an excellent idea. Students in nursing programs at larger universities will likely find free classes that
specifically teach users how to navigate and use the online catalog. If smaller colleges and universities do not offer these services, one
should take advantage of the library’s online tutorials, help pages, frequently asked questions pages, and online reference service (if
available). Local public libraries often have subscriptions to popular databases and offer free classes on searching techniques to
patrons, providing yet another free access point to the best information for one’s research needs. Making full use of available library
resources serves to strengthen information literacy skills, enabling learners to master content and extend their investigations, become
more self-directed, and assume greater control over their own learning (Association of College and Research Libraries, 2000).
Fair Use of Information and Sharing
Copyright laws in the world of technology are notoriously misunderstood. The same copyright laws that cover physical books,
artwork, and other creative material apply in the digital world. Have you ever given a friend a CD that contains a computer game or
some other type of software that you paid for and registered? Have you ever downloaded a song from the Internet without paying for
it? Have you ever copied a section of online content from a reference site and used that content as if it was your own? Have you ever
copied a picture from the Internet without asking permission from the photographer who took the picture? Have you ever copied and
pasted information about a disease or drug from a website and then printed out the information to give to a patient or family member?
These are all examples of the type of copyright infringements enabled by technology that occur almost without thought.
The value of creative material—whether it is written content, a song, a painting, or some other type of creative work—lies not in
the physical medium on which it is stored, but rather in the intangibles of creativity, skills, and labor that went into creating that item.
The person who created the material should be properly credited and possibly reimbursed for the use of the material. How would
musicians be reimbursed for their music if everyone just downloaded their songs illegally from the Internet? Imagine that you created a
game to teach patients with type 1 diabetes how to manage their diet, and other nurses copied and distributed that game without getting
your permission to do so. How would you feel?
Almost all software, music CDs, and movie DVDs come with restrictions on how and why copies can be made. The license
included with the software clarifies exactly which restrictions are applicable. The most common type of software license is a “shrink
wrap” license, meaning as soon as the user removes the shrink wrap from the CD or DVD case, he or she has agreed to the license
restriction. Most computer software developers allow for a backup copy of the software to be made without restriction. If the hard
drive fails on the user’s computer, the software can usually be reinstalled through this backup copy. Some software companies even
allow the purchaser of a software package to transfer it to a new user. In this case, the software typically must be uninstalled from the
original owner’s computer before the new owner is free to install the software on his or her computer. Most of these restrictions
depend on the honesty of the user in reading and following the licensing agreement. As a result of widespread abuses, however, the
music and film industries commonly include hardware security features in their products that block users from making a working copy
of a music CD or movie DVD.
The bottom line: Copyright laws also apply to the digital world, and copyright violations can lead to prosecution. Advances in
technology have made the sharing of information easy and extremely fast. A scanner can convert any document to digital form
instantly, and that document can then be shared with people anywhere in the world. Nevertheless, the person who originally created
that document has the right to approve of the sharing of the work. Carefully read the fine print of any software purchased and be sure
to clarify any questions regarding how that software can be copied. Avoid downloading music illegally from the Internet, and do not
use information from the Internet without permission to do so or without citing the reference appropriately. Healthcare organizations
that allow access to the Internet from a network computer should ensure that users are well aware of and compliant with all copyright
and fair use principles.
Informatics Tools for Collecting Data and Storage of Information
Nurses are already intimately familiar with data collection as daily agents of patient care documentation, patient monitoring, and
interview data (Chang, 2001). In this way, formal nursing data sets are actually made up of gathered information, such as healthcare
definitions, classification, and nursing information. Before data can be analyzed or critically reviewed to determine outcomes or
assessment, they must be collected and aggregated. According to the Cleveland Clinic (2010):
[C]ollaborative nursing-led research is enhanced by the ability to support these projects with patient data that is more easily
extracted electronically (para. 12). Supporting these efforts and initiatives is a dedicated team of clinical and system analysts
who provide support for the development and management of information databases, systems and processes to bring efficiency
to nursing-driven quality and research endeavors through informatics (para. 13).
Nurses may generate and record data from their own observations or with the assistance of various devices. Free text
(informational data, such as drug dosages administered, resources used, and problems diagnosed) is recorded electronically. Free text
is then interpreted and organized by some standardizing principle, either manually or by computer. In this way, data (often qualitative
data that cannot be traditionally measured in a numeric sense) can be organized and processed. A central issue to the generation and
analysis of free text data is the lack of a generally accepted set of terminology to capture nursing data. Data actually become
information when these separate components are interpreted, organized, combined, and structured within a specific context to convey
particular meanings (Hovenga & Sermeus, 2002).
Database management systems consist of software designed to collect, sort, organize, store, retrieve, select, and aggregate data.
Nursing and health data may be classified into four basic types: (1) resource data (e.g., financial information); (2) patient and client
demographics; (3) activity data (clinical data); and (4) health service provider data. These primary data may be either recorded
manually or collected electronically, with manual collection providing a greater opportunity for error. When data are electronically
recorded, this process follows a programmed set of instructions built into the software, thereby cutting down substantially on
collection error. Of paramount importance in the collection process are the data collection form and the computer interface used for
inputting the data; these affect the completeness, consistency, and accuracy of the resulting data (Hovenga & Sermeus, 2002).
Quantitative data collection tools or instruments include questionnaires, interviews, surveys, quizzes, assessments, e-mail
interviews, and Web-based surveys, all of which generate numerical data rather than text-based data. Questionnaires—one of the most
popular means of data collection—can be administered in hard copy, on paper, or programmed into a website where individuals may
answer the questions electronically (Chang, 2001). Other electronic data collection tools include personal digital assistants and on-
site laptops. A key benefit of using electronic data collection is the ability to transmit data to another computer directly for compilation
and analysis, thereby cutting down on the risk of error (Hebda, Czar, & Mascara, 2005).
Of course, one must always be cognizant of the need to protect the privacy of participants by deidentifying data collected for
research and by having tools in place to provide secure transmission and storage of private information. Some researchers are finding
rich qualitative data on public and freely available patient support sites and blogs. An important issue associated with such Internet-
based data collection is whether participants actually are who they say they are, and whether they actually have the variable of interest
or are just pretending to be someone or something they are not. Remember that the same rules for protecting human subjects apply no
matter where the data are accessed or collected, and that all research involving human subjects should be formally approved by the
appropriate institutional review board (IRB). Many IRBs have specific policies in place that govern electronic data collection and
storage to ensure that the rights and privacy of research participants are protected (Office of the Vice President for Research at Penn
State, n.d.).
An excellent example of innovative electronic data collection is the system used by participants in the Nightingale Tracker System
pilot study, in which nursing students traveling to rural clinical sites submitted information via hand-held devices while miles away
from their preceptor-supervisors. The results of this study suggest that, despite some technical challenges associated with the hardware,
using the hand-held technology enhanced students’ learning (especially in the area of physical assessment), increased their confidence
in practicing in community-based settings, and provided efficient data input capabilities (Ndiwane, 2005).
Harder-to-measure, nonnumerical qualitative data can be collected electronically in the form of a narrative or diary-like entry.
Much in the way free text is analyzed and sorted, this narrative dialogue is assessed and then coded, looking for patterns and themes
that represent the phenomenon under study. For example, a nurse researcher may be interested in studying the lived experiences of
women recently diagnosed with breast cancer and, therefore, ask them to keep a diary of their thoughts, questions, and treatment
experiences.
Tools for Processing Data and Data Analysis
Data analysis is the process by which data collected during the course of a study are processed to identify trends and patterns of
relationships. Descriptive statistics allow the researcher to organize information meaningfully, thereby facilitating insight by
describing what the data show (Hebda et al., 2005). There exist a range of tools to facilitate such analysis, including specialized
databases, word processing/spreadsheet/database applications, and statistical packages (Hovenga & Sermeus, 2002).
Quantitative Data Analysis
Quantitative data focus on numbers and frequencies, with the goal of describing a situation or looking for more robust relationships
such as correlations and specific variable contributions to an outcome. This aim stands in contrast to qualitative analysis, which
focuses on experiences and meaning. Although the kind of data generated by quantitative collection is fairly straightforward and easy
to analyze (responses to questionnaires, experiments, and psychometric tests), quantitative data analysis has come under criticism.
Psychologists, for example, prefer to use a combination of quantitative and qualitative data, backing up research participants’
explanations with statistically reliable information obtained by numerical measurement (Quantitative and Qualitative Data, n.d.).
In quantitative studies, variables represented by data are collected in numerical form. These values are then entered into specific
fields that have predetermined meanings or are coded. Various quantitative data analyses can be applied to nursing research, such as
intervention research, quality improvement studies, and outcomes research. One of the most popular statistical packages for this kind
of analysis is the Statistical Package for Social Sciences (SPSS). Depending on the research goal, the researcher may use different
types of analysis. Different statistical goals may require hypothesis testing, model building, descriptive and exploratory analyses, and
others. For example, hypothesis testing is based on assumptions regarding the relative truth of the hypothesis, so a data analysis would
compare actual outcomes with purported hypotheses (Chang, 2001).
Qualitative Data Analysis
Extremely varied in nature, qualitative data can include nearly any information that can be captured and is not numerical (Trochim,
2006a). Qualitative data are more concerned with describing meaning than with drawing statistical inferences; what is lost in reliability
(faulty transcription, forgotten details, and so forth) is gained in validity (Quantitative and Qualitative Data, n.d.). Although qualitative
data rely on judgments, they can still be manipulated numerically, much in the same way that quantitative data can be open to
judgment (Trochim, 2006b).
Some major types of qualitative data include in-depth interviews, direct observation, and written documents. Interviews include
individual and focus group interviews and may be recorded in some way. Interviews differ from direct observation in their interactive
nature. Direct observation differs from case to case and often means the researcher does not make contact with the respondent. Written
documents might include a variety of written materials, including memos, newspaper clippings, conversation transcripts, and books
(Trochim, 2006a).
Computers can aid greatly in the storage, tabulation, and retrieval of qualitative data by acting as the equivalent of an electronic
filing cabinet (Hebda et al., 2005). Data analysis can also be aided by simple data management programs, such as Excel, Access, or
NVivo, in which a user can categorize data and link categories with key words (Chang, 2001). Data can be converted into information
and knowledge by either inductive or deductive reasoning. Most qualitative methods use an inductive approach in which the researcher
generates hypotheses (versus the deductive approach typical of quantitative studies, in which hypotheses are tested). Data analysis in
quantitative studies may allow the researcher to make inferences to a population beyond the sample, as long as the sample was
representative of the population. In contrast, generalizing to a larger population is not a goal of qualitative research. Rather, in
qualitative research the goals are exploration and deeper understanding a phenomenon that has not been widely studied.
Two relatively new approaches to quantitative research are cohort research and case control research. Cohort research is a type of
study in which two groups of people are identified, one with an exposure of interest and another without the exposure. The two groups
are followed forward to determine whether the outcome of interest occurs. Groups are defined based on whether they have had an
exposure to a particular risk factor. Case control research, in contrast, is a type of study in which patients who have an outcome of
interest and patients who do not have the outcome are identified; the researcher then looks back in time (typically using health records)
to determine exposures and experiences that could have contributed to the outcome occurring or not occurring (Brown, 2014).
The Future
The future of nursing informatics is growing as fast as technology itself. The more nurses participate in the development process of
healthcare technology, the more efficient and effective nursing informatics may become. Nurses are urged to take an active role in the
profession by providing real-world feedback during the design process and after implementation. Such practical insights will provide
valuable data for technology evaluation and advancement in the field of nursing informatics.
Summary
This chapter discussed the value of information literacy as an essential research tool and its relationship to knowledge generation and
lifelong learning. The reader is now acquainted with informatics tools useful for acquiring new knowledge and assessing previous
knowledge and tools useful for collecting and storing and analyzing information to generate knowledge. In an ideal world, information
literacy and informatics tools will be used as a critical skill set for increasing healthcare efficiency, effectiveness, and safety in the 21st
century.
THOUGHT-PROVOKING
QUESTIONS
1. How will the advent of information literacy affect nursing informatics in the 21st century?
2. Reflect on copyright law and why it is needed. Suppose you determine that photographs or other images can be replicated based on your
assessment of fair use, but your administrative assistant refuses to photocopy them because he feels that it is copyright infringement and against
company policy. Describe in detail how you would handle this situation.
References
American Library Association (ALA). (1989). Presidential committee on information literacy: Final report.
http://www.ala.org/acrl/publications/whitepapers/presidential
American Library Association (ALA). (2000). Information literacy competency standards for higher education. Chicago, IL: Author.
Association of College and Research Libraries. (2000). Information literacy competency standards for higher education.
http://www.ala.org/acrl/standards/informationliteracycompetency
Ball, M. J., Hannah, K. J., & Douglas, J. V. (2000). Nursing and informatics. In M. J. Ball, K. J. Hannah, S. K. Newbold, & J. V. Douglas (Eds.),
Nursing informatics: Where caring and technology meet (pp. 6–14). New York, NY: Springer.
Brown, S. (2014). Evidence-based nursing: The research–practice connection (3rd ed.). Burlington, MA: Jones & Bartlett Learning.
Chang, B. L. (2001). Computer use in nursing education. In V. Saba & K. McCormick (Eds.), Essentials of computers for nurses: Informatics for the
new millennium (3rd ed., pp. 445–456). New York, NY: McGraw-Hill.
Cleveland Clinic. (2010). Nursing informatics. http://my.clevelandclinic.org/nursing/informatics.aspx
Hebda, T., Czar, P., & Mascara, C. (2005). Handbook of informatics for nurses & health care professionals (3rd ed.). Upper Saddle River, NJ: Prentice
Hall.
Hovenga, E. J. S., & Sermeus, W. (2002). Data analysis methods. In J. Mantas & A. Hasman (Eds.), Textbook in health informatics: A nursing
perspective (pp. 113–125). Amsterdam, Netherlands: IOS Press.
McHaney, D. F. (2007, June–August). Embracing the integration of technology and care. Alabama Nurse, 34(2), 1.
Ndiwane, A. (2005). Teaching with the Nightingale Tracker technology in community-based nursing education: A pilot study. Journal of Nursing
Education, 44(1), 40–42.
Office of the Vice President for Research at Penn State. (n.d.) IRB guideline X: Guidelines for computer- and Internet-based research involving human
participants. http://www.research.psu.edu/policies/research-protections/irb/irb-guideline-10
Quantitative and qualitative data. (n.d.). http://www.holah.karoo.net/quantitativequalitative.htm
Trochim, W. M. K. (2006a). Qualitative data. http://www.socialresearchmethods.net/kb/qualdata.php
Trochim, W. M. K. (2006b). Types of data. http://www.socialresearchmethods.net/kb/datatype.php
Chapter 24
Data Mining as a Research Tool
Dee McGonigle and Kathleen Mastrian
OBJECTIVES
1. Describe big data.
2. Assess knowledge discovery in data.
3. Explore data mining.
4. Compare data mining models.
Key Terms
Algorithm
Bagging
Big data
Boosting
Brushing
Classification
Classification and regression trees (CART)
Data mining
Data set
Decision tree
DMAIC (define, measure, analyze, improve, control)
Drill down
Exploratory data analysis (EDA)
Knowledge discovery and data mining (KDD)
Knowledge discovery in data
Knowledge discovery in databases
Length of stay (LOS)
Machine learning
Meta-learning
Modeling
Multidimensional database
Neural network
Online analytic processing (OLAP)
Return on investment (ROI)
Scoring
SEMMA (sample, explore, modify, model, assess)
Six Sigma
Stacking Unstructured data
Introduction: Big Data, Data Mining, and Knowledge Discovery
Data mining methods have been developed over time using research. As data mining evolves, we have not only become able to
navigate our data in real time, but have also progressed beyond mere access to retrospective data with navigational improvements.
Using data warehousing and decision support, we could, for example, answer the question, “What was the most commonly diagnosed
disease in our nine-hospital system last April?” We could then drill down to one hospital. As we have developed big data collection
capabilities, data capture, data transmission, storage capabilities, powerful computers, statistics, artificial intelligence, high-functioning
relational database engines with data integration, and advanced algorithms, we have realized the ability to data mine our big data to
predict and deliver prospective and proactive information. We can begin to predict by answering a question such as “What is likely to
be the most commonly diagnosed disease next month and why?” Data mining includes tools for visualizing relationships in the data
and mechanizes the process of discovering predictive information in massive databases.
Pattern discovery entails much more than simply retrieving data to answer an end user’s query. Data mining tools scan databases
and identify previously hidden patterns. The predictive, proactive information resulting from data mining analytics then assists with
development of business intelligence, especially in relation to how we can improve. Much of our big data is unstructured;
unstructured big data reside in text files, which represent more than 75% of an organization’s data. Such data are not contained in
databases and can be easily overlooked; moreover, it is difficult to discern trends and patterns in such data. Data mining is an iterative
process that explores and models big data to identify patterns and provide meaningful insights.
As we evolve the tools with which we can collect, access, and process data and information, it is necessary to concomitantly evolve
how we analyze and interpret the data and information. IBM (2013) describes big data in a way that is easy to understand:
Every day, we create 2.5 quintillion bytes of data—so much that 90% of the data in the world today has been created in the last
two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites,
digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. This data is big data. (p.
1)
According to Tishgart (2012), “More data means more knowledge, greater insights, smarter ideas and expanded opportunities for
organizations to harness and learn from their data” (para. 2). Data mining is the process of using software to sort through data to
discover patterns and ascertain or establish relationships. This process may help to discover or uncover previously unidentified
relationships among the data in a database with a focus on applications. This information can then be used to increase profits or
decrease costs, or a combination of the two. In health care, it is being used to improve efficiency and quality, resulting in better
healthcare practices and improved patient outcomes.
Data mining projects help organizations discover interesting knowledge. These projects can be predictive, exploratory, or focused
on data reduction. Data mining focuses on producing a solution that generates useful forecasting through a four-phase process: (1)
problem identification, (2) exploration of the data, (3) pattern discovery, and (4) knowledge deployment, or application of knowledge
to new data to forecast or generate predictions. Data mining is an analytic, logical process with the ultimate goal of forecasting or
prediction. It mines or unearths concealed predictive information, constructing a picture or view of the data that lends insight into
future trends, actions, or behaviors. This data exploration and resulting knowledge discovery foster proactive, knowledge-driven
decision making.
Problem identification is the initial phase of data mining. The problem must be defined, and everyone involved must understand
the objectives and requirements of the data mining process they are initiating.
Exploration begins with exploring and preparing the data for the data mining process. This phase might include data access,
cleansing, sampling, and transformation; based on the problem you are trying to solve, data might need to be transformed into another
format. To assure meaningful data mining outcomes, you must comprehend and truly understand your data. The goal of this phase is to
identify the relevant or important variables and determine their nature.
Sometimes known as model building or pattern identification, pattern discovery is a complex phase of data mining. In this phase,
different models are applied to the same data to choose the best model for the data set being analyzed. It is imperative that the model
chosen should identify the patterns in the data that will support the best predictions. The model must be tested, evaluated, and
interpreted. Therefore, this phase ends with a highly predictive, consistent pattern-identifying model.
The final phase, knowledge deployment, takes the pattern and model identified in the pattern discovery phase and applies them to
new data to test whether they can achieve the desired outcome. In this phase, the model achieves insight by following the rules of a
decision tree to generate predictions or estimates of the expected outcome. This deployment provides the organization with the
actionable information and knowledge necessary to make strategic decisions in uncertain situations.
Data mining develops a model that uses an algorithm to act on a data set for one situation where the organization knows the
outcome and then applies this same model to another situation where the outcome is not known—an extension known as scoring. Data
mining is concerned with extracting what is needed, and it applies statistics so that organizations can gain an advantage by
manipulating information for practical applications. In our information-overloaded healthcare world, all too often we find ourselves
grasping for knowledge that is currently nonexistent or fleeting. Data mining is a dynamic, iterative process that is adjusted as new
information surfaces. It is a robust, predictive, proactive information and knowledge tool that, when used correctly, empowers
organizations to predict and react to specific characteristics of and behaviors within their systems.
Data mining is also known as knowledge discovery and data mining (KDD), knowledge discovery in data, and knowledge
discovery in databases. The term “knowledge discovery” is key, as data mining looks at data from different vantage points, aspects,
and perspectives and brings new insights to the data set. This analysis then sorts and categorizes the data and finally summarizes the
relationships identified. In essence, then, data mining is the process of finding correlations or patterns among the data.
Health care, as noted earlier, generates big data. Data mining tools, in turn, are able to analyze enormous databases to determine
patterns and establish applications to new data. Healthcare organizations clearly need to invest more in big data and data mining
analysis: The “big data market [reached an estimated] $2.2 billion in 2011, [but] only 6% of that investment came from health care”
(Tishgart, 2012, para. 2).
The healthcare sector has discovered data mining through the realization that knowledge discovery can help to improve healthcare
policy making, healthcare practices, disease prevention, detection of disease outbreaks, prevention of sequelae, and prevention of in-
hospital deaths. On the business side, healthcare organizations use data mining for the detection of falsified or fraudulent insurance
claims. According to Manyika et al. (2011):
If US healthcare were to use big data creatively and effectively to drive efficiency and quality, the sector could create more
than $300 billion in value every year. Two-thirds of that would be in the form of reducing US healthcare expenditure by about
8 percent. (para. 2)
If they are to develop a successful data mining process, organizations must have the data needed to create meaningful information.
In health care, we are honing our ability to analyze our data by making sure that those data are comprehensive and complete, meet our
needs, and are cleansed and prepared for the data mining process. Many facilities are using data warehouses to store data and facilitate
this pre–data mining process. We are learning to ask the right questions during the data mining process to gain a thorough
understanding of our data. The following pages introduce the concepts, techniques, and models used in data mining.
KDD and Research
According to IBM (2013), big data does not just refer to size, but rather “is an opportunity to find insights in new and emerging types
of data and content, to make your business more agile, and to answer questions that were previously considered beyond your reach” (p.
6). We now have advanced analytical software designed to facilitate data mining. The knowledge discovery capabilities continue to
evolve. Goodwin et al. (1997), for example, reported on their collaborative data mining research, which explored the relationship
between clinical data and adult respiratory distress syndrome (ARDS) in critically ill patients. DeGruy (2000) indicated that big data in
healthcare needed to be analyzed using KDD tools and applications to determine trends and relationships with the ultimate goal of
decreasing healthcare costs while improving quality. Berger and Berger (2004) suggested that nurse researchers are positioned to use
data mining technologies to transform the repositories of big data into comprehensible knowledge that will be useful for guiding
nursing practice and facilitating interdisciplinary research.
Madigan and Curet (2006) described the classification and regression trees (CART) data mining method for analyzing the
outcomes and service use in home health care for three conditions: chronic obstructive pulmonary disease, heart failure, and hip
replacement. They found that four factors—patient age, type of agency, type of payment, and ethnicity—influenced discharge
destination and length of stay.
Over the last several decades, the KDD capability has improved as the analytical power of data mining tools has increased, thereby
facilitating the recognition of patterns and relationships in big data. Trangenstein, Weiner, Gordon, and McNew (2007) described how
their faculty used data mining to analyze their students’ clinical logs, which enabled them to make programmatic decisions and
revisions and rethink certain clinical placements. Zupan and Demsar (2008) described the open source tools developed by data mining
researchers that they felt were ready to be used in biomedical research. Fernández-Llatas et al. (2011) described workflow mining
technology as a means to facilitate relearning in dementia processes. Lee et al. (2011) discussed the application of data mining to
identify critical factors such as nursing interventions related to patient fall outcomes. Lee, Lin, Mills, and Kuo (2012) used data mining
to determine risk factors related to each stage of pressure ulcers and identified five predictive factors: hemoglobin level, weight, sex,
height, and use of a repositioning sheet. Based on the results of this data mining analysis, nurses can better target their interventions to
prevent pressure ulcers. Green et al. (2013) identified differences in limb volume patterns in breast cancer survivors; their results have
the potential to influence clinical guidelines for the assessment of latent and early-onset lymphedema.
CASE STUDY
In a large teaching hospital, there is a high rate of readmission for patients with congestive heart failure (CHF) who are being treated at the facility.
The chief nursing officer (CNO) wants to know the cause of the readmissions, as the rate at this facility is almost twice that of competing healthcare
entities in the area. The CNO works with the nursing researchers at the university to address this situation.
The researchers begin to scour the electronic health records (EHRs) of more than 15,000 CHF hospitalizations in the past 4
years to determine the cause of the situation. As they begin to understand this data set, they are able to build a data mining model
using algorithms to discover patterns and relationships in the data. Based on the old data, they determine that the key factor for
readmission was the length of time it took to follow up at home with discharged patients.
In response to this new knowledge, a program in which nurses contact patients with CHF the day after their discharge by phone
was developed, and a home visit is scheduled for the second day post discharge to ensure a smooth transition to home or an
assistedliving facility. This follow-up within the first 4 days of discharge has reduced readmissions by 40%. The model that was
used with the old data is being applied to the new data.
As these examples suggest, we must continue to employ data mining to reduce inefficiencies, improve quality, and support
transformations using data-driven models of care.
Data Mining Concepts
Bagging is a term for the use of voting and averaging in predictive data mining to synthesize the predictions from many models or
methods or the use of the same type of a model on different data. This term can also refer to the unpredictability of results when
complex models are used to mine small data sets.
Boosting is what the term infers—a means of increasing the power of the models generated by weighting the combinations of
predictions from those models into a predicted classification. This iterative process uses voting or averaging to combine the different
classifiers.
Data reduction shrinks large data sets into manageable, smaller data sets. One way to accomplish this is via aggregation of the data
or clustering.
Drill down analysis typically begins by identifying variables of interest to drill down into the data. You could identify a diagnosis
and drill down, for example, to determine the ages of those diagnosed or the number of males. You could then continue to drill down
and expose even more of the data.
Exploratory data analysis (EDA) is an approach or philosophy that uses mainly graphical techniques to gain insight into a data
set. Its goal varies based on the purpose of the analysis, but EDA can be applied to the data set to extract variables, detect outliers, or
identify patterns.
Feature selection reduces inputs to a manageable size for processing and analysis, as the model either chooses or rejects an
attribute based on its usefulness for analysis.
Machine learning is a subset of artificial intelligence that permits computers to learn either inductively or deductively. Inductive
machine learning is the process of reasoning and making generalizations or extracting patterns and rules from huge data sets—that is,
reasoning from a large number of examples to a general rule. Deductive machine learning moves from premises that are assumed true
to conclusions that must be true if the premises are true.
Meta-learning combines the predictions from several models. It is helpful when several different models are used in the same
project. The predictions from the different classifiers or models can be included as input into the meta-learning. The goal is to
synthesize these predicted classifications to generate a final best predicted classification—a process also referred to as stacking.
Predictive data mining identifies the data mining project as one with the goal of identifying a model that can predict classifications.
Stacking or stacked generalization synthesizes the predictions from several models.
Data Mining Techniques
It is important to understand your data before you begin the data mining process so that you can choose the best technique and get the
most from the data mining. The commonly used techniques in data mining are neural networks, decision trees, rule induction,
algorithms, and the nearest neighbor method.
Neural networks represent nonlinear predictive models. These models learn through training and resemble the structure of
biological neural networks; that is, they model the neural behavior of the human brain. Computers are fast and can respond to
instructions or programs over and over again. Humans use their experience to generalize the world around them. Neural networks are a
way to bridge the gap between computers and humans. Neural networks go through a learning process or training on existing data so
that they can predict, recognize patterns, associate data, or classify data.
Decision trees are so named because the sets of decisions form a tree-shaped structure. The decisions generate rules for classifying
a data set. Classification and regression trees (CART) and chi square automatic interaction detection (CHAID) are two commonly used
types of decision tree methodologies. See Box 24-1 and (Figure 24-1) for an overview of decision tree analysis.
BOX 24-1 DECISION TREE ANALYSIS
Steven L. Brewer, Jr.
Decision trees are a statistical technique based on numerous algorithms to predict a dependent variable. These predictions are determined by the
influence of independent variables. Decision trees help researchers understand the complex interactions between variables generated from research
data. The entire data set is split into child nodes based on the impact of the independent predictors. The most influential variable is situated at the top
of the tree; the subsequent nodes are ranked by the significance of the remaining independent predictors.
Graphically, decision trees produce a tree (as illustrated in Figure 24-1) that consists of a root node and child nodes. The tree is an inverted,
connected graphic. The graphic representation of the decision tree helps general users, such as practitioners, understand the complex
interrelationships between the independent and dependent variables in a large data set.
Within the graphical display, there are three major components: the root node, the child node, and the terminal node. The root node represents the
dependent variable, and the child nodes represent the independent variables. The root node is essentially the base of the tree or the top node. It
contains the entire sample, while each child node contains a subset of the sample within the node directly above it. In Figure 24-1, the root node
represents a sample of women who were either reassaulted or not reassaulted in a domestic violence database. Data in the root node are then
partitioned and passed down the tree.
The number of child nodes will vary depending on the classification procedure that is used to determine how the data are split. In Figure 24-1,
the first child node is “women’s perception of safety.” This node suggests that the most influential predictor in domestic violence reassault for this
sample is the women’s perception of safety. The tree produces additional child nodes based on the responses to that variable. Node 1 represents the
women who responded “no” when asked whether they felt safe in the relationship. For this node, women had a 90% chance of being reassaulted. In
contrast, node 2 represents the women who responded “yes” when asked whether they felt safe in the relationship. These women had only a 72%
chance of being reassaulted.
The decision tree in (Figure 24-1) splits the entire sample into subsamples, which in turn allows for different predictions for different groups
within the sample. For example, women who felt safe in their relationship (node 2) and experienced controlling behaviors (node 6) had an 80%
chance of being reassaulted. In contrast, women who did not feel safe (node 1) and were in a relationship characterized by controlling behaviors
(node 6) had a 97.3% chance of being reassaulted. The splitting of the data continues until the data are no longer sufficient to predict the remaining
variables or there are no additional cases to be split.
Classification trees share commonalities with nonlinear traditional methods such as discriminant analysis, cluster analysis, nonparametric
statistics, and nonlinear estimation; however, the technique differs significantly from linear analyses. In general, the decision tree technique does not
rely on “multiplicative” or “additive” assumptions such as regression to predict the outcome of y. The flexibility of classification trees is one
characteristic that makes them attractive to researchers. The trees are not bound or limited to examining all predictor variables simultaneously.
Therefore, each predictor variable can be examined as a singularity to produce univariate splits in the tree. Additionally, classification trees can
handle a mixture of categorical and continuous variables when univariate splits are used. While this flexibility offers advantages over traditional
methods, classification trees are not limited to univariate splits.
Decision trees have become a popular alternative to methods such as regression and discriminate analysis for data mining big data. Such trees use
algorithms from one of the numerous classification procedures to separate the data into different branches or child nodes that predict y. The
dependent variable (y) is represented by the root node.
These algorithms have three main functions: (1) they explain how to separate or partition the data at each split, (2) they decree
when to stop or end the splitting of data, and (3) they determine how to predict the value of y for each x in a split. The child nodes
are separated into homogenous groups by the algorithms from different classification procedures. This process of partitioning is the
main purpose of classification procedures. First, the procedure clusters and creates child nodes. Second, it ranks them based on their
predictive values of y. Hence, the most influential variable(s) will be located at the base of the tree. From this point, the
classification procedures further expand the child nodes by finding the next best factor. The tree is expanded until the algorithm is
unable to find a clearly distinguishable split within the data.
Based on the results of the sample decision tree analysis presented in Figure 24-1, practitioners would conclude that women
should be attuned to their perception of safety in a relationship, as those who feel unsafe have a much higher chance of another
assault.
Decision trees are a powerful tool that can be used to mine large data sets and discover previously unknown relationships
among the data. The predictive relationships uncovered by the decision tree analysis may be useful in directing approaches to future
care interventions.
Rule induction is based on statistical significance. Rules are extracted from the data using if-then statements, which become the
rules.
Algorithms are typically computer-based recipes or methods with which data mining models are developed. To create the model,
the data set is first analyzed by the algorithm, looking for specific patterns and trends. Based on the results of this analysis, the
algorithm defines the parameters of the data mining model. The identified parameters are then applied to the entire data set to mine it
for patterns and statistics.
Nearest neighbor analysis classifies each record in a data set based on a select number of its nearest neighbors. This technique is
sometimes known as the k-nearest neighbor.
Text mining for text is equivalent to data mining for numerical data. Because text is not always consistent in health care owing to
the lack of a generally accepted terminology structure, it is more difficult to analyze. Text documents are analyzed by extracting key
words or phrases.
Online analytic processing (OLAP) generates different views of the data in multidimensional databases. These perspectives
range from simplistic views such as descriptive statistics, frequency tables, or comparative summaries to more complicated analyses
requiring various forms of cleansing the data such as removing outliers. OLAP is also known as fast analysis of shared data.
Brushing is a technique in which the user manually chooses specific data points or observations or subsets of data on an
interactive data display. These selected data can be visualized in two-dimensional or three-dimensional surfaces as scatterplots.
Brushing is also known as graphical exploratory data analysis.
Figure 24-1 Decision Tree Analysis Output
Data Mining Models
To generate predictions and infer relationships, the data set, statistics, and patterns identified in existing data must be applied to new
data. A data mining model is developed by exercising more than algorithms on data. Specifically, the data mining model consists of a
mining structure plus an algorithm. The data mining model remains empty until it applies the algorithm or processes and analyzes the
data provided by the mining structure. This model stores the information obtained from a statistical analysis of the data, identifying
patterns and gaining insights. It then contains metadata that specify the name and definition of the model, the server location or other
place where it is stored, definitions of any filters applied when processing the model, columns from the mining structure that were used
to build the model, and the algorithm used to analyze the data. The columns, their data types, any filters, and the algorithm used are all
choices that are made in the data mining process, and each of these decisions can greatly influence the data mining results. The same
data can be used to create many models; one type of model could organize the data into trees, for example, whereas another type of
model might cluster the data in groups based on the rules applied. Different results can be achieved from the same mining structure,
even though it is used in many models, based on the filtering method or analysis conducted. Therefore, each decision made along the
way is very important.
There are many models. In this chapter, we will review the following models: Cross-Industry Standard Process for Data Mining
(CRISP-DM), Six Sigma, and SEMMA (Sample, Explore, Modify, Model, Access).
CRISP-DM
The CRISP-DM model follows a path or series of steps to develop a business understanding by gaining an understanding of the
business data collected and analyzed. The six steps are business understanding, data understanding, data preparation, modeling,
evaluation, and deployment.
The CRISP-DM model begins with an understanding of the business. The situation must be assessed to establish the data mining
goals and produce the project plan. You must be able to answer the following questions: What are the business objectives? What are
the requirements? Have we specifically defined the problem? The answers to these questions help transform the business perspective
knowledge into a data mining problem definition and initial plan to meet the objectives.
Data understanding begins with the preliminary data collection and assimilation of the data. It is during this step that the data will
be described and explored to facilitate the user’s comprehension of the data. As the user gains familiarity with the data, data quality
issues are identified and the quality of the data is verified.
The data are cleansed and transformed during the data preparation step. First, one must select the data, attributes, and records to be
used. These data are then cleansed, constructed, integrated, and properly formatted. At this point, the final data set is constructed from
the data; this data set will be processed by the model.
Modeling involves selection of the modeling methods and their application to the prepared data set. Parameters are calibrated, a
test design is generated, and the model is built and assessed. This step might require you to revisit data preparation if the format of the
data does not meet the specific requirements of the methods.
During the evaluation step, the degree to which the objectives were met is assessed from a business perspective. A key question is,
Were any important business issues not considered? The model was built for high-quality data analysis. To see whether this goal has
been met, the process is reviewed, results evaluated, and the model interpreted. This is where you determine whether the model should
be implemented or whether more iterations must occur before its deployment. The project may not be completed, or a new data mining
project might be initiated. If the project is deployed, this step is when you must decide how the results from the data analysis will be
used.
Deployment is the final step, in which the model is finally implemented. The plan must be monitored and maintained and the
project reviewed. The six-step process should yield a reliable, repeatable data mining process. The knowledgeable insights gained from
the implementation of the model must be organized and presented in such a way that they can be used. The final project report is
generated to document the process and share this enhanced knowledge of the data.
The CRISP-DM model employs a process that has been proven to make data mining projects both more rapid and more effectual.
Using this model helps to avoid typical mistakes while assessing business problems and detailing data mining techniques.
Six Sigma
Six Sigma is a data-driven method to eliminate defects, avoid waste, or assess quality control issues. It aims to decrease discrepancies
in business and manufacturing processes through dedicated improvements. Six Sigma uses the DMAIC steps: define, measure,
analyze, improve, and control.
The first step defines the goals of the project or the goals for improvement. During this step, you can use data mining techniques to
discover prospective ideas for implementing the project.
In the measure step, exploratory and descriptive data analyses are used on the existing system to enhance the understanding of the
data. Reliable, valid, and accurate metrics with which to measure goal achievement in each of the steps are identified.
The analysis step should assess the system to identify discrepancies between the current big data and the goal. Statistical methods
guide this analysis.
Improvements must be made to the current system to attain the organizational goals. The use of project management skills
facilitates the application of the new methodology and processes. Statistical methods assess the improvements and any deficiencies
that exist.
The final step of the model is control. Controlling the system is important so discrepancies are remedied before they cause a
disruption.
The Six Sigma model applies a different mentality to the same old business model or way of thinking. The DMAIC steps that are
implemented typically result in success.
SEMMA
According to SAS (n.d.), “The acronym SEMMA—sample, explore, modify, model, assess—refers to the core process of conducting
data mining” (para. 1). This model is similar to Six Sigma but concentrates more on the technical activities characteristically involved
in data mining.
The first step is to sample the data. Sampling is optional but creates a more robust data mining effort. Using “[a] statistically
representative sample of your data, SEMMA makes it easy to apply exploratory statistical and visualisation techniques, select and
transform the most significant predictive variables, model the variables to predict outcomes, and confirm a model’s accuracy” (SAS,
n.d., para. 1). Creating a target data set shrinks the data to a manageable size, yet maintains the important information necessary to
mine.
The exploration of the data seeks to discover discrepancies, trends, and relationships in the data. It is at this point that ideas about
the data should emerge to help the organization understand the data and its implications for the organization’s business. In health care,
for example, it would be important to determine how many people use the emergency department each year and how many of those
people are admitted, released, and return, and to identify any disparities in care and diagnoses. What did you discover? What are the
trends and relationships that emerge from the data mining?
After exploring, you should modify your data based on the information discovered. It might be important to modify the data based
on groupings such as “all people who are diagnosed with congestive heart failure who present with shortness of breath” if the trending
and relationships indicate that this subgroup is significant. Other variables can also be introduced at this time to help gain a further
understanding of the data.
The data are modeled to predict outcomes based on analytically seaching the data. Combinations of data must be identified to
predict desired outcomes.
During the assessment phase, the data as well as the models are assessed for not only the reliability of the discoveries, but also the
usefulness of the data mining process. Assessment is key to determine the success of the data mining approach.
SEMMA focuses on the tasks of modeling. This approach has been praised for its ability to guide the implementation of data
mining. Conversely, it has been criticized for omitting the critical features of the organization’s business. SEMMA is logical and can
be robust from sampling through assessment.
Based on the needs of the organization, a variety of models can be used in combination. A coordinated, cooperative environment is
necessary for complicated data mining projects, because they require organizational commitment to ensure their success. As described
in this section, models such as CRISP-DM, Six Sigma, and SEEMA have been designed as blueprints to deal with the dilemma of how
to integrate data mining techniques into an organization. They facilitate the gathering of data, the analysis of those data, the conversion
of the data into information, and the dissemination of this information in a format that is easy to digest and understand so as to inform
organizational decision making. It is imperative that the results of the data mining process be implemented and any resultant
improvements monitored and evaluated.
Benefits of KDD
KDD can enhance the business aspects of healthcare delivery and help to improve patient care. Some examples of how KDD can be
applied effectively follow:
A durable medical equipment company analyzed its recent sales and enhanced its targeting of hospitals and clinics that yielded
the highest return on investment (ROI).
Several plastic surgery suites were bought by the same group of surgeons. They wanted to know how those organizations were
the same and how they were different. They ran analytics for disparities while looking for patterns and trends that led them to
develop standardized policies and modify treatment plans.
Analytic techniques were used in the clinical trials of a new oral contraceptive to aid in monitoring trends and disparities.
Hidden patterns and relationships between death and disease in selected populations can be uncovered.
Government spending on certain aspects of health care or specific disease conditions can be analyzed to discover patterns and
relationships and to distinguish between the real versus desired outcomes from the investment.
Patient data can be analyzed to identify effective treatments and discover patterns or relationships in the data to predict inpatient
length of stay (LOS).
Data can be analyzed to help detect medical insurance fraud.
Even though KDD can be complex, this process tends to yield a potent knowledge representation. As analytics evolve, KDD will
almost certainly become easier to use, more efficient, and more effective in facilitating data mining in health care.
Ethics of Data Mining
Data mining in health care is dependent on the use of private health information (PHI). Practitioners engaging in data mining must
ensure that such data are deidentified and that confidentiality is maintained. Because most data mining depends on the aggregation of
data, maintaining individual patient confidentiality should be relatively straightforward. You can follow changes and specific
requirements for compliance with HIPAA at this website:
http://www.hhs.gov/ocr/privacy/hipaa/understanding/special/research/index.html.
Summary
Big data is everywhere—we collect and store data every second of every day. The data in big clinical data sets can get lost, however,
diminishing its value. Therefore, in health care, it is imperative that we use KDD to analyze these data sets and discover meaningful
information that will influence our practice. Our existing data repositories are ripe for the picking; they contain hidden patterns, trends,
and undiscovered nuggets that we must mine to continue to hone our understanding and improve health care.
Data management is essential so that this process can begin with clean, good data. The decisions that are made when conducting
the analysis and when developing the model and algorithms enable us to predict and discover patterns and trends in the data, thereby
making them meaningful. We must be able not only to extract meaningful information and knowledge, but also to share and
disseminate what we are learning and the new knowledge we are generating.
THOUGHT-PROVOKING
QUESTIONS
1. Reflect on these terms: database, data warehouse, and data mining. What do they have in common? How do they differ?
2. Describe an issue associated with healthcare data that impedes the construction of meaningful databases and inhibits the data mining process.
Which strategies would you use to remedy this situation? Thoroughly describe one strategy and its potential outcomes.
3. Suggest a data mining project for your practice. Which information would you like to have about your practice area that could be extracted using
data mining strategies?
4. Data mining is associated with numerous techniques and algorithms. How can you make sure that you select and develop those that best fit your
data?
References
Berger, A. M., & Berger, C. R. (2004). Data mining as a tool for research and knowledge development in nursing. Computers Informatics Nursing,
22(3), 123–131. PubMed ID: 15520581
DeGruy, K. B. (2000). Healthcare applications of knowledge discovery in databases. Journal of Healthcare Information Management, 14(2), 59–69.
PubMed ID: 11066649
Fernández-Llatas, C., Garcia-Gomez, J. M., Vicente, J., Naranjo, J. C., Robles, M., Benedi, J. M., & Traver, V. (2011). Behaviour patterns detection for
persuasive design in nursing homes to help dementia patients. Conference Proceedings IEEE Engineering in Medicine & Biology Society, 6413–
6417. PubMed ID: 22255806
Goodwin, L., Saville, J., Jasion, B., Turner, B., Prather, J., Dobousek, T., & Egger, S. (1997). A collaborative international nursing informatics research
project: Predicting ARDS risk in critically ill patients. Studies in Health Technology Informatics, 46, 247–249. PubMed ID: 10175406
Green, J., Paladugu, S., Shuyu, X., Stewart, B., Shyu, C., & Armer, J. (2013). Using temporal mining to examine the development of lymphedema in
breast cancer survivors. Nursing Research, 62(2), 122–129. PubMed ID: 23458909
IBM. (2013). Big data at the speed of business. http://www-01.ibm.com/software/data/bigdata/
Lee, T., Lin K., Mills, M., & Kuo, Y. (2012). Factors related to the prevention and management of pressure ulcers. Computers Informatics Nursing,
30(9), 489–495. PubMed ID: 22584879
Lee, T., Liu, C., Kuo, Y., Mills, M., Fong, J., & Hung, C. (2011). Application of data mining to the identification of critical factors in patient falls using
a Web-based reporting system. International Journal of Medical Informatics, 80(2), 141–150. PubMed ID: 21115393
Madigan, E., & Curet, O. (2006). A data mining approach in home healthcare: Outcomes and service use. BMC Health Services Research, 6, 18.
PubMed ID: 16504115
Manyika, J., Chu, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. (2011). McKinsey Global Institute: Big data: The next frontier for
innovation, competition, and productivity. http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation
SAS. (n.d.). SAS enterprise miner. http://www.sas.com/offices/europe/uk/technologies/analytics/datamining/miner/semma.html
Tishgart, D. (2012). Why security matters for big data and health care: Data integrity requires good data security. http://soa.sys-con.com/node/2389698
Trangenstein, P., Weiner, E., Gordon, J., & McNew, R. (2007). Data mining results from an electronic clinical log for nurse practitioner students. Studies
in Health Technology Informatics, 129, 1387–1391. PubMed ID: 17911941
Zupan, B., & Demsar, J. (2008). Open-source tools for data mining. Clinics in Laboratory Medicine, 28(1), 37–54. PubMed ID: 18194717
Chapter 25
Translational Research: Generating Evidence for Practice
Jennifer Bredemeyer and Ida Androwich
OBJECTIVES
1. Clarify the differences between evidence-based practice and translational research.
2. Describe models for introducing research findings into practice.
3. Identify barriers to research utilization in practice.
Key Terms
Agency for Healthcare Research and Quality (AHRQ)
Context of care
Evidence
Evidence-based practice (EBP)
Iowa model
Meta-analysis
National Guideline Clearinghouse (NGC)
Open Access Initiative
Qualitative study
Quantitative study
Research utilization
Research validity
Translational research
Introduction
Mr. James is an 87-year-old man with osteoarthritis in his knees. He is frail and very thin and requires assistance getting out of bed.
Mary, a new registered nurse, is making her rounds with her team members and nurse’s aide. Realizing Mr. James is at risk for skin
breakdown and falls, she reviews the agency policy manual regarding pressure ulcer prevention and fall prevention. Which other
resources could Mary consult if she wanted more information on preventing these issues? If Mary wanted to know what the current
research suggests about preventing each of these conditions, how would she obtain this information?
This chapter introduces the concept of translational research and its role in evidence-based practice with specific emphasis on
nursing informatics. Before pursuing the content in this chapter, the reader should already have an understanding of nursing research,
the Foundation of Knowledge model, and knowledge generation through nursing research. Key words and definitions used in this
chapter are described briefly next. Classic sources (5 years or older) are used to enhance the reference base.
If I limit what I speak about to what I know from experience to be true rather than what I think I am expected to say or what I
am pressured to say, then I will have a contribution to make. (Camus, 1943)
Clarification of Terms
Evidence-based practice, translational research, and research utilization are all terms that have been used to describe the application
of evidential knowledge to clinical practice. The following paragraphs explore the definitions of each term. Although these terms are
related, they have slightly different meanings and applications.
Evidence-based practice (EBP), developed originally for its application to medicine, is defined by Sackett et al. (1996) as “The
conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients” (p. 71).
The “best evidence” in this context refers to more than just research. Goode and Piedalue (1999) state that evidenced-based practice
should be combined with other knowledge sources and “involves the synthesis of knowledge from research, retrospective or
concurrent chart review, quality improvement and risk data, international, national, and local standards, infection control data,
pathophysiology, cost effectiveness analysis, benchmarking data, patient preferences, and clinical expertise” (p. 15). EBP starts with a
clinical question to resolve a clinical problem. For example, published research studies are used in healthcare quality initiatives as the
evidence behind the development of practice algorithms designed to decrease practice variability, increase patient safety, improve
patient outcomes, and eliminate unnecessary costs. Use of EBP promotes the use of clinical judgment and knowledge, with procedures
and protocols being linked to scientific evidence rather than based on what is customary practice or opinion.
Research utilization is the use of findings from one or more research studies in a practical application unrelated to the original study
(Polit & Beck, 2008, p. 29) resulting in the generation of new knowledge. Stetler (2001, p. 274) defines research utilization as the
“process of transforming research knowledge into practice.” Research utilization can be self-limiting if research is inconsistent or not
enough research is available to develop a consensus regarding the answer to the clinical question (Kirchhoff, 2004).
Translational research (science) describes the methods used in translating medical, biomedical, informatics, and nursing research
into bedside clinical interventions. Woolf (2008) describes translational research in two ways:
T1: the transfer of clinical research to its first testing on humans
T2: the transfer of clinical research to an everyday clinical practice setting
Difficulties in translating research to the T2 setting exist when research applications do not fit well within the clinical context or
practical considerations constrain the application in a clinical setting. Translational research is complicated by the follow-up analysis,
practice, and policy changes that occur when adopting research into practice; consequently, available evidence-based healthcare
practices are often not fully incorporated into daily care (Titler, 2004, 2010). Organizational culture influences the changes made to a
clinical application and establishes the groundwork and the support for change-making activities (Titler, 2004). The study of ways to
promote the adoption of evidence in the healthcare context is called “translation science” (Titler, 2010).
History of Evidence-Based Practice
Research results are crucial to furthering EBP. The concept of using randomized controlled trials and systematic reviews as the gold
standard against which one should evaluate the validity and effectiveness of a clinical intervention was introduced in 1972 by Archie
Cochrane, a scientist and a physician (Cochrane, 1972). Cochrane’s experiences as a prisoner of war and medical officer while
interning during World War II led to his belief that not all medical interventions were needed and some caused more harm than good.
Cochrane viewed the randomized clinical trial as a means of validating clinical interventions and limiting the interventions to those
that were scientifically based, effective, and necessary (Dickersin & Manheimer, 1998).
Cochrane’s colleague, Iain Chalmers, began compiling a comprehensive clinical trials registry of 3,500 clinical trial results in the
field of perinatal medicine. In 1988, after being published in print 3 years earlier, the registry became available electronically.
Chalmers’ methods for compiling the trials databases became a model for future registry assembly. Eventually, the National Health
Service in the United Kingdom, recognizing the value of and need for systemic reviews for all of health care, developed the Cochrane
Center. The Cochrane Collaboration was initiated in 1993 and expanded internationally to maintain systematic reviews in all areas of
health care (Dickersin & Manheimer, 1998). Many universities subscribe to the Cochrane Collaboration database, making this
information easily accessible to students, faculty, and nurses who work for university hospital systems.
Evidence
The randomized controlled trial (RCT) is considered the most reliable source of evidence. Yet, RCTs are not always possible or
available; consequently, nurses must use critical analysis to base their clinical decision making on the best available evidence
(Baumann, 2010). The updated Stetler model of research utilization (Stetler, 2001) identifies internal and external forms of evidence.
External evidence originates from research and national experts, whereas internal forms of evidence originate from nontraditional
sources, such as clinical experience and quality improvement data.
Evidence includes standards of practice, codes of ethics, philosophies of nursing, autobiographic stories, esthetic criticism, works
of art, qualitative studies, and patient and clinical knowledge (Melnyk, Fineout-Overholt, Stone, & Ackerman, 2000). French (2002)
summarizes evidence as “truth, knowledge (including tacit, expert opinion and experiential), primary research findings, meta-analyses
and systematic reviews” (p. 254). Nurses may additionally draw on evidence from the context of care, such as audit and performance
data, the culture of the organization, social and professional networks, discussion with stakeholders, and local or national policy
(Rycroft-Malone et al., 2004, p. 86).
Concern has been voiced by nurse theorists that nurses are being influenced too much by the medical model in accepting the
randomized controlled trial as the only true source of evidence, thereby “reverting to the medical perspective” rather than
incorporating “theory-guided evidence and diverse ways of knowing” (Fawcett, Watson, Walker, & Fitzpatrick, 2001, p. 115). The
context change from medicine to nursing requires nurses to apply other knowledge and nursing theory. The use of research results as
the sole basis for clinical decision making ignores other types of evidence inherent in nursing practice (Scott-Findlay & Pollock,
2004).
To use evidence in practice, the weight of the research, also called research validity, must be determined. Evidence hierarchies
have been defined to grade and assign value to the information source. For example, an evidential hierarchy developed by Stetler et al.
(1998) prioritizes evidence into six categories:
1. Meta-analysis
2. Individual experimental studies
3. Quasi-experimental studies
4. Nonexperimental studies
5. Program evaluations, such as quality improvement projects
6. Opinions of experts
The hierarchy identifies meta-analysis as the best-quality evidence because it uses multiple individual research studies to reach a
consensus. It is interesting to note that opinions of experts are considered the least significant in this hierarchy, yet nurses most often
seek the opinion of a more experienced colleague or peer when searching for information regarding patient care (Pravikoff, Tanner, &
Pierce, 2005).
Qualitative research allows one to understand the way in which the intervention is experienced by the researcher and the
participant and the value of the interventions to both parties (O’Neill, Jinks, & Ong, 2007). Qualitative research is not always
considered in EBP, because methods for synthesizing the evidence do not currently exist. The Cochrane Qualitative Research Methods
Group (CQRMG) is developing search, appraisal, and synthesis methodologies for qualitative research (Joanna Briggs Institute, n.d.).
Bridging the Gap Between Research and Practice
The time between research dissemination and clinical translation may be significant, and this delay may adversely affect patient
outcomes. Bridging the gap between research and practice requires an understanding of the key concepts and barriers, access to
research findings, access to clinical mentors for research understanding, a reinforcing culture, and a desire on the part of the clinician
to implement best practices (Melnyk, 2005; Melnyk, Fineout-Overholt, Stetler, & Allen, 2005). In the Iowa model of EBP, research
and other evidential sources are adopted directly in the practice setting with the goal of developing a standard of care (Titler, 2007).
Additionally, the groundwork required to create a conceptual framework supportive of an EBP includes workplace culture change and
support of the change through leadership (Stetler et al., 1998). Beliefs and attitudes, involvement in research activities, information
seeking, professional characteristics, education, and other socioeconomic factors are potential determinants of research utilization
(Estabrooks, Floyd, Scott-Findlay, O’Leary, & Gushta, 2003); however, meta-analysis points out that too much original research and
not enough repetition of previous studies fails to advance the knowledge base.
Developing countries are often constrained economically from accessing research sources. Such organizations as the Cochrane
Collaboration provide free reviews to fill this void. Even so, knowledge dissemination strategies and education are required to take
advantage of these resources (Cochrane Collaboration, 2004).
Barriers to and Facilitators of Evidence-Based Practice
Barriers to the application of EBP include lack of time, lack of access to libraries within a facility, lack of technology confidence, lack
of knowledge on how to search for information, lack of value assigned to using research in practice (Pravikoff et al., 2005), inadequate
EBP knowledge and skills, lack of mentors in EBP, inadequate support and resources from administration, and insufficient time
(Melnyk, Fineout-Overholt, Stillwell, & Williamson, 2009). McKnight (2006) notes that nurses on one unit felt constrained by time
and ethically obligated to provide patient care rather than spend time looking up evidence-based references. Nurses may also see the
job of interpreting research as too complex or see the organizational culture as a barrier to implementation of EBP (McCaughan,
Thompson, Cullum, Sheldon, & Thompson, 2002).
Yet, Melnynk et al. (2009) noted that a number of factors also facilitate the use of EBP. These driving forces include knowledge
and skills in EBP; having a conviction that there is a value to using evidence in practices; practicing in a supportive culture with tools
available to sustain evidence-based care, including access to computers and databases, evidence-based content at the point of care, and
the presence of EBP mentors.
The Role of Informatics
Computers are used in all areas of research: (1) literature search databases, such as CINAHL; (2) online literature reference lists, such
as RefWorks; (3) data capture, collection, and coding; (4) data analysis; (5) data modeling; (6) meta-analysis; (7) qualitative analysis;
and (8) dissemination of results (e.g., via e-mail or Internet website) (Saba & McCormick, 2006). The context for nursing informatics
has expanded to support dramatic changes in the way science is accomplished. Information need and the collaborative component of
interdisciplinary research rely heavily on technology and informatics. Technologies, such as social networking (Web 2.0), may also
improve collaboration. The use of technology and informatics in facilitating interdisciplinary and translational research is a key
architectural component of the National Institutes of Health’s reengineering of the clinical research enterprise as part of its road map
initiative for medical research (National Institutes of Health, 2009).
An informatics infrastructure is critical to EBP. Bakken, Stone, and Larson (2008) discuss expanding the context of informatics to
genomic health care, shifting research paradigms, and social web technologies. Ensuring the global collaborative nature of nursing
research for 2010–2018 requires an expansion of the nursing research agenda to user information needs, data management, information
support for nurses and patients, practice-based knowledge generation, and design evaluation methodologies. Giuse et al. (2005)
describe the evolving role of the clinical informationist as being a partner on the healthcare team who provides timely clinical evidence
for the clinical workflow. Although not specific to nursing informatics, the National Institutes of Health provides awards under its
Clinical and Translational Science Award (CTSA) program to accelerate the transfer of research to the clinical setting (National
Institutes of Health, 2009). The Quality and Safety Education in Nursing initiative (Cronenwett et al., 2009) cites key competencies
(knowledge and skills) in both EBP and informatics.
As an example of the integration of informatics and the medical record, Matter (2006) describes the positive effects of a successful
integration of referential links with EBP clinical content in the clinical pathway on patient outcomes. This pilot project, which started
in 2005 at Pinnacle Health in Pennsylvania, involved two vendors and combined the use of electronic health records with evidence-
based electronic reference links to create a point-of-care tool that allowed clinicians to integrate nursing care plans supported by the
corresponding evidential link. Outcomes realized as a result of this project included gains in the development of institutional
guidelines, clinical collaboration, and 76% compliance with pain assessment 2 hours after medication administration.
With the goal of promoting the use of research findings and tool use based on these findings, the Agency for Healthcare
Research and Quality (AHRQ) became an active participant in pushing evidence forward into practice. The AHRQ is a government-
sponsored organization with the mission of reducing patients’ risk of harm, decreasing healthcare costs, and improving patient
outcomes through the promotion of research and technology applications focused on EBP. In 1999, AHRQ implemented its
Translating Research into Practice Initiative (TRIP) to generate knowledge about evidence-based care (Agency for Healthcare
Research and Quality, 2001). In the second Translating Research into Practice Initiative (TRIP-II), the focus shifted to improving
health care for underserved populations and using information technology to shape translational research and health policy. AHRQ, in
partnership with the American Medical Association and the American Association of Health Plans, developed the National Guideline
Clearinghouse (NGC). NGC is a comprehensive database of evidentially based clinical practice guidelines and related documents that
are regularly published through the NGC electronic mailing list and are available on the NGC website (National Guideline
Clearinghouse, 2010). The NGC website allows users to browse for the clinical guidelines, view abstracts and full-text links, download
fulltext clinical guidelines to personal digital assistant (PDA) devices and smartphones, obtain technical reports, and compare
guidelines. PubMed4Hh (PubMed for handheld devices) is a powerful and free application for smartphones that provides access to the
national Library of Medicine and supports PICO searches, clinical queries, and multilanguage searches with links to consensus
abstracts.
In addition, a growing number of printed and electronic resources are available to assist in creating guidelines and offering
information about EBP. A selection of existing websites is shown in Table 25-1.
TABLE 25-1 THE ROLE OF INFORMATICS: ONLINE EVIDENCE-BASED RESOURCES
Website Description
Academic Center for Evidence-Based Practice (ACE)
http://www.acestar.uthscsa.edu
The School of Nursing at the University of Texas Health Science
Center at San Antonio sponsors the Academic Center for Evidence-
Based Practice.
The center’s ultimate goal is to bring research to practice to
improve patient care, outcomes, and safety. The center is also
home to the ACE star model of knowledge transformation.
The Agency for Healthcare Research and Quality
http://www.ahrq.gov
The agency for Healthcare Research and Quality contains a wealth
of information regarding healthcare quality. There is no charge for
access to the site or its resources.
BMJ Publishing
http://clinicalevidence.bmj.com/x/index.html
The BMJ publishing group provides clinical databases by
prescription. The BMJ Clinical Evidence site allows the download
of some clinical papers and some interesting risk tools without
charge.
The Center for Evidence-Based Practices (CEBP)
http://www.evidencebasedpractices.org
The Center for Evidence-Based Practices of the Orelena Hawks
Puckett Institute focuses on research to practice initiatives related
to early intervention, early childhood education, parent and family
support, and family-centered practices.
Centre for Evidence Based Medicine
The Center for Evidence Based Medicine, located in Oxford in the
UK, is devoted to developing and promoting evidence-based
resources for healthcare professionals. In addition to free articles,
the site also provides free teaching resources and presentation.
CINAHL
http://cinahl.com
CINAHL information systems offers a multitude of online services,
which include website link sources, CINAHL’S online nursing and
allied health database, document delivery, and search services.
The Cochrane Collaboration
http://www.cochrane.org
The Cochrane Collaboration provides reviews for free, but full-text
articles are by subscription.
Entrez PubMed
http://www.ncbi.nlm.nih.gov/guide/all/#databases_
Entrez PubMed is a service provided by The National Library of
Medicine (NLM). The NLM was developed by the National Center
for Biotechnology Information (NCBI), which provides access to
life science journals and MEDLINE citations. Some of the journal
links are free, and some require a subscription.
PubMed Central
http://www.pubmedcentral.nih.gov
PubMed Central (PMC) is a free digital archive of science-related
articles managed by the NCBI. BioMed Central (an open-source
online archive) may be accessed here.
The Joanna Briggs Institute
http://www.joannabriggs.org/index.html
The Joanna Briggs Institute was established in 1996 as a resource
for best care practices. Joanna Briggs was first matron of the
Adelaide Hospital in Australia and is recognized for her financial
and organizational support. The Joanna Briggs Institute is a leader
in developing evidence-based practices.
Information [DM-AMP] Resources for Nurses Worldwide
http://www.nurses.info/specialty_evidenced_based_orgs.htm
This website provides searchable links to evidence-based practice
organizations by specialty.
The Iowa Model of Evidence-Based Practice
http://www.nnpnetwork.org/ebpresources/iowa-model
This website provides a brief overview of the Iowa Model for
Evidence-Based Practice.
The Johns Hopkins Bloomberg School of Public Health
http://www.jhsph.edu/epc
The Johns Hopkins Evidence-Based Practice Center was
established in 1997 and is one of 14 such centers producing
comprehensive, systematic reviews for the AHRQ.
Trip database
http://www.tripdatabase.com
The Trip database is a clinical search tool to allow clinicians to
identify the best evidence for clinical practice.
University of Iowa College of Nursing
http://www.nursing.uiowa.edu/hartford
The John A. Hartford Foundation Center of Geriatric Nursing
Excellence at the University of Iowa has evidence-based practice
resources available at this site for a nominal fee.
World Views on Evidence-Based Nursing
http://blackwellpublishing.com/wvn
Through Blackwell Publishing, this magazine, sponsored by Sigma
Theta Tau, is dedicated exclusively to evidence-based nursing
articles. The magazine is also offered online by subscription.
Developing EBP Guidelines
Several models have been developed to guide organizations into translating research into practice. Brief descriptions of these models
are provided in Table 25-2. As an example, Titler (2007) identifies the steps in the Iowa model for translating research into practice as
(1) identifying the problem, issue, or topic in nursing practice; (2) research and critique of related evidence; (3) adaptation of the
evidence to practice; (4) implementation of the EBP; and (5) evaluation of patient outcomes and care practices. Careful analysis and
discussion of the research or other forms of evidence in this scenario may reveal that given the context, implementation may not be
practical. Following implementation, results must be monitored to determine whether the application works for the context. Thoughtful
discussion of the findings will help the clinical team determine if further research is warranted or if further change is needed. As a
practical application, evidence-based standards for care are developed by hospitals to meet the American Nurses Association/American
Nurses Credentialing Center standards for achieving Magnet hospital recognition.
TABLE 25-2 COMPARISON OF MODEL APPROACHES TO EVIDENCE-BASED PRACTICE
Stetler model (Stetler, 2001) ACE star model (Stevens, 2004) The Iowa model of evidence-based practice topromote quality care (Titler et al., 2001)
1. Preparation
2. Validation
3. Cooperative evaluation
4. Decision making
5. Translational application
6. Evaluation
1. Discovery
2. Evidence summary
3. Translation
4. Integration
5. Evaluation
1. Select the trigger as impetus for practice (knowledge
focused or practice focused) change.
2. Determine if the topic is worth pursuing for the
organization and if not, pursue new trigger.
3. Determine if there is significant research base. If so,
change, otherwise conduct research or seek more
research.
4. If change is appropriate for practice, implement
change.
5. Monitor results.
6. Disseminate results.
Information technology is important in synthesizing the research regardless of the model. Bakken (2001) recommends (1)
standardized nomenclature required for the electronic health record (standardized terminologies and structures); (2) digital sources of
evidence; (3) standards that facilitate healthcare data exchange among heterogeneous systems; (4) informatics processes that support
the acquisition and application of evidence to a specific clinical situation; and (5) informatics competencies (p. 1999). Bakken’s
recommendations encourage the development of an infrastructure that creates a database of experiential clinical evidence.
Meta-Analysis and Generation of Knowledge
Systematic reviews combine results from multiple primary investigations to obtain consensus on a specific area of research. Studies
are discarded from the review if they are not considered sound, thereby creating a reliable end result. The strength of the systematic
review is its ability to corroborate findings and reach consensus. Systematic reviews show the need for more research by revealing the
areas where quantitative results may be lacking or minimal. Bias may occur if the selected studies are inadequate, if all sources of
evidence are not investigated, or if the publications selected are not adequately diverse (Lipp, 2005).
Meta-analysis, a form of systematic review, uses statistical methods to combine the results of several studies (Cook, Mulrow, &
Haynes, 1997). Quantitative studies are typically used. According to Glass (1976), meta-analysis is the statistical analysis of a large
collection of analysis results from individual studies for the purpose of integrating the findings (p. 3).
Kraft (2006) describes the documentation search strategy for meta-analysis as beginning with the identification of the studies
through a search of bibliographic databases, identification of meta-analysis articles that match the search criteria, elimination of those
articles that do not match the search criteria, review of the reference lists in the meta-analysis for other articles that may relate to the
topic, and review of each article for quality and content. Additional sources should include unpublished works, such as conferences
and dissertation abstracts, with the goal of obtaining as many relevant articles as possible. Gregson, Meal, and Avis (2002) identify the
steps of a meta-analysis as (1) defining the problem, followed by protocol generation; (2) establishing study eligibility criteria,
followed by literature search; (3) identifying the heterogeneity of results of studies; (4) standardizing the data and statistically
combining the results; and (5) conducting sensitivity testing to determine whether the combined results are the same. The often-cited
criticism of meta-analysis is that emphasis is on quantitative studies, not qualitative studies. Additionally, the analysis is only as
good as the studies used (Gregson et al., 2002). Collection and dissemination of these meta-analysis and systematic reviews are
available in paper and on the Internet, although many such databases require a subscription.
The term “open access” refers to a worldwide movement to make a library of knowledge available to anyone with Internet access.
The Open Access Initiative came about in response to the tremendous cost of research library access. Libraries pay large fees for
journal subscriptions, and the richness of library references is limited to what the budget allows. The cost of keeping current with
research has caused library subscriptions to decline (Yiotis, 2005). Open access adds to the controversy, with some journals charging
authors for publication of their work, which in itself may provide a financial barrier to publication in this form.
According to Suber (2004), open access refers to digital literature that is available to anyone with Internet access free of charge.
There are two vehicles for open access: archives and journals. Open access journals are generally peer reviewed and freely available.
The publishers of open access journals do not charge the reader but rather obtain funds for publishing elsewhere. Open access journals
may charge the author or depend on other forms of funding, such as donations, grants, and advertising, to publish.
The Future
Titler (2007) indicates that future priorities should include development of theoretical formulations to guide research and systematic
reviews so that they may be grouped by organizational context (e.g., primary care, outpatient). Focus on other forms of research, such
as qualitative research, should also be incorporated into systematic reviews.
Given the vast amounts of data, Bakken et al. (2008) identify areas of focus for nursing informatics in knowledge representation,
data management, analysis, and predictive modeling in genomic health care and the need for policies and procedures to protect data
acquisition, dissemination, privacy, security, and confidentiality, as well as education in these areas. Informatics tools support nursing
practice, education of healthcare consumers, and knowledge generation. The technology is available now to incorporate evidence into
reference links embedded in electronic clinical care plans. Incorporation of personalized clinical desktops to allow each clinician to
have appropriate references (similar to Internet ad bot technology) provided to him or her may be possible. Time, research, and
technology will tell.
Summary
These are amazing times. Technology has taken us faster and further than we ever thought possible. Healthcare jobs have become
more technical and more complicated. In some ways, technology has increased the margin for error. Some healthcare practitioners will
continue to rely on little scraps of paper and nonsystematic methods to keep themselves and their patients safe. Unfortunately,
individuals who become so tied to these things close their mind to new innovations. The evolving quality culture and increased patient
safety concerns are dragging healthcare workers forward. For the benefit of our patients, health care must move forward.
Collaboration, improved access to online libraries, research tool transparency, a common data language, organizational and
informational support, and continued research are a short list of needed items to advance translational research. Repeat studies are
needed to provide meaningful meta-analysis and systematic reviews. Technology advancement in the area of incorporating evidence
into clinical tools must continue. Removing the barriers to knowledge-seeking behavior and providing access to evidential resources
will promote knowledge and, in the end, improve patient outcomes.
In the era of EBP, healthcare providers must continue to think critically about their actions. What is the science behind their
interventions? Healthcare workers must no longer do things one way just because they have always been done that way. Research the
problem; use evidence-based resources; critically select electronic and nonelectronic references; consolidate the research findings and
combine and compare the conclusions; present the findings; and propose a solution. One will be the first to ask why and may be a key
player in making change happen.
THOUGHT-PROVOKING
QUESTIONS
1. Twelve-hour shifts are problematic for patient and nurse safety, yet hospitals continue to keep the 12-hour shift schedule. In 2004, the Institute of
Medicine (Board on Health Care Services & Institute of Medicine, 2004) published a report that referred to studies as early as 1988 that
discussed the negative effects of rotating shifts on intervention accuracy. Workers with 12-hour shifts experienced more fatigue than workers on
8-hour shifts. In another study done in Turkey by Ilhan, Durukan, Aras, Turkcuoglu, and Aygun (2006), factors relating to increased risk for
injury were age of 24 years or younger, less than 4 years of nursing experience, working in surgical intensive care units, and working for more
than 8 hours. As a clinician reading these studies, what would your next step be?
2. The use of heparin versus saline to maintain the patency of peripheral intravenous catheters has been addressed in research for many years. The
American Society of Health System Pharmacists (ASHSP) published a position paper in January 2006 advocating its support of the use of 0.9%
saline in the maintenance of peripheral catheters in nonpregnant adults. It seems surprising that this position paper references articles that
advocate the use of saline over heparin dating from 1991. What do you believe are some of the barriers that would have caused this delay in
implementation?
References
Agency for Healthcare Research and Quality. (2001). AHRQ profile: Quality research for quality healthcare. http://www.ahrq.gov/about/profile.htm
American Society of Health System Pharmacists. (2006). ASHSP therapeutic position statement on the institutional use of 0.9% sodium chloride
injection to maintain patency of peripheral indwelling intermittent infusion devices. American Journal of Health System Pharmacy, 63, 1273–1275.
Bakken, S. (2001). An informatics infrastructure is essential for evidence-based practice. Journal of American Medical Informatics Association, 8, 199–
201.
Bakken, S., Stone, P. W., & Larson, E. L. (2008). A nursing informatics research agenda for 2008–18: Contextual influences and key components.
Nursing Outlook, 56, 206–214.
Baumann, S. (2010). The limitations of evidence-based practice. Nursing Science Quarterly, 23(3), 226–230.
Board on Health Care Services & Institute of Medicine. (2004). Keeping patients safe. Washington, DC: National Academies Press.
Camus, A. (1943). The stranger. London, UK: Penguin.
Cochrane, A. L. (1972). Effectiveness and efficiency: Random reflections on health services. London, UK: Nuffield Provincial Hospitals Trust.
Cochrane Collaboration. (2004). Bridging the gaps across the income divide: A review of the collaboration’s efforts to date and recommendations for the
future. http://www2.cochrane.org/colloquia/abstracts/ottawa/O-006.htm
Cook, D. J., Mulrow, C. D., & Haynes, R. B. (1997). Systematic reviews: Synthesis of best evidence for clinical decisions. Annals of Internal Medicine,
126(5), 376–380.
Cronenwett, L., Sherwood, G., Pohl, J., Barnsteiner, J., Moore, S., Sullivan, D., … Warren, J. (2009). Quality and safety education for advanced nursing
practice. Nursing Outlook, 57(6), 338–348.
Dickersin, K., & Manheimer, E. (1998). The Cochrane Collaboration: Evaluation of health care services using systematic reviews of the results and
randomized control trials. Clinical Obstetrics and Gynecology, 41(2), 315–331.
Estabrooks, C. A., Floyd, J. A., Scott-Findlay, S., O’Leary, K. A., & Gushta, M. (2003). Individual determinants of research utilization: A systematic
review. Journal of Advanced Nursing, 42, 73–81.
Fawcett, J., Watson, J., Walker, P. H., & Fitzpatrick, J. J. (2001). On nursing theories and evidence. Journal of Nursing Scholarship, 33(2), 115–119.
French, P. (2002). What is the evidence on evidence-based nursing? An epistemological concern. Journal of Advanced Nursing, 37(3), 250–257.
Giuse, N. B., Koonce, T. Y., Jerome, R. N., Gahall, M., Sathe, N. A., & Williams, A. (2005). Evolution of a mature clinical informationist model.
Journal of the American Medical Informatics Association, 12(3), 249–255.
Glass, G. V. (1976). Primary, secondary and meta-analysis of research. Educational Research, 5(10), 3–8.
Goode, C. J., & Piedalue, F. (1999). Evidence-based clinical practice. Journal of Nursing Administration, 29(6), 15–21.
Gregson, P. R., Meal, A. G., & Avis, M. (2002). Meta-analysis: The glass eye of evidence-based practice? Nursing Inquiry, 9(1), 24–30.
Ilhan, M. N., Durukan, E., Aras, E., Turkcuoglu, S., & Aygun, R. (2006). Long working hours increase the risk of sharp and needlestick injury in nurses:
A need for new policy implication. Journal of Advanced Nursing, 56(5), 563–568.
Joanna Briggs Institute. (n.d.). http://joannabriggs.org/index.html
Kirchhoff, K. T. (2004). State of the science of translational research: From demonstration projects to intervention testing. Worldviews on Evidence-
Based Nursing, 1(suppl 1), S6–S12.
Kraft, M. R. (2006). Meta-analysis: A research tool. SCI Nursing Journal, 23(2). Retrieved September 2007 from
http://www.unitedspinal.org/publications/nursing/2006/08/27/research-corner/
Lipp, A. (2005). The systematic review as an evidence-based tool for the operating room. Association of Operating Room Nurses Journal, 81(6), 1279–
1287.
Matter, S. (2006). Empower nurses with evidence-based knowledge. Nurse Management, 37(12), 34–37.
McCaughan, D., Thompson, C., Cullum, N., Sheldon, T. A., & Thompson, D. R. (2002). Acute care nurses’ perceptions of barriers to using research
information in clinical decision-making. Journal of Advanced Nursing, 39(1), 46–60.
McKnight, M. (2006). The information seeking of on-duty critical care nurses: Evidence from participant observation and in-context interviews. Journal
of the Medical Library Association, 94(2), 145–151.
Melnyk, B. M. (2005). Advancing evidence-based practice in clinical and academic settings. Worldviews on Evidence-Based Nursing, 3, 161–165.
Melnyk, B. M., Fineout-Overholt, E., Stetler, C., & Allen, J. (2005). Outcomes and implementation strategies from the first U.S. evidence-based practice
leadership summit. Worldviews on Evidence-Based Nursing, 2(3), 113–121.
Melnyk, B. M., Fineout-Overholt, E., Stillwell, S., & Williamson, K. (2009). Igniting a spirit of inquiry: An essential foundation for evidence-based
practice. American Journal of Nursing, 109(11), 49–52.
Melnyk, B. M., Fineout-Overholt, E., Stone, P., & Ackerman, M. (2000). Evidence-based practice: The past, the present, and recommendations for the
millennium. Pediatric Nursing, 26(1), 77–80.
National Guideline Clearinghouse. (2010). http://www.guideline.gov/
National Institutes of Health. (2009). Re-engineering the clinical research enterprise.
https://web.archive.org/web/20100527150048/http://commonfund.nih.gov/clinicalresearch/overviewtranslational.asp
O’Neill, T., Jinks, C., & Ong, B. N. (2007). Decision-making regarding total knee replacement surgery: A qualitative meta-synthesis. BMC Health
Services Research, 7(52). http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1854891
Polit, D. F., & Beck, T. C. (2008). Nursing research: Generating and assessing evidence for nursing practice (8th ed.). Philadelphia, PA: Lippincott
Williams & Wilkins.
Pravikoff, D. S., Tanner, A. B., & Pierce, S. T. (2005). Readiness of U.S. nurses for evidence-based practice. American Journal of Nursing, 105(9), 40–
51.
Rycroft-Malone, J., Seers, K., Titchen, A., Harvey, G., Kitson, A., & McCormack, B. (2004). What counts as evidence in evidence-based practice?
Journal of Advanced Nursing, 47(1), 81–90.
Saba, V. K., & McCormick, K. A. (2006). Essentials of nursing informatics (4th ed.). New York, NY: McGraw-Hill.
Sackett, D. I., Rosenberg, W. M., Gray, J. A., Haynes, R. B., & Richardson, W. S. (1996). Evidence based medicine: What it is and what it isn’t. British
Medical Journal, 312, 71–72.
Scott-Findlay, S., & Pollock, C. (2004). Evidence, research, knowledge: A call for conceptual clarity. Worldviews on Evidence-Based Nursing, 1(2), 92–
97.
Stetler, C. B. (2001). Updating the Stetler model of research utilization to facilitate evidence-based practice. Nursing Outlook, 49(6), 272–279.
Stetler, C. B., Brunell, M., Giuliano, K. K., Morse, D., Prince, L., & Newell-Stokes, V. (1998). Evidence-based practice and the role of nursing
leadership. Journal of Nursing Administration, 28(7/8), 45–53.
Stevens, K. R. (2004). ACE star model of EBP: Knowledge transformation. Retrieved July 2010 from http://www.acestar.uthscsa.edu/Learn_model.htm
Suber, P. (2004). A very brief introduction to open access. http://www.earlham.edu/~peters/fos/brief.htm
Titler, M. G. (2004). Methods in translation science. Worldviews on Evidence-Based Nursing, 1, 38–48.
Titler, M. (2007). Translating research into practice: Models for changing clinician behavior. American Journal of Nursing, 107(6), 26–33.
Titler, M. G. (2010). Translation science and context. Research Theory and Nursing Practice: An International Journal, 24(1), 35–55.
Titler, M. G., Kleiber, C., Steelman, V., Rakel, B., Budreu, G., Everett, L., … Goode, T. (2001). The Iowa model of evidence-based practice to promote
quality care. Critical Care Nursing Clinics of North America, 13(4), 497–509.
Woolf, S. H. (2008). The meaning of translational research and why it matters. Journal of the American Medical Association, 299(2), 211–213.
Yiotis, K. (2005). The Open Access Initiative: A new paradigm for scholarly communications.
http://ejournals.bc.edu/ojs/index.php/ital/article/view/3378/2988
Chapter 26
Bioinformatics, Biomedical Informatics, and
Computational Biology
Dee McGonigle and Kathleen Mastrian
OBJECTIVES
1. Describe bioinformatics, biomedical informatics, and computational biology.
2. Appreciate the intertwining of bioinformatics and health care in biomedical informatics.
3. Imagine the future of health care based on genomics.
Key Terms
Allele
Bioinformatics
Biomedical informatics
Computational biology
Data set
Genome
Genomics
Haplotype
Human Genome Project
Informaticist
Introduction
The National Center for Biotechnology Information (NCBI, 2004) states that “Biology in the 21st century is being transformed from a
purely lab-based science to an information science as well” (para. 5). What must be remembered when delving into the new
informatics frontier is that biological systems are information systems. Consider that DNA essentially is a storehouse of information.
Unlocking that information and learning how that information is transcribed to RNA and ultimately expressed as proteins promises
interesting and cutting-edge developments in understanding diseases and managing them at the molecular level. This chapter
introduces the reader to the exciting world of bioinformatics and provides a beginning understanding of the frontiers unleashed by the
collection, mapping, storage, and sharing of genetic data.
Bioinformatics, Biomedical Informatics, and Computational Biology Defined
Three terms are frequently used: (1) bioinformatics, (2) biomedical informatics, and (3) computational biology. As terms continue
to be bandied about, it is important to comprehend what they mean to understand better these evolving fields. According to the
University of Texas at El Paso (2010) website, “Bioinformatics is an interdisciplinary science with a focus on data management and
interpretation for complex biological phenomena that are analyzed and visualized using mathematical modeling and numerical
methodologies with predictive algorithms” (para. 1). A website operated by the University of Minnesota (2010) states:
Bioinformatics is defined here as an interdisciplinary research area that applies computer and information science to solve
biological problems. However, this is not the only definition. The field is being defined (and redefined) at present, and there
are probably as many definitions as there are bioinformaticians (bioinformaticists?). (para. 1)
The myriad of definitions for the “moving target named bioinformatics” (University of Minnesota, 2010, para. 2) are reflected in
those developed from 2000 to 2009. According to NCBI (2004), “Bioinformatics is the field of science in which biology, computer
science, and information technology merge to form a single discipline. The ultimate goal of the field is to enable the discovery of new
biological insights” (para. 5). Network Science (2009) believes that
An absolute definition of bioinformatics has not been agreed upon. The first level, however, can be defined as the design and
application of methods for the collection, organization, indexing, storage, and analysis of biological sequences (both nucleic
acids [DNA and RNA] and proteins). The next stage of bioinformatics is the derivation of knowledge concerning the pathways,
functions, and interactions of these genes (functional genomics) and proteins (proteomics). (para. 14)
Mulligen et al. (2008) wrote that
BioInformatics (BI) is a less mature scientific discipline which aims to research and develop algorithms, computational and
statistical techniques which solve biological problems. Significantly, BI has experienced an exponential growth as a result of
its importance to the understanding and interpretation of data generated by “omics” technologies. (para. 6)
The complete sequencing of the human genome has led to systems biology referred to as “omics” and has elevated scientists’ ability
from studying one gene or protein to studying fundamental biological processes (Box 26-1).
BOX 26-1 OMICS
Kelly (2008) describes that “‘ome’ and ‘omics’ are suffixes that are derived from genome” (para. 1). National Public Radio (2010) credits botanist
Hans Winkler with merging “the Greek words ‘genesis’ and ‘soma’ to describe a body of genes” (para. 1) in 1920. The term “genome” was born
from this combination, and genomics arose as the study of the genome.
Kelly (2008) continues to explain:
Scientists like to append to these to any large-scale system (or really, just about anything complex), such as the collection of proteins in a cell
or tissue (the proteome), the collection of metabolites (the metabolome), and the collection of RNA that’s been transcribed from genes (the
transcriptome). High-throughput analysis is essential considering data at the “omic” level, that is to say considering all DNA sequences, gene
expression levels, or proteins at once (or, to be slightly more precise, a significant subset of them). (para. 1)
The National Human Genome Research Institute (NHGRI, n.d.) tutorial on bioinformatics defines bioinformatics as “the branch of
biology that is concerned with the acquisition, storage, display, and analysis of the information found in nucleic acid and protein
sequence data. Computers and bioinformatics software are the tools of the trade” (para. 4). Based on these definitions, one can get a
flavor for what bioinformatics entails. It is clear that the definition of bioinformatics varies, and that there is no single definition that
everyone agrees with at the present time.
Biomedicine applies bioinformatics to promote health. According to the website of the Ohio State University Medical Center
(2010), “Biomedical informatics is the study and process of efficiently gathering, storing, managing, retrieving, analyzing,
communicating, sharing, and applying biomedical information to improve the detection, prevention, and treatment of disease” (para.
2). The Vanderbilt University (2010) website suggests that
Biomedical Informatics is the interdisciplinary science of acquiring, structuring, analyzing and providing access to biomedical
data, information and knowledge. As an academic discipline, biomedical informatics is grounded in the principles of computer
science, information science, cognitive science, social science, and engineering, as well as the clinical and basic biological
sciences. (para. 1)
Gennari (2002) stated, “Biomedical Informatics is the science underlying the acquisition, maintenance, retrieval, and application of
biomedical knowledge and information to improve patient care, medical education, and health sciences research” (para. 1). He believes
that “a primary feature of Biomedical and Health Informatics is its interdisciplinary nature: It connects computer science, medicine,
biology and health care, and provides a synergy that goes beyond anything that researchers in any single domain can provide” (para.
3). According to Bioinformaticsweb.tk (2005), “Biomedical Informatics is an emerging discipline that has been defined as the study,
invention, and implementation of structures and algorithms to improve communication, understanding and management of medical
information” (para. 16). This organization then goes on to muddy the water with its description of bioinformatics:
Bioinformatics derives knowledge from computer analysis of biological data. These can consist of the information stored in the
genetic code, but also experimental results from various sources, patient statistics, and scientific literature. Research in
bioinformatics includes method development for storage, retrieval, and analysis of the data. Bioinformatics is a rapidly
developing branch of biology and is highly interdisciplinary, using techniques and concepts from informatics, statistics,
mathematics, chemistry, biochemistry, physics, and linguistics. It has many practical applications in different areas of biology
and medicine. (para. 1)
Biomedical informatics is a growing field, with significant applications and implications throughout the biomedical and clinical
worlds. The authors believe that biomedical informatics is the application of bioinformatics to health care.
Computational biology is the action complement of bioinformatics and, therefore, biomedicine. NCBI (2004) stated:
Ultimately, however, all of this information must be combined to form a comprehensive picture of normal cellular activities so
that researchers may study how these activities are altered in different disease states. Therefore, the field of bioinformatics has
evolved such that the most pressing task now involves the analysis and interpretation of various types of data, including
nucleotide and amino acid sequences, protein domains, and protein structures. The actual process of analyzing and
interpreting data is referred to as computational biology. Important subdisciplines within bioinformatics and computational
biology include:
The development and implementation of tools that enable efficient access to, and use and management of, various types of
information
The development of new algorithms (mathematical formulas) and statistics with which to assess relationships among members
of large data sets, such as methods to locate a gene within a sequence, predict protein structure and/or function, and cluster
protein sequences into families of related sequences. (para. 6)
In 2000, the National Institutes of Health’s (NIH) Biomedical Information Science and Technology Initiative Consortium defined
bioinformatics and computational biology. Members of the consortium stated that “no definition could completely eliminate overlap
with other activities or preclude variations in interpretation by different individuals and organizations” (NIH, 2000, para. 3).
Ultimately, they defined bioinformatics as “Research, development, or application of computational tools and approaches for
expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or
visualize such data” (para. 4). The same group defined computational biology as “The development and application of data-analytical
and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and
social systems” (para. 5).
Bioinformatics tools help biomedical informaticists and healthcare personnel tackle the analysis of large data sets. The authors
believe that biomedical informatics uses bioinformatics, whereas computational biology is its action complement. By using
bioinformatics and computational biology to analyze and interpret intricate biological events, biomedical informaticists promote health
and improve patient care. Biomedical informatics and bioinformatics may seem similar but if one thinks of biomedical informatics as
focusing on health care and patients, it helps distinguish between the two. Biomedical informaticists use bioinformatics methods to
integrate large biological and medical data sets to facilitate understanding of the human body and its biological functioning; these
efforts are geared toward improving health by defeating disease.
Why Are Bioinformatics and Biomedical Informatics So Important?
The future of health care is based on genomics. Bioinformatics and computational biology have provided the tools to make it possible
to analyze and interpret complex biological processes. Through these developments, several projects have advanced understanding of
the human genome, haplotypes, and the genomic changes related to disease.
In 2006, the Cancer Genome Atlas Project began (NHGRI, 2010). This “$100 million pilot will map the genomic changes in brain,
lung and ovarian cancers to assess the feasibility of a full-scale effort to systematically explore the entire spectrum of genomic changes
involved in every major type of human cancer” (para. 1). The goal of this project is to develop a “resource that will be used to develop
new strategies for preventing, diagnosing and treating the disease” (para. 1).
The goal of the International HapMap Project (2006) is to
[D]evelop a haplotype map of the human genome, the HapMap, which will describe the common patterns of human DNA
sequence variation. The HapMap is expected to be a key resource for researchers to use to find genes affecting health, disease,
and responses to drugs and environmental factors. The information produced by the Project will be made freely available.
(para. 1)
This international partnership of scientists has taken blood samples from clusters of related people, such as parents and children,
from different international regions. Using these samples, the researchers have been able to catalog some of the common variations in
DNA and investigate inherited alleles. As the name implies, a haplotype map identifies a set of closely linked alleles on a chromosome
that tend to be inherited together. The International HapMap Project states:
Most common diseases, such as diabetes, cancer, stroke, heart disease, depression, and asthma, are affected by many genes
and environmental factors. Although any two unrelated people are the same at about 99.9% of their DNA sequences, the
remaining 0.1% is important because it contains the genetic variants that influence how people differ in their risk of disease or
their response to drugs. Discovering the DNA sequence variants that contribute to common disease risk offers one of the best
opportunities for understanding the complex causes of disease in humans. (para. 3)
A major contribution has been the Human Genome Project (HGP), which began in 1990 and was completed in 2003 (HGP,
2010). The U.S. Department of Energy and the NIH coordinated this program, which had the following goals:
Identify all of the approximately 20,000–25,000 genes in human DNA
Determine the sequences of the 3 billion chemical base pairs that make up human DNA
Store this information in databases
Improve tools for data analysis
Transfer related technologies to the private sector
Address the ethical, legal, and social issues (ELSI) that may arise from the project (para. 2)
According to NHGRI (n.d.):
[I]t was understood that to meet the project’s goals, the speed of DNA sequencing would have to increase and the cost would
have to come down. Over the life of the project virtually every aspect of DNA sequencing was improved. It took the project
approximately four years to sequence its first one billion bases but just four months to sequence the second billion bases.
(para. 1)
During the month of January 2003, 1.5 billion bases were sequenced. As the speed of DNA sequencing increased, the cost
decreased from $10 per base in 1990 to $0.10 per base at the conclusion of the project in April 2003 (NHGRI, n.d., para. 2).
One of the most important aspects of bioinformatics is identifying genes within a long DNA sequence. Until the development of
bioinformatics, the only way to locate genes along the chromosome was to study their function in the organism (in vivo) or to isolate
the DNA and study it in a test tube (in vitro). Bioinformatics allows scientists to make educated guesses about where genes are located
simply by analyzing sequence data using a computer (in silico) (NHGRI, n.d., para. 8).
BOX 26-2 ESLI QUESTIONS RAISED BY THE HUMAN GENOME PROJECT
Who should have access to personal genetic information, and how will it be used?
Who owns and controls genetic information?
How does personal genetic information affect an individual and society’s perceptions of that individual?
How does genomic information affect members of minority communities?
Do healthcare personnel properly counsel parents about the risks and limitations of genetic technology?
How reliable and useful is fetal genetic testing?
What are the larger societal issues raised by new reproductive technologies?
How will genetic tests be evaluated and regulated for accuracy, reliability, and utility? (Currently, there is little regulation at the federal level.)
How do we prepare healthcare professionals for the new genetics?
How do we prepare the public to make informed choices?
How do we as a society balance current scientific limitations and social risk with long-term benefits?
Should testing be performed when no treatment is available?
Should parents have the right to have their minor children tested for adult-onset diseases?
Are genetic tests reliable and interpretable by the medical community?
Do people’s genes make them behave in a particular way?
Can people always control their behavior?
What is considered acceptable diversity?
Where is the line between medical treatment and enhancement?
Are genetically modified foods and other products safe to humans and the environment?
How will these technologies affect developing nations’ dependence on the West?
Who owns genes and other pieces of DNA?
Will patenting DNA sequences limit their accessibility and development into useful products?
Source: Human Genome Program. (2008). Human Genome Project information: Ethical, legal, and social issues. Retrieved from
http://www.ornl.gov/sci/techresources/Human_Genome/elsi/elsi.shtml
The other major piece brought out through the HGP was the realization that ethical, legal, and social issues (ESLI) arise from
studying human genomes. The participants in the HGP set aside a percentage of their annual budgets to research ESLI (HGP, 2008).
Box 26-2 identifies some of the questions raised regarding ESLI.
Even though the HGP has ended, researchers continue to improve DNA sequencing. Specifically, they continue to advance the
bioinformatics and computational biology tools that are used in biomedical informatics. However, as Butte (2008) laments, “There is
an absolute paucity of people trained to make use of these resources, to build the infrastructure, to ask these novel questions, and to
even answer these questions” (p. 173).
These three projects are pivotal in genomics. The HGP focused on the DNA sequence from a single individual, the HapMap
project focused on variation in the genome and on human populations, and the Cancer Genome Atlas Project is concerned with how
cancer affects the genomes. As a result of these seminal projects and a unique culture of data sharing previously unknown among
biological researchers, molecular data and measurement tools are now publicly available. Two examples of publicly available
databases are the Gene Expression Omnibus, which is maintained by the National Center for Biotechnology Information at the
National Library of Medicine, and Array-Express, which is maintained by the European Bioinformatics Institute (Butte, 2008). As new
researchers with both biology and computational expertise emerge, bioinformatics and computational biology projects will contribute
new insights into disease mechanisms and therapeutic interventions.
What Does the Future Hold?
It will take many more years of researching and applying bioinformatics and computational biology before the information in the
human genome is understood in detail. Because these applications have the ability to allow one to analyze and interpret complex
biological processes, researchers are on the path to understanding the etiology of disease and of treatment interventions at the
molecular level.
Consider a typical day on any clinical unit. The advanced practice nurse who wants to prescribe a drug for a patient begins by
reviewing the patient’s genetic test results. The advanced practice nurse knows that this information must be assessed before
prescribing so that a drug that will treat the patient’s illness successfully without producing harmful side effects can be selected. The
patient will receive only the medication that he or she needs, and one that is designed to interfere with or enhance the specific
molecular processes that are the signature for the patient’s particular health challenge. The advances that bioinformatics and
biomedical informatics promise will dramatically impact healthcare delivery as it is known. As explained by Rajappa, Sharma, and
Saxena (2004):
Understanding molecular mechanisms lead to better classification of disease and better management. A drop of blood from a
hypertensive patient gives gene expression profile by cDNA microarray analysis. It may reveal SNPs [singlenucleotide
polymorphisms] related to hypertension and others which predispose a patient to diabetes mellitus or myocardial infarction
and the clinician can determine which drugs are beneficial and which are harmful. This scenario has a whale of difference
from the current “trial and error” method of matching a patient with antihypertensives. (p. 128)
Nurses can be involved in bioinformatics in many ways, including as nurse researchers helping to map molecular processes and as
educators and advocates helping patients and families to understand these complex biological processes. For more information about
the emerging roles of nurses in this exciting new field, visit the website of the International Society of Nurses in Genetics (ISONG,
2010).
Summary
The focus of this text is on nursing informatics, but one can clearly see the connection between biomedical informatics and nursing
informatics. The discipline of bioinformatics and its use in biomedical informatics epitomize the integration of computer science,
information science, computational biology, and health care. These new applications deal with the resources, devices, strategies, and
methods needed to optimize the acquisition, processing, storage, retrieval, generation, and use of information in health and
biomedicine. Biomedicine and its applications of bioinformatics support and manage all healthcare behaviors. They affect how
clinicians deliver health care to the infirmed, prevent disease, promote health, conduct research, and provide formal education for
entry-level practitioners and continuing education for those who are currently practicing. The field of biomedical informatics—that is,
bioinformatics capabilities coupled with health care—includes informatics and computational biology algorithms and tools and clinical
guidelines. This knowledge can be applied to the areas of nursing, pharmacy, laboratory, dentistry, medicine, and public health. Those
living the profession of nursing know that the practice of nursing is intertwined with the management and processing of information
including the new knowledge being generated by biomedical informatics.
On the biomedical side of informatics, one must be cognizant of the fact that medical data typically are extracted from personal,
confidential, and legally protected medical records. The protection of human subjects must be paramount and all ESLI issues must be
addressed.
Biomedical informatics provides knowledge about the effects of DNA disparities among individuals. Being able to study human
genomes and biological processing at the molecular level will revolutionize how conditions are diagnosed and care is provided. It is
helping to prevent disease. If one can better understand an organism’s biological processes and genetic coding, one can better prevent
or treat medical conditions. Clinical care as it is known will change; it will become genomics based.
THOUGHT-PROVOKING
QUESTIONS
1. After reading this chapter, you know that the study of genomics is helping clinicians to understand better the interaction between genes and the
environment. This new information and knowledge will continue to help clinicians find ways to improve health and prevent disease. How do you
envision patient care will change based on genomics in 10 years, 20 years, or 50 years in the future?
2. Review the ethical, social, and legal issues (ESLI) raised by the Human Genome Project presented in Box 26-2. Prepare a similar list of ESLI
questions to apply to the public health databases being developed for health information exchanges. Can you appreciate how these ESLI
questions are widely applicable to protecting information gathered from human subjects?
References
Bioinformaticsweb.tk. (2005). Bioinformatics resource portal: Bioinformatics definition. http://bioinformaticsweb.net/definition.html
Butte, A. (2008). Translational bioinformatics: Coming of age. Journal of the American Medical Informatics Association, 15(6), 709–714. Retrieved
from CINAHL database.
Gennari, J. (2002). Biomedical informatics defined. http://faculty.washington.edu/gennari/MedicalInformaticsDef.html
Human Genome Program (HGP). (2008). Human Genome Project information: Ethical, legal, and social issues.
http://www.ornl.gov/sci/techresources/Human_Genome/elsi/elsi.shtml Human Genome Program (HGP). (2010).
Human Genome Project information. http://www.ornl.gov/sci/techresources/Human_Genome/home.shtml
International HapMap Project. (2006). About the International HapMap Project. http://hapmap.ncbi.nlm.nih.gov/abouthapmap.html
International Society of Nurses in Genetics (ISONG). (2010). Welcome to ISONG. http://www.isong.org/index.php
Kelly, R. (2008). What are “omics” technologies? http://www.reagank.com/2007/03/what_are_omics_technologies.php
Mulligen, E., Cases, M., Hettne, K., Molero, E., Weeber, M., Robertson, K., Maojo, V. (2008). Training multidisciplinary biomedical informatics
students: Three years of experience. Journal of the American Medical Informatics Association, 15(2), 246–254. PMCID: PMC2274784. doi:
10.1197/jamia.M2488. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2274784/?tool=pubmed
National Center for Biotechnology Information (NCBI). (2004). Bioinformatics.
https://web.archive.org/web/20130901174154/http://www.ncbi.nlm.nih.gov/About/primer/bioinformatics.html
National Human Genome Research Institute (NHGRI). (2010). 2006: The Cancer Genome Atlas (TCGA) Project started.
http://www.genome.gov/25520505
National Human Genome Research Institute (NHGRI). (n.d.). NHGRI: Understanding the Human Genome Project CD-ROM: Mining the genome using
bioinformatics (tutorial). http://www.genome.gov/19519278#al-5
National Institutes of Health (NIH). (2000). NIH working definition of bioinformatics and computational biology.
http://www.bisti.nih.gov/docs/CompuBioDef
National Public Radio. (2010). Where the word genome came from. http://www.npr.org/templates/story/story.php?storyId=128410577
Network Science (NetSci). (2009). Terms and definitions in bioinformatics. http://www.netsci.org/Science/Bioinform/terms.html
Ohio State University Medical Center. (2010). College of Medicine, School of Biomedical Science: Biomedical informatics.
https://web.archive.org/web/20120308121755/http://biomed.osu.edu/bmi/index.cfm
Rajappa, M., Sharma, A., & Saxena, A. (2004). Bioinformatics and its implications in clinical medicine: A review. International Medical Journal, 11(2),
125–129. Retrieved from CINAHL database.
University of Minnesota. (2010). What is bioinformatics? https://web.archive.org/web/20120605125223/http://www.binf.umn.edu/about/whatsbinf.php
University of Texas at El Paso. (2010). College of Science: Bioinformatics. https://web.archive.org/web/20121017124201/http://bioinformatics.utep.edu/
Vanderbilt University. (2010). Department of Biomedical Informatics. https://medschool.vanderbilt.edu/dbmi/
Section VII
Imagining the Future of Nursing Informatics
Chapter 27 The Art of Caring in Technology-Laden Environments
Chapter 28 Emerging Technologies and the Generation of Knowledge
Chapter 29 Nursing Informatics and the Foundation of Knowledge
You might wonder why we are ending this text with a section on caring in the future. The authors believe that nurses are taught to care
and hone their ability to care for patients as they practice; however, in light of technologies that can sometimes be disruptive, caring
can become compromised or lost. We want to refocus nurses on the art of caring, enhancing the science of nursing using informatics
tools.
We challenge you to reflect on what you know and what you are learning and to think of where you are going in relation to your
own practice and nursing informatics knowledge. Just as our professional and personal lives overlap at times, so do our social and
professional informatics and networking experiences. We cannot assume that what we do or use in our personal lives is appropriate or
even useful in our professional practice. This section begins with a chapter (The Art of Caring in Technology-Laden Environments)
that considers the heart of what nurses do—caring.
The next chapter, Emerging Technologies and the Generation of Knowledge, looks at our technological history and progresses to a
discussion of the tools that we use in our professional and personal lives, including e-portfolios. This examination addresses emerging
technologies and the generation of knowledge. Although not explicitly at each point addressing the four key areas of the Foundation of
Knowledge model, the sections of this chapter do consider how emerging technologies may address the four areas of (1) knowledge
acquisition, (2) knowledge processing, (3) knowledge generation, and (4) knowledge dissemination and feedback. We also do not
focus explicitly on changes in health care and nursing technologies, but rather on the more general changes that might be adapted or
adopted for use by nurses or within health care.
The final chapter, Nursing Informatics and the Foundation of Knowledge, refocuses what you have learned about the many facets
of nursing informatics and the interfacing of nurse knowledge workers and technology. The Foundation of Knowledge model provides
a framework for examining the dynamic interrelationships among data, information, and knowledge used to meet the needs of
healthcare delivery systems, organizations, patients, and nurses. The importance of knowledge management in nursing is emphasized
by taking this one last opportunity to ensure that the reader understands and appreciates the value of knowledge management in the
nursing profession and the role that technology has in knowledge acquisition, knowledge generation, knowledge dissemination, and
knowledge processing.
Nursing informatics entails the synthesis of nursing science, information science, computer science, and cognitive science to
manage and enhance healthcare data, information, knowledge, and wisdom for the dual betterment of patient care and the nursing
profession. After reading the first six sections of this book, you should have a good idea of the current state of the science of nursing
informatics. Now this final section challenges you to think about the future. Each reader should envision his or her current practice
setting and the nursing informatics applications he or she uses. What will come next? What should come next?
The material within this text is placed within the context of the Foundation of Knowledge model (Figure VII-1) to meet the needs
of healthcare delivery abounds with others’ thoughts and information, whereas wisdom is focused on one’s own mind and the
synthesis of one’s own experience, insight, understanding, and knowledge.
Figure VII-1
Foundation of Knowledge Model
Source: Designed by Alicia Mastrian.
Reflect on the model while reading through this final section. You are challenged to ask, “How can I use my wisdom to help create
the theories, tools, and knowledge of the future?”
Chapter 27
The Art of caring in technology-Laden environments
Kathleen Mastrian and Dee McGonigle
OBJECTIVES
1. Explore caring theories as they apply to the art of nursing.
2. Acknowledge the potential disruption of technology to the therapeutic nurse–patient relationship.
3. Define presence and caring presence.
4. Formulate strategies to enhance caring presence.
Key Terms
Active listening
Art of nursing
Bracketing
Caring
Caritas processes
Centering
Presence
Introduction
Nursing is hard work. Depending on the site of practice, it can be both physically and mentally taxing. Nurses are masters at
multitasking—that is, performing several caring functions simultaneously during a patient encounter. Some nursing interventions are
readily apparent and easily described, such as collecting vital signs data and changing dressings, while others are less visible yet
equally important, such as interpreting the vital signs data, generating knowledge about the patient’s situation, and then using that
knowledge to inform practice. Equally invisible, yet important to the therapeutic caring environment, are the little things that nurses,
say, project, and do in the caring episode. In this chapter, we pause to reflect on the art of caring. We emphasize the need to preserve
this central and unique function of nursing and suggest ways that nurses can ensure that the caring functions do not become a lost art as
technologies are introduced into patient care environments.
In the Nursing Science and the Foundation of Knowledge chapter, we derived a definition of nursing science from the American
Nurses Association’s definition of nursing. Nursing science is the ethical application of knowledge acquired through education,
research, and practice to provide services and interventions to patients to maintain, enhance, or restore their health, and to acquire,
process, generate, and disseminate nursing knowledge to advance the nursing profession. Caring functions, such as therapeutic
communication, listening, touch, and mindfulness, are an integral part of nursing science, as they also help patients to maintain,
enhance, or restore their health. While the new technologies such as smart pumps, bar-code medication administration systems,
electronic health records (EHRs), and smartphones being introduced into our practice environments are designed to increase
efficiency, promote safety, and streamline the work of nursing, we need to ask: To what extent do these technologies disrupt the nurse–
patient caring encounter? How can we continue to care effectively for our patients and promote a healing environment while
incorporating the advantages and efficiencies that technologies provide?
Caring Theories
Let us begin our exploration of caring as a concept with the work of Jean Watson. As Dr. Watson described:
The Theory of Human Caring was developed between 1975 and 1979 while I was teaching at the University of Colorado. I
tried to make explicit that nursing’s values, knowledge, and practices of human caring were geared toward subjective inner
healing processes and the life world of the experiencing person. This required unique caring–healing arts and a framework
called “carative factors,” which complemented conventional medicine but stood in stark contrast to “curative factors.”
(Watson & Woodward, 2010, p. 352)
It is important to remember that Watson developed her theory during a time when the nursing profession was struggling to define
itself and identify the unique contributions of nursing to patient care. In the theory of human caring, Watson defined caring as “healing
consciousness and intentionality to care and promote healing” and caring consciousness as “energy within the human–environmental
field of a caring moment” (Watson & Woodward, 2010, p. 353). Think about the use of the word “energy” in these definitions and
pause to appreciate the level of cognitive energy that nurses expend as they care for patients. Nursing is hard work!
Watson further described the evolution of her theory from the original 10 carative factors to what she now calls caritas processes.
As her work expands, she is recognizing the need for “love and caring to come together for a new form of deep transpersonal caring.”
In the evolving theory, she has emphasized that the “relationship between love and caring connotes inner healing for self and others”
(Watson & Woodward, 2010, p. 353). The 10 caritas processes enumerated by Watson are summarized here:
1. The practice of loving kindness and equanimity within the context of caring consciousness
2. Being authentically present and enabling and sustaining the deep belief system and subjective life world of self and one being
cared for
3. Cultivation of one’s own spiritual practices and transpersonal self, going beyond ego self, opening to others with sensitivity
and compassion
4. Developing and sustaining a helping–trusting, authentic caring relationship
5. Being present to, and supportive of, the expression of positive and negative feelings as a connection with deeper spirit of self
and the one being cared for
6. Creative use of self and all ways of knowing as part of the caring process; to engage in artistry of caring–healing practices
7. Engaging in genuine teaching–learning experience that attends to unity of being and meaning, attempting to stay within others’
frames of reference
8. Creating a healing environment at all levels (a physical and nonphysical, subtle environment of energy and consciousness,
whereby wholeness, beauty, comfort, dignity, and peace are potentiated)
9. Assisting with basic needs, with an intentional caring consciousness, administering “human care essentials,” which potentiate
alignment of mind–body–spirit, wholeness, and unity of being in all aspects of care, tending to both embodied spirit and
evolving spiritual emergence
10. Opening and attending to spiritual–mysterious and existential dimensions of one’s own life–death; soul care for self and the
one being cared for (Watson & Woodward, 2010, p. 355)
Think about a recent patient encounter. Were you fully present in the moment and conscious of the individual and his or her
uniqueness? Did you smile and greet the patient by name and acknowledge visitors? Did you attentively listen to the concerns of the
patient and family and offer them the opportunity to ask questions? Did you explain what you were doing with and for the patient, and
why? Conversely, did you push the workstation into the room, focus your attention on the computer screen, and talk at the screen as
you clicked on the drop-down menus to document the patient encounter? Did the workstation create a barrier between you, the patient,
and the patient’s family? Was your assessment of the patient’s current situation colored by the objective representation of the person
created by the monitoring technologies present in the room (O’Keefe-McCarthy, 2009)? “The overwhelming presence of technology at
the clinical bedside has the power to become the strongest reference point that nurses use to inform, direct, interpret, evaluate, and
understand nursing care” (p. 787). We must remember that “Technology, however, does not take into consideration the specific
symptom presentation unique to the person experiencing the illness. Technology’s use is not meant to replace the person-to-person
interaction that is essential in any nurse–patient encounter” (p. 792). Central to the caritas processes described by Watson and the
discussion about technology-mediated care by O’Keefe-McCarthy is the concept of a caring presence. Strategies for developing and
enhancing caring presence are discussed in the latter part of this chapter.
The humanistic nursing theory developed by Paterson and Zderad also offers some insight into the less visible aspects of nursing
care (Kleiman, 2010). These authors suggested that the basis of nursing is the response to the call for help in solving healthrelated
concerns.
This call, a foundational concept of humanistic nursing, can be heard where nursing is offered, coming to our attention as a
subtle murmur of pain, sorrow, anxiety, desperation, joy, laughter, even silence, that expresses the state-of-being of the
protagonists in the drama of health-care delivery, our patients and ourselves. (Kleiman, 2010, p. 338)
Nurses hear the call and respond with their entire being. Their knowledge, experiences, ethics, and competencies shape the interaction
with the patient as they respond.
In humanistic nursing we say that each person is perceived as existing “all-atonce.” In the process of interacting with patients,
nurses interweave professional identity, education, intuition, and experiences, with all their other life experiences, creating their own
tapestry which unfolds during their responses. (Kleiman, 2010, pp. 341–342)
Nursing care requires conscious awareness of self and the uniqueness of each of our patients. It requires emotional energy
expenditure as we seek to find ways to meet the calls of our patients. We need to be aware of the potential for inadvertently
dehumanizing the patient experience in our technology-laden practice environments. According to Kleiman (2010), “The context of
Humanistic Nursing Theory is humans. The basic question it asks of nursing practice is: Is this particular intersubjective–transactional
nursing event humanizing or dehumanizing?” (p. 349). We must be fully present and self-aware in every patient encounter, seeking to
deliver exactly what is needed in every situation. Yes, nursing is hard work, but when we are able to respond with our whole being, we
may find that our patients and families are more satisfied with the care we provide and that we also experience personal satisfaction
and find joy in our profession.
Presence
Presence is the act of being there and being with our patients—fully focusing on their needs. “Presence is an interpersonal process that
is characterized by sensitivity, holism, intimacy, vulnerability and adaptation to unique circumstances” (Finfgeld-Connett, 2008, p.
528). Penque and Snyder (2010) defined three types of presence: physical presence, full presence, and transcendent presence. A nurse
who is physically present is largely competent in carrying out care, efficient with interventions, but inattentive to communication and
nonverbal cues projected by the patient and family. When fully present, a nurse will greet the patient by name, communicate
appropriately with the patient, and pay attention to what is being said and not said during the encounter. When nurses practice
transcendent presence, they will first center themselves, clearing their mind of all potential distractions, and then use the patient’s
name and gentle touch to convey interest and responsiveness while carrying out the necessary physical interventions.
Strategies for Enhancing Caring Presence
Nursing is hard work. Our patients have complex problems and needs. They may be scared, angry, resistant to change, or happily
oblivious to the extent of their health challenges. We, too, have complex personal lives with many competing roles and issues that
consume our energies. Our workplace may be short-staffed, resulting in care assignments that stretch us to our maximum. We may be
struggling to learn to use the new technologies that are introduced nearly daily into our practice environments. As a result, we may feel
disorganized, tired, angry, and emotionally spent.
We need to take care of ourselves first so that we can be effective in our patient and family care. Caring for ourselves involves
conscious attention to our health and health practices. Do we eat a balanced diet, get appropriate exercise, and get enough sleep? Do
we have strategies to manage stress appropriately, and do we have adequate social support? “Just as surgical instruments are washed
and sterilized before being used for another procedure, professionals who are instruments of healing must identify therapies that will
ensure the cleansing of self so as to provide a personal energy field and space that facilitates healing for self and patients” (Leonard &
Towey, 2010, p. 26).
As part of a concepts of health course, students are asked to develop a personal health plan and journal periodically during the
semester about their ability to stick to the plan. Here is an example of a simple plan: “I will increase my intake of fruit and vegetables
and walk outside for 30 minutes at least 3 times per week.” As the students reflect on their ability to stick to the plan in the journal,
caring for self is brought into conscious awareness. This simple self-reflective practice may be just the boost that is needed for a nurse
to commit to self-awareness and self-care on a long-term basis.
One additional strategy that we share with students is a breathing/meditative exercise from Tai-Chi, Qi Gong called the five-
element breathing sequence. This meditative exercise, which can be performed in less than 10 minutes and can be very energizing and
stress reducing, is described in Box 27-1.
The simplest and perhaps most effective strategy we can use to help us be fully present to our patients is to pause to take a few
deep breaths to calm ourselves and clear the clutter from our minds before we address each patient. It also helps to repeat the patient’s
name silently a time or two before we enter the room. This practice, known as centering, enables the nurse to “be available with the
whole self and be open to the personal and care needs of the patient” (Penque & Snyder, 2010, p. 40). When we are with a patient, we
need to be certain that our mind is fully engaged in the interaction with this patient for the moment. We must be fully attentive to the
patient, be both physically and mentally present, meet the patient where he or she is emotionally, listen actively to what the patient is
saying, focus on the nonverbal cues the patient is projecting, touch the patient gently and reassuringly, and demonstrate acceptance
(Penque & Snyder, 2010; Zerwekh, 2006).
BOX 27-1 TAI-CHI, QI GONG FIVE-ELEMENT BREATHING SEQUENCE
1. Stand with your feet shoulder-width apart. Relax your arms and shoulders.
2. Inhale slowly as you straighten and then move both arms slightly back and then up with palms facing up (as though you are gathering a giant ball
of energy). Stretch to your full height as you inhale.
3. Exhale slowly as you press both palms down in front of you with hands slightly cupped and thumbs and index fingers nearly touching. Bend your
knees slightly to sink down as you exhale.
4. Turn your hands over (palms up) just below the waist, and inhale slowly as you raise your hands in front of you, to chest height. Straighten your
legs as you inhale. (Imagine lifting a ball of energy.)
5. Exhale slowly and extend the arms directly in front of you, chest high, and fan your hands open to release the energy in front of your chest. Bend
the knees slightly to sink down as you exhale.
6. Inhale slowly, with palms facing you, to gather the energy back toward your chest. Straighten your legs as you inhale.
7. Press both arms straight out at shoulder height as you exhale. Pretend you are pushing on walls located on either side of you. Continue the
exhalation as you bring your arms in front of you. Bend your knees slightly to sink down as you exhale.
8. Inhale slowly as you gather the energy to your chest. Straighten your legs as you inhale and stretch to your full height.
9. Exhale slowly as you raise your arms above your head to set the energy free. Bend your knees slightly to sink down as you exhale.
10. Transition your hands to the beginning to repeat the sequence by inhaling as you bring your hands halfway down and exhaling the rest of the
way. (You can also end here by bringing your palms together in closure, first inhaling and then exhaling as you slowly move your hands down in
front of you.)
Source: Visit the following website for a demonstration of the five-element breathing: http://www.youtube.com/watch?v=KtVCCLlkcKg.
A related and similar concept for practicing presence, bracketing, is described in humanistic nursing theory. Consider, for
example, that a nurse experienced in caring for elders with congestive heart failure will have expectations and preconceived ideas
about what he or she will find in a patient situation. These expectations may not allow us to really “see” the whole patient and his
experience of the illness. “Bracketing prepares the inquirer to enter the uncharted world of the other without expectations and
preconceived ideas” (Kleiman, 2010, p. 343). We need to come to know our patients both intuitively and scientifically. Our
technologies provide an objective view of the patient, and the nurse synthesizes this view with his or her own perspective that is based
on the nurse’s experience, education, and intuition as applied to the patient’s situation. This is the essence of caring.
One of the first skills we were taught in our basic nursing education programs was active listening. We were taught to get down to
the same level of the patient, make eye contact, touch gently (if culturally acceptable), listen attentively and nod appropriately, restate
and clarify what we heard, ask questions to seek additional information, listen for feelings that are not being explicitly stated, and use
silence to encourage the patient to think and provide additional information to us (Watanuki, Tracy, & Lindquist, 2010; Zerwekh,
2006). These communication skills are fundamental to caring. When was the last time you sat in a chair at a patient’s bedside to get to
the same level as the patient? Even a brief sit at the bedside can communicate volumes about your availability and willingness to
listen, and it certainly feels good to get off of your feet for a moment. We also need to think carefully about the potential barriers to
active listening that technology might present. For example, what are some of the ways that these caring presence skills could be
adapted for use in a telehealth encounter? What are the challenges of communicating at a distance, yet being fully present for the
patient?
We conclude our discussion of caring presence with a definition of the art of nursing provided by Finfgeld-Connett (2008):
The art of nursing is the expert use and adaptation of empirical and meta-physical knowledge and values. It is relationship-
centered and involves sensitively adapting care to meet the needs of individual patients. In the face of uncertainty, creativity is
employed in a discretionary manner. Artful nursing promotes beneficent practice and results in enhanced mental and physical
well-being among patients. It also results in professional satisfaction and personal growth among nurses. (p. 528)
Let us all strive for beneficent practice that atones for the potential disruptions to the therapeutic nurse–patient relationship that our
use of technology produces.
Reflective Practice
As professionals, we should be constantly mindful of the need for practice improvement. One way to focus on our practice is to engage
in reflective journaling. In the concepts of health course, we ask students to complete a reflective practice assignment over a 6-week
period. Students are directed to review concepts of caring presence and active listening and to commit to consciously using a strategy
for 6 weeks. At 3 and 6 weeks, they are asked to complete the following reflective journal entry:
1. Write a brief description of the presence and therapeutic communication approaches you tried in your practice for the last 3
weeks. Provide specific examples of patient situations in which you tried the approach.
2. Reflect on the following: What did you do well?
Which behaviors and skills do you need to improve?
How did you feel about the experience as it was happening? Did you plan thoroughly?
Did you achieve your objectives?
Which aspects of planning do you need to improve?
How will this experience affect your future practice?
3. Which personal professional development needs have you identified after reflecting on your performance? Which strategies
will you use to address these needs?
Our students frequently report that they enjoy this experience and that the exercise helps to remind them why they were originally
attracted to nursing. They describe experiences where they felt an authentic connection to the patient. They also report that after 6
weeks of consciously practicing the strategy, it becomes a part of their daily practice. Centering is the most frequent strategy that the
students choose to practice.
Summary
Nursing practice relies on information and communication technologies that receive inputs from the nurses as well as all of the patient
care technologies. Computers, handheld devices, monitors, and other healthcare technologies are essential tools for nurses. Therefore,
the nurse must have the ability to implement, monitor, and evaluate all of this equipment based on its inputs and outputs. The increased
demands on the nurse make it easy to lose sight of the patient amid all of these technologies. Nurses must look at monitors, devices,
and other gadgets to receive information; oftentimes, it is easy to forget the patient is at the core of our care.
We hope that this brief overview of caring presence prompts you to be more mindful of your practice and that you, too, will
commit to employing strategies that enhance your caring presence in all patient encounters. We do not want our patients to feel that we
are more focused on the machines that they are connected to or the workstations that we bring with us to the patient encounter. Yes,
technology is great and it does help us collect meaningful data and generate knowledge about our patient situations, but equally
important is the need to collect the human-to-human data that become available only when we step away from the technology and
interact authentically with our patients.
When you save a person’s life—they call you a hero
When you blend science with caring—they call you an expert
When you share your compassion—they call you a friend
When you do all three—they call you a nurse
—AUTHOR UNKNOWN
THOUGHT-PROVOKING
QUESTIONS
1. Examine each of the 10 caritas processes developed by Watson. Describe an example of a patient encounter that demonstrates the use of each
caritas process.
2. Reflect on your personal health. Are you a role model for your patients? Which aspects of your personal health do you need to improve? Which
strategies will you adopt to improve your health?
3. Choose a caring presence strategy to implement in your practice and use the reflective journal template provided in the chapter to reflect on your
practice.
References
Finfgeld-Connett, D. (2008). Qualitative convergence of three nursing concepts: Art of nursing, presence and caring. Journal of Advanced Nursing,
63(5), 527–534. doi: 10.1111/j.1365-2648.2008.04622.x
Kleiman, S. (2010). Josephine Paterson and Loretta Zderad’s humanistic nursing theory. In M. Parker & M. Smith (Eds.), Nursing theories and nursing
practice (3rd ed., pp. 337–350). Philadelphia, PA: F. A. Davis.
Leonard, B., & Towey, S. (2010). Self as healer. In M. Snyder & R. Lindquist (Eds.), Complementary and alternative therapies in nursing (6th ed., pp.
25–34). New York, NY: Springer.
O’Keefe-McCarthy, S. (2009). Technologically-mediated nursing care: The impact on moral agency. Nursing Ethics, 16(6), 786–796.
Penque, S., & Snyder, M. (2010). Presence. In M. Snyder & R. Lindquist (Eds.), Complementary and alternative therapies in nursing (6th ed., pp. 35–
46). New York, NY: Springer.
Watanuki, S., Tracy, M. F., & Lindquist, R. In M. Snyder & R. Lindquist (Eds.), Complementary and alternative therapies in nursing (6th ed., pp. 47–
59) New York, NY: Springer.
Watson, J., & Woodward, T. (2010). Jean Watson’s theory of human caring. In M. Parker & M. Smith (Eds.), Nursing theories and nursing practice (3rd
ed., pp. 351–368). Philadelphia, PA: F. A. Davis.
Zerwekh, J. (2006). Connecting and caring presence. In Nursing care at the end of life: Palliative care for patients and families (pp. 113–130).
Philadelphia, PA: F. A. Davis Company.
Chapter 28
Emerging Technologies and the Generation of Knowledge
Peter J. Murray, W. Scott Erdley, Dee McGonigle, and Kathleen Mastrian
OBJECTIVES
1. Outline the history of technology development and informatics applications.
2. Describe some of today’s state-of-the-art technologies.
3. Predict the evolution of technology and its impact on knowledge generation in nursing.
The future is here. It’s just not evenly distributed…
—William Gibson, novelist and visionary
Key Terms
Blogs Change
Cloud storage
Community of practice (CoP)
E-portfolio
Electronic data interchange
Emerging technologies
Evidence
Folksonomies
Futurologists
Genomics
Half-life of knowledge
HealthVault
Information mediator
Lab-on-a-chip device
Mobile e-health (m-health)
Nanotechnology
Podcasts
Privacy
Professional networking
Really simple syndication (RSS)
Reflective commentary
Relational databases
Social bookmarking
Social media
Social networking
Tags/tag clouds
Transistor
Transparent technology
Ubiquitous
Ubiquitous health (u-health)
Virtual peers
Virtual worlds
Wearable computing
Web 2.0
Web publishing
Weblogs Wetware
Wikis
Introduction
Anyone foolhardy enough to try to predict the future holds himself or herself hostage to fortune as soon as the words are documented.
Many nursing colleagues have written about the future of nursing and about the challenges and changes that will affect nursing or that
nursing might influence. The authors do not, therefore, claim to be doing anything radically new in this chapter; in the best tradition of
“standing on the shoulders of giants” (especially in view of the increasing tendency of many to use tools, such as Google Scholar, for
finding much of their literature), some of their works are used as a launch pad for explorations of the issues.
The authors have, for several years, been specifically working on projects and making conference presentations about some of the
emerging technologies that are both in general, everyday use and in use and development within health care; they have also written
and spoken at conferences about what the future might hold for nurses, nursing, and health care more generally. In addition, they were
fortunate, along with nursing colleagues from around the world, to be able to explore many of these issues during the NI2006 Post
Congress conference (Murray, Park, Erdley, & Kim, 2007), from which they continue to draw and develop a number of the ideas and
issues presented and discussed in this chapter.
This chapter presents an optimistic view of the future—one that some critics might say is unwarranted. It envisages a future with a
high degree of continuing technologic development and general worldwide economic growth, and an absence of some of the
possibilities that might bring much of the global infrastructure crashing down. The authors assume there will not be widespread global
social breakdown; the predicted possible pandemics of avian and swine influenza and other problems will not happen; and catastrophic
climate change will not produce as rapid effects as the worst prognostications suggest. However, the authors are aware many of these
things could happen, to some degree or other.
Change is coming, and much of it is far enough advanced as to be virtually unavoidable. Demographic changes are occurring—
changes to population age structures, increasing rates of dementia, and crises in funding of health care and retirement (Development
Concepts and Doctrine Centre [DCDC], 2010)—that will have a profound impact on all aspects of society, health, and care.
Technology will not solve all future problems; some emerging technologies could create more problems than they solve. However, it is
by having an awareness of likely and possible technological developments that one can assess how they may affect the ways in which
we work and live, and whether the many issues can be addressed.
Although not explicitly addressing the four key areas of the Foundation of Knowledge model, the sections of this chapter do
consider how emerging technologies may address (1) knowledge acquisition, (2) knowledge processing, (3) knowledge generation,
and (4) knowledge dissemination and feedback. We also do not focus explicitly on changes in health care and nursing technologies,
but rather consider the more general changes that might be adapted or adopted for use by nurses or within health care. The reader will
also note that much of the literature cited here comes not from peerreviewed academic journals, but rather from more popular or
journalistic sources. Among the major reasons for this preference are the rapidly evolving nature of the changes and discussions of the
issues addressed, and the long lead-in time for scientific publication, which means that many of the issues have generated little
scientific exploration to date.
One of the most important issues for nursing informatics in the future, and one facing health care in general, is the vast amount of
information available to everyone.
Knowledge is growing exponentially (Siemens, 2005), and in many disciplines its useful life span can be measured in months,
coupled with an ever-decreasing half-life (halflife of knowledge is the time span from when knowledge is gained to when it becomes
obsolete). Many people believe there is now information overload; however, the present situation is nothing compared to the future
amount of data and information that will have to be acquired, from new forms of patient monitoring, from advances in genomic
medicine, and from the continuing explosion of knowledge generation and publishing. Additionally, all of the data and information
need to be stored and manipulated if they are to be of use. Perhaps the key challenge for the future is to identify which emerging
technologies will best provide the usable tools that nurses need to manage the knowledge explosion.
Looking Back from the Future
Visions of societies of the future, both utopias and dystopias, have been produced by many people. Sometimes they have been science
fiction writers, whereas futurologists (e.g., DCDC, 2010) have used scenario planning to examine possible alternatives for the future
and consider how they might be achieved or prevented and the implications for society. This technique was used in part in the NI2006
Post Congress workshop (Murray et al., 2007), but other useful scenarios that demonstrate possible emerging technologies also exist.
Easteal and Demosthenes (2005) postulated one such future containing some interesting and already very plausible possibilities.
Some brief examples of the kinds of what-if thinking that may be helpful in exploring the implications of emerging technologies
include the following:
“Googlezon” (formed from a merger of Google and Amazon) used factstripping robots to create personalized news stories
dynamically for delivery to health information portal subscribers (for patients, nurses, or family caregivers).
Personalized “genoming” (the sequencing of an individual’s genome) was a routine procedure.
Biomedical research institutes had only executives and intellectual property lawyers as their permanent staff, with all the actual
research outsourced and conducted under contract in India and China.
“GoogleHealth” allowed individuals to store privately or publish their own health information, combining diverse information
provided by patients, their own monitoring devices, and records supplied by hospitals, pathology and imaging services, and the
general practitioner.
GoogleHealth’s search engines and social networking spaces allowed people to explore information held in virtual populations
of individuals who were genetically and behaviorally like themselves, so they could see health trends in these populations that
had real and direct meaning to their lives and adopt the best practices of their virtual peers, so as to derive the same health
benefits.
In 2005, when this scenario was written, these were speculative ideas. However, just 2 years later, Nobel laureate James Watson,
codiscoverer of the DNA double helix and father of the Human Genome Project, become the first human to receive the data
encompassing his entire personal genome sequence (Williams, 2007). Microsoft’s HealthVault, along with Google Health, are now
actual public online personal health record systems. Personalized information feeds using such tools as really simple syndication
(RSS) now provide easy ways for people to receive the latest information from a range of multimedia sources that they can readily
specify and customize. Search engine technology, combined with the forms of open information-sharing and tools underpinning social
networking websites (e.g., Facebook), could rapidly support the sharing of health behavior information. The future will be here more
rapidly than many expect, and nurse informaticians need to be at least aware of, if not directly involved in determining, the many new
emerging technologies and their possibilities for use within their domains of interest and practice.
Historical Overview
There are several ways to view the explosive growth of information technologies and applications (Saba & Erdley, 2006; Sackett &
Erdley, 2002). Regardless of perspective, such growth tends to follow a long and often convoluted path from simple to complex in
detail. An in-depth examination reveals the significance of these developments. Initial generations of computers (analog devices)
focused on military uses, such as cryptography and weapons flight paths. Evolution proceeded from hardware to software for business
administration and accounting tasks. The invention of transistors (solidstate semiconductors) resulted in the second generation of
computers, digital devices that were much smaller and faster than analog computers. Silicon chips, smaller and more powerful than
transistors, eventually replaced transistors, advancing the process of miniaturization. Future directions point toward a shift to advanced
miniaturization (nanotechnology) for central processing unit (CPU) and data storage capabilities and increased ubiquity of
computational devices in general (Kurzweil, 2005).
One may also view computing history using eight time groupings, or demarcations, beginning with the pre-1800s. Sackett and
Erdley (2002) presented a schema based on this model; their specific designations are pre- to mid-1800s, later 1800s, 1900–1950s,
1960s, 1970s, 1980s, 1990s, and early 21st century.
Pre-1800 computing primarily encompassed many basic forms of human communication (e.g., speech, oral history, hieroglyphics)
(Sackett & Erdley, 2002). Examples of ancient tabulation devices include shells, rocks, and pieces of bone. Despite the individual
impact of such devices as the abacus, growth and advancement of such devices was essentially influenced by the mobility of people
and dispersion of ideas from the Old World to the New World (Sackett & Erdley, 2002).
The 1800s witnessed technological developments such as Babbage’s difference engine in England (seen by many as the
predecessor of the modern computer) and the invention of the telephone by Alexander Graham Bell. Bell’s invention eventually
provided the initial infrastructure on which computer connectivity began. From a software focus, a development of large significance
was the first foray into standardized language (Dr. Peter M. Roget, author of Roget’s Thesaurus of English Words and Phrases, based
on synonyms, in 1852).
From the early 1900s through the 1950s, a number of hardware developments emerged with respect to computing, including the
Z1, Colossus, Mark I, Electronic Numerical Integrator and Computer (ENIAC), and Universal Automatic Computer (UNIVAC). In
health care, one of the first computers to be used was the IBM 704, which also was one of the first computers to use magnetic core
memory (invented by Wang) (Collen, 1995). The invention of transistors and their subsequent integration into computational devices
also helped shape the future of computing. Additional important developments include storing and running applications, a stored
program concept by Neumann, and shift to a binary system from the traditional base 10 (Collen, 1995).
The next decade (1960s) witnessed a substantial increase in activity and inventiveness providing important direction and substance
to future computational needs. Transistor use in computers marked the end of first-generation computers and the beginning of the
second generation. Kilby and Noyce’s discovery of the ability of transistors to function as their own circuit boards greatly diminished
the size and power requirements of computers, thereby leading to minicomputers (Collen, 1995). Noyce and his colleagues also
developed the first integrated silicon chip, which eventually led to the use of very-large-scale integration (VLSI) chips in the late
1960s (Collen, 1995). Noyce went on to found Intel in the late 1960s, which grew into one of the world’s leading manufacturers of
computational and other chips. These integrated circuits demarcated the introduction of second-generation computers (Blum, 1986;
Saba & McCormick, 1986).
Software applications grew substantially during this time. The Health Evaluation through Logical Processing (HELP) system was
developed at the Latter Day Saints/LDS Hospital in Salt Lake City, Utah, in the late 1950s and early 1960s (Warner, Olmsted, &
Rutherford, 1972). In 1962, Clark and Molnar of Massachusetts Institute of Technology (MIT) developed LINC (a laboratory-specific
application) (Collen, 1995). The National Library of Medicine committed to computerization when it placed its journal collection,
Medlars/Medline, onto a computer in 1963 (Collen, 1995). PLATO, from the late 1960s, was one of the first computer-based
education programs. In health care, a number of landmark applications appeared, including the Technicon Medical Information System
(TMIS), initiated in 1965; the problem-oriented medical record (POMR) and problemoriented medical information systems (PROMIS)
in the 1960s; and the computer-stored ambulatory record (COSTAR), developed and implemented in the late 1960s using the
Massachusetts General Hospital utility multiprogramming system (MUMPS) (Barnett et al., 1979).
In the 1970s, the focus of information technology in health care shifted away from hardware to software applications (Boutros,
1993; Brandejs, 1976; Collen, 1995; Elioutina & Tarasov, 1995; Hannah, Ball, & Edwards, 1999; Mandil, Moidu, Korpela, Byass, &
Forster, 1993). The invention and subsequent use of the silicon chip in 1975 promoted a worldwide revolution in computer hardware
and subsequent software developments. Shortly after this development, Paul Allen and Bill Gates founded Microsoft Corporation,
which was destined to create an operating system of eventual global importance.
Internationally, from Canada and the United Kingdom, examples of computerized information systems in nursing practice became
evident. King’s College Hospital in London pioneered computerized nursing care plans; Ninewells Hospital in Scotland implemented a
real-time system for nursing documentation; and York Central Hospital in Richmond Hills, Ontario, implemented a computerized
patient care system (Hayes & Barnett, 2008; Sackett & Erdley, 2002).
In the United States, hospital information systems (HISs) began adding care management and cost control functionality in the
1970s. Edgar F. Codd’s work with relational databases provided the framework and underpinnings for growth in data collection,
manipulation, and analysis (Haux, Knaup, & Schmucker, 1999). Examples of other United States HISs include Technicon at El
Camino, California (a continuation of work from the mid-1960s), and PROMIS (Medical Center Hospital, Burlington, Vermont)
(Collen, 1995); the refinement of PROMIS continued during this decade. The Patient Care System (PCS) developed at Duke Medical
Center focused on nursing staffing. MYCIN, a program designed to identify bacteria and recommend antibiotics (so named because of
the typical suffix of antibiotics at the time), formally recognized the inexact reasoning in medicine (Shortliffe & Buchanan, 1975).
The decade of the 1980s witnessed continued growth in both the application and the hardware arenas. The shift in computer focus
from mainframe architecture to individual machines signaled a shift of power to the user. Hardware and software growth became
collaborative in use and purpose. The U.S. Defense Agency Research Projects Agency (DARPA) network was charged with
developing means to maintain communication in the event of an atomic war. The concept of an ethernet suggested by Bob Metcalf
defined a form of communication over wires, which, along with developments in communication protocols, such as Transmission
Control Protocol/Internet Protocol (TCP/IP), provided the framework for not only necessary communication but future collaborations
(e.g., client–server architecture).
One of the more prominent software developments in the healthcare field in the early 1980s was Internist-1 (Miller, People, &
Myers, 1982). This application applied symbolic reasoning to develop computer-assisted diagnoses by modeling physician behavior as
a decision support aid (van Ginneken, Koenderink, & Dana, 1999). Other examples following in Internist-1’s footprint include QMR
(Internist-1’s direct successor), DxPlain, Iliad, and Meditel. A hospital information systems project, based on the MUMPS, was
initiated at Obafemi Awolowo University Teaching Hospital in Ile-Ife, Nigeria, in 1988 (Daini, Makanjuola, & Ojo, 1993).
Technology applications “exploded” in the 1990s, with the advent of increasingly sophisticated hardware and software, cheaper
memory and speed, and the decreased cost of digital computer technology. From the development and use of the Internet to satellites
to wireless innovations, from mainframes to minicomputers to microcomputers to workstations to global networks, the amount of
knowledge generated and the necessity for management of increasingly complex information systems became evident everywhere and
was especially noticeable in health care.
International examples of information systems include CARE Telematics Project, led by the World Health Organization (WHO)
regional office for Europe (Zollner, 1995) and OPADE: Optimization of Drug Prescription Using Advanced Informatics (de Zegher et
al., 1995), a joint venture between individuals from Belgium, France, Italy, Sweden, and the United Kingdom. TELENURSING was a
European Commission-funded project
… to promote standardized and formalized clinical nursing care data based on uniform definitions of data items with the
purpose of developing countable and comparable nursing minimum data sets as a means of communicating nursing care
information electronically between clinical setting, health care sectors nationally and European-wide and a means of
producing Euro-Nursing Health statistics on people’s need for nursing care. (Mortensen & Nielsen, 1995, p. 115)
Several healthcare initiatives were also undertaken in South America (Stammer, 2000), including swipe cards to validate patient
identity and eligibility at public health centers.
As the healthcare market has become increasingly complex, so has healthcare software. The number of software applications for
health care is in the thousands, with programs available for any and all purposes. In the last years of the 1990s, healthcare informatics
was swept up by both the emergence of the Internet and the lure of the World Wide Web. The increased popularity of electronic data
interchange (EDI), with its ability to reduce human errors, forever altered the financial aspects of health care. Carrying through to the
early 21st century, this trend has continued, with pledges to “computerize the nation’s health records in five years, saving billions of
dollars in healthcare costs and countless lives” (White House, 2009). Regionalism and interoperability are now the buzzwords for
healthcare informatics both in the United States and increasingly abroad. Denmark has provided a health portal for its citizens and
health providers, which is accessible to all and can handle healthcare concerns ranging from scheduling an appointment to reviewing
laboratory results (Danish eHealth Portal, 2008). Second-generation Web applications (Web 2.0) are increasing in popularity for both
health care and the population in general in the United States and worldwide. Thus knowledge and information continue to evolve
from specific uses by individuals to growth, evolution, and application by the population as a whole. This shift will continue to impact
the role of providers and health care in general.
This shift of information access, acquisition, and interpretation, for both provider and patient, may result in overload in terms of
content and comprehension. Managing this overload will increasingly consume both providers and patients. How this issue will be
managed—whether by an increased use of technology or a regression away from technology—is of great concern.
Some Technologies of Today
In many countries (not just the United States), much of the most recent focus of technology use in nursing and health care has been
aimed at improving patient safety (e.g., reducing medication errors), as described by Oren, Shaffer, and Guglielmo (2003), among
others. They point out, however, that only limited research provides evidence of the impact of introducing these technologies on error
reduction and the reduction of adverse events. The recent effort in the United States directed toward developing meaningful use of
electronic health records (e.g., Blumenthal, 2010) has reemphasized this focus. This perhaps reflects two interacting sets of issues: (1)
priorities for use of technologies often change in conjunction with changing political forces, and (2) many technologies are changing
so rapidly that, by the time they are evaluated in large-scale, controlled trial environments, they have been succeeded by a new
generation of devices and applications. Patient safety issues are a strong driver to using technology today and will probably continue to
be so as long as politicians see benefit from such a focus; however, as other priorities—often political—emerge, the focus will shift.
One can already begin to see some of this changing focus with the emerging discussions on personal health records. In just one
example of the plethora of materials on personal health records (PHR) and personal health information management systems (PHIMS),
the Post-Congress Workshop of the 10th International Nursing Informatics Congress (NI2009) explored a range of perspectives from
multiple countries on the state of the art of development of PHIMS and some of the emerging issues that need to be addressed,
including safety and confidentiality, patient engagement and empowerment, and standards and governance issues (Saranto, Brennan, &
Casey, 2009).
However, many nurses often see technology as a hindrance or a barrier, rather than as something that provides real benefit within
their practice. Murray et al. (2007), for example, state, “Much technology currently gets in the way of care, and there is a need for
increasingly transparent technology [boldface added], which will need to function in the background; if you talk about the
technology, it is not transparent” (p. 18). This lack of transparency may exist for a wide variety of reasons but is often related to lack of
consultation with end users about technology application to care processes. The true potential of technology to support nursing and
health care will be realized only when it is both ubiquitous (everywhere) and transparent. The point has been made by Weaver et al.
(2007), who noted that “small, inexpensive, unobtrusive” (p. 80) devices will facilitate future monitoring of patients and data
collection. Kirovski, Oliver, Sinclair, and Tan (2007) suggest that the current generation of wearable physiologic sensors are obtrusive
and so meet resistance from users. If they are to gain widespread acceptance, these devices need to provide for little or no change in
the user’s daily routine, with transparency being the key to widespread adoption.
Even the most cursory examination of the proceedings of any recent health or nursing informatics conference reveals the plethora
of ways in which nurses are currently using a wide range of technologies to support and advance their practice. One can also begin to
see adoption of the kinds of near-transparent and ubiquitous technologies just discussed. Taking examples from recent events, by way
of illustration, from the NI2006 International Congress on Nursing Informatics (Park, Murray, & Delaney, 2006), one can find
examples from around the world of the following:
Computerized decision support systems to prevent unnecessary visits to healthcare facilities in South Africa (Horner, Hanmer,
& Mbananga, 2006)
Wireless speech recognition and touchscreen triage support systems in emergency departments in Taiwan (Chang et al., 2006)
Nurse-managed telehealth services in United Kingdom dermatology clinics (Lawton & Timmons, 2006)
Use of open source software for development of Web-based nursing informatics education in Germany (Schrader, 2006)
Nurse-led development of a personal health record system in the United States (Lee, Delaney, & Moorhead, 2006)
Demonstrated uses of wireless biomedical sensors for invasive monitoring in Norway (Oyri, Balasingham, & Hogetveit, 2006)
More recently, the NI2009 International Congress on Nursing Informatics (Saranto, Brennan, Park, Tallberg, & Ensio, 2009)
provided examples of technologies to support online knowledge management for e-health, use of virtual environments for education
and clinical practice, and mobile e-health applications.
Two reports focusing on nursing and technology also summarize some of the practical ways in which nurses are involved in using
technologies. The report of the February 2007 Technology Informatics Guiding Education Reform (TIGER) Initiative summit defined
a 3-year action plan geared toward achieving a 10-year vision to enable practicing nurses and nursing students to engage fully in the
unfolding digital era of health care, thereby enabling nurses to use information technology (IT) seamlessly to provide safer, higher
quality patient care (TIGER, 2007). Nine different collaborative teams were formed to initiate Phase II, to address topics ranging from
standards and interoperability to personal health records. Each committee submitted a report of its findings. TIGER is currently
entering Phase III, which entails integrating the recommendations elicited in Phase II into the nursing community at all levels of care
and the development of online learning management systems to provide content delivery and interaction. Among the examples of
current use cited were Web-based patient health records, such as the U.S. Department of Veterans Affairs’ MyHealtheVet
(http://www.myhealth.va.gov), which patients can access from anywhere, and which empowers patients in making their own decisions
about their health.
A 2004 report on technology’s role in addressing the nursing crisis in Maryland (Technology Workgroup, Maryland Statewide
Commission on the Crisis in Nursing, 2004), noting technology was not the solution to issues, acknowledged it could be a facilitator.
This role was demonstrated by pockets of innovation, including the following:
The use of wearable, hands-free communication badges (Vocera, 2007)
Bar-coded medication administration schemes that have demonstrated reductions in error rates
Use of intranets for providing current and consistent protocol and procedure information
In addition to the technologies currently available, changes are occurring in the ways in which these technologies facilitate
interaction, in particular through the emergence of a wide range of what are termed “Web 2.0” applications. Web 2.0 has many
definitions and descriptions. O’Reilly (2005) first described it extensively, and many more recent descriptions continue to focus on
interaction, the development of online communities, and support for collaboration and sharing as being the key elements of Web 2.0.
Among the technologies seen as contributing to Web 2.0 are blogs (weblogs), wikis, podcasts, RSS feeds, and other methods of
providing many-to-many publication and communication. Social networking and social educational applications (Anderson, 2005),
such as Facebook and Twitter, which facilitate interaction and collaboration, are also central components of Web 2.0 (Murray & Maag,
2006).
Web 2.0 technologies allow for the possibility of increased participation among formal and informal online communities of users.
As such, they are disruptive technologies that have the potential to transform the ways in which knowledge is generated and shared.
There is not space within this chapter to explore at length the emerging tools and technologies generally labeled Web 2.0, nor to
provide a detailed discussion of their potential implications. Nevertheless, they are undoubtedly beginning to have profound impacts
on the ways in which data, information, and knowledge (and perhaps even wisdom) are generated, so it is worth summarizing some
emerging ways in which these elements are being used within nursing and health care. Detailed research studies on their use and
effects remain relatively sparse, although there are emerging case study reports of their use in clinical practice and education (e.g.,
Anderson et al., 2010).
Maag (2006) described the use of podcasts of lecture materials with nursing students (the term “podcast” originated as an amalgam
of broadcasting and Apple’s proprietary iPod MP3 music player, although podcasts can be played on any computer or MP3 player).
Although much argument persists regarding whether podcasts will provide long-term benefits and changes to ways in which people
learn, some evidence shows that they can enhance the learning experience and engage learners.
Maag (2005) is also among those who have explored the potential use of blogs within nurse education, and she cites examples of
http://www.myhealth.va.gov
small-scale evaluations of their use within other educational contexts for teaching and research purposes. She concluded that health
professionals’ writing, reading, and communication skills can be enhanced through the use of blogs, and that blogs facilitate
collaborative and information-gathering skills. Other practical examples support the use of blogs within formal education (Godwin-
Jones, 2003; Martindale & Wiley, 2004) for professional development, sharing information, interacting as part of a learning
community, and building an open knowledge base. In addition, they have been used for encouraging informal knowledge sharing and
professional development around health and nursing informatics conferences (Murray & Ward, 2004).
Spallek, O’Donnell, Clayton, Anderson, and Krueger (2010) have explored the use of Web 2.0 tools—or, as they are increasingly
called, social media applications—in clinical practice, and through a series of vignettes have illustrated some current uses and
identified research questions. The emergence of social bookmarking, tags and tag clouds, and folksonomies are providing
opportunities to explore new ways of linking, ordering, and searching information, based in large part on popularity of their use among
a community. These tools have implications for the ways in which health and nursing informatics are classified. They also influence
the shared learning processes of communities of practice (CoPs), as these Web-based tools can facilitate their development and
support their continuation. These CoPs that form on the Internet are known as online communities of practice (OCoPs). Refer to Box
28-1 to learn more about CoPs.
BOX 28-1 COMMUNITIES OF PRACTICE
Glenn Johnson and Jeff Swain
Revised by Dee McGonigle and Kathy Mastrian
Developing and sustaining a community of practice in the work environment is beyond the main scope of this text. However, educators (in
particular) and nurses need to understand how the effective collaboration skills that learners acquire in learning communities during their formal
education may promote the development of CoPs in students’ future professional lives. As the Web-based tools continue to evolve, they are
supporting online communities of practice, also known as virtual communities of practice. The following is a brief overview of this important and
emerging trend for work in the knowledge era.
Wenger (2006) defines a CoP as follows: “Communities of practice are groups of people who share a concern or a passion for something they do
and learn how to do it better as they interact regularly” (para. 3). He suggests that CoPs have three important characteristics: (1) the domain, (2) the
community, and (3) the practice. Wenger defines a domain as a shared interest about which people are passionate and for which they possess some
competence related to the domain. The community aspect relates to the joint activities that are undertaken by the group to build relationships and
knowledge about the domain. The practice aspect denotes that the members of the community are practitioners of something and through the CoP
develop and share resources, knowledge, and solutions to problems. The recent improvement of Internet-based communications technologies has the
potential to promote the global expansion of CoPs.
Nursing is a profession that is particularly well suited to the development of CoPs, especially in light of the new knowledge generated and
disseminated every day. The many nursing LISTSERVs are evidence of the growing trend toward sharing knowledge in a CoP. Caring (now ANIA),
a nursing informatics organization, has a very active LISTSERV through which members post questions to other members and request information
and experiences related to informatics. For example, a member of the LISTSERV might ask others about their experiences related to a specific issue
in implementing a bar-code medication administration system or about training procedures for implementing an electronic health record.
One of the difficulties of using a LISTSERV as a CoP is that the discussion is not moderated; many threads could be running simultaneously. An
example of a well-organized and global CoP is the Cochrane Collaboration (http://www.cochrane.org/), an international organization that promotes
and supports collaboration among healthcare professionals to develop evidence-based practice recommendations for healthcare interventions. A
nursing practice council within a healthcare organization is another example of a CoP. In the future, it is hoped that the use of CoPs in nursing will
grow beyond knowledge sharing and promote more knowledge discovery and sense making.
REFERENCE
Wenger, E. (2006). Communities of practice: A brief introduction.
https://web.archive.org/web/20130704222806/http://www.ewenger.com/theory/communities_of_practice_intro.htm
Some Views of What Will Affect the Future
Several nursing informaticians have concluded their books by looking to the future and imagining scenarios for the life and work of
the 21st-century nurse. McCormick (2001), writing a possible scenario for 2020, envisaged the nurse using voice recognition to
interact with his or her computer (portable, of course) and with components of the hospital systems, telehealth applications for
physiologic monitoring, use of genetic screening information in treatment decisions, and smart cards for storing a form of electronic
health record. Today (well before 2020), of course, many of these applications have become almost routine. The growth of networks
(especially the Internet) means that we have already moved beyond some of the ideas in our development of ways of working.
Ball (2005) also advocates the use of technology, including wireless networks and personal digital assistants (PDAs), to reduce the
time spent on documentation (and so potentially increase the time available for direct patient care). She perceives technology as
supporting new roles, such as use of an Internet guide to assist patients in accessing appropriate educational resources. McCormick et
al. (2007), in discussing the American Medical Informatics Association (AMIA) Nursing Informatics Work Group’s white paper on
how the nursing informatics profession needs to set new directions, identified many areas that will influence the nature of nursing and
health care in the future, although many of these were nontechnological.
Issues
Theme and tracks of the 2012 International Congress on Nursing Informatics: Advancing Global Health Through Informatics revealed
a wide variety of topics (AMIA, 2012). The presentations and posters covered the impact of informatics on quality care, policy,
research, and professional practice. The importance of attending these conferences derives from the sharing of timely information and
knowledge that occurs during such gatherings. Some of the major accomplishments of the 2012 meeting resulted from increased
Internet connectivity and development of informatics tools that can help us reach out to underserved populations needing care. At other
times, however, we may discover that our issues are much the same from year to year, with only minimal progress being made in
certain areas, such as patient safety.
Some of the Issues from NI2006
In mid-2006, following the NI2006 International Congress on Nursing Informatics in Seoul, Korea (Park et al., 2006), a group of nurse
informaticians from around the world participated in an intensive workshop-based exploration of possible futures and the implications
for nursing and nursing informatics. Some of the issues that they identified are worth exploring here. During the 2006 workshops, it
was acknowledged that nursing and nursing informatics would have to change. Among the new roles for nurses, arising out of current
developments, was that of information mediator, but the implications of this were, again, the need for technology to support
immediate access to up-to-date knowledge anywhere and anytime.
Many of the issues that were explored were not technological or related to information technology. Instead, discussions explored
the many demographic changes that are known to be occurring or predicted to occur and that have been discussed by others exploring
future trends (e.g., growing and aging populations; changes in population structure, especially the numbers of productive working-age
members able to contribute tax and other revenues to support healthcare systems; growing proportions of populations living in urban
areas; and shifting global balances of power) (DCDC, 2010). Possible trends in healthcare provision and delivery as health policies and
priorities change (e.g., moves toward less costly preventive or interventional measures, growing use of telehealth) and the impact that
these and other changes might have on the nature of nursing were also discussed.
Among the key issues that emerged from the discussions that are important for the international nursing informatics community to
explore in coming years were the following:
The development of the concept, and a possible model, of u-nursing (ubiquitous nursing), which has implications for the
practice of nursing and profound implications for all aspects of the education and continuing professional development of
nurses (Oyri et al., 2007). One area that showcases the professional development of nurses is the e-portfolio (Box 28-2).
The role of the nurse changing to become more of a knowledge professional, working in partnership with patients and their
other caregivers.
The continuing growth of patient informatics, perhaps as an outgrowth and evolution of the current concept of consumer health
informatics and with the increasing centrality of the patient as the controlling force in the entire enterprise.
The vast impact that genomics will have on all aspects of life, and in particular health care (Turley, Murray, Saranto, Ehnfors,
& Seomun, 2007).
Such discussions of technologies as emerged often focused on the interaction with these other changes. Among the issues
discussed were nanotechnology; remote (e.g., wireless) monitoring of vital signs; wearable monitoring and treatment devices;
ubiquitous access to computer networks; lifelong personal and electronic health records; and treatments, not only preventive and
interventional, but also predictive of likely development of diseases, based in genetic medicine (Oyri et al., 2007).
Nanotechnology and its specific applications within medicine and health care (nanomedicine) open up many possibilities,
including the development of minute biomedical devices the size of molecules that could provide drug delivery systems, precise
targeting of therapies (e.g., delivery of drugs or radiation sources to individual cancer cells), or even nanorobots to undertake surgical
procedures on cells. These types of technology are still in development stages. One project is the lab-on-a-chip device for undertaking
comprehensive blood analyses (Schmidt, 2007). Staggers et al. (2008) note that because nanotechnology has potential applications in
drug delivery devices and monitoring mechanisms, it has implications for the education of healthcare professionals about many issues,
including the safe and ethical use of nanomaterials and their capabilities and limitations. Nurses and informaticians need to consider
what, if any, would be the nursing or informatics role in such interventions. As Loescher and Merkle (2005) point out in relation to
other emerging technologies, without knowledge of genomic technologies, reasons for their use, and the possible implications of
genetic diagnosis and genomic-based treatments, nurses will be increasingly unable to provide the quality of care needed and
demanded by patients. The same caveat applies to all new and emerging technologies that may impact on or become integral parts of
the delivery of health care, in whatever setting.
BOX 28-2 WHAT IS AN ELECTRONIC PORTFOLIO?
Glenn Johnson and Jeff Swain
Revised by Dee McGonigle and Kathy Mastrian
Today’s information technology infrastructure allows users to easily build Web-based collections that include evidence of their knowledge and
skills. Users can upload artifacts that represent evidence of their learning experiences both inside and outside of the classroom. Electronic portfolios
(e-portfolios) may also contain a blog element where students reflect on their total experience and demonstrate growth in their areas of study.
E-portfolios can be built using a range of different technologies. Some individuals use PowerPoint presentations to capture and present evidence.
Web-based e-portfolios are built using common Web publishing tools to create webpages, such as Web 2.0 or open source tools; the webpages are
then published on the Internet (Barrett, 2012). In addition, an increasing number of institutional e-portfolio systems have emerged through which
users can log in and then upload, enter, and share information or evidence related to their experiences. Examples of such systems include the
following:
Association for Authentic, Experiential and Evidence-Based Learning: http://www.aaeebl.org
Digication: http://www.digication.com
Epsilen: http://www.epsilen.com/Epsilen/Public/Home.aspx
Facebook: http://www.facebook.com
iWebfolio: http://www.iwebfolio.com
LiveText: http://www.livetext.com
MySpace: http://www.myspace.com
PebblePAD: http://www.pebblepad.com
TaskStream: https://www.taskstream.com/pub
TypePad: http://www.typepad.com
WHY CREATE AN E-PORTFOLIO?
Although academic institutions may use e-portfolios for assessment of student learning, for the individual, e-portfolios are all about opportunity.
Such opportunities might include supporting a working relationship with a mentor, networking with other professionals, or representing certain
qualities and characteristics to prospective employers. In all of these cases, having gone through the process of developing an e-portfolio requires
critical examination of which qualities make individuals who they are and why these qualities are important to them and their profession. It is
important for all professionals to have a foundational understanding of where they are in their career trajectory and how this fits their long-term
professional goals.
Practically speaking, e-portfolios are efficient. When introducing oneself in an e-mail message, a self-starting individual who has taken the
initiative to develop and publish an e-portfolio can add this line to the message: “Here is a link to my e-portfolio.” The recipient can click on this
link, which automatically opens that individual’s e-portfolio in a web browser. Metaphorically, the senders of such messages have just walked into
the recipient’s office with information that illustrates who they are, what they know, what they can do, and what they value as important; they have
just walked in with what could be a multimedia showcase of their qualities. The Internet is a very powerful communication medium, and individuals
with professional e-portfolios are simply taking advantage of this fact.
E-PORTFOLIOS IN POSTSECONDARY EDUCATION
As an instructional strategy, portfolios have been around for a long time. Instructionally, portfolios, whether electronic or paper based, require
students to demonstrate or provide evidence that they have attained specific learning outcomes. For instance, in the arts, portfolios have been used to
demonstrate the depth and breadth of the work of an artist. Although performance-based programs of study are more likely to be familiar with the
concept of the portfolio as a demonstration of what a student knows and can do, other areas of study have also begun to adopt this method of
assessment.
Portfolios can be particularly helpful in areas where higher-level thinking and analysis are essential. For instance, being a good nurse
encompasses much more than simply being able to get high scores on examinations. Nurses need to be able to collect information, analyze the
information presented, relate it to past experience, apply related knowledge, and evaluate various options, and from this present a diagnosis and a
plan of action. In short, nurses need to be able to think critically and make informed decisions. In learning to become a nurse, portfolios can be used
to capture, support, and improve this type of thinking as it develops.
Like the artist, the nursing student can connect, share, and present cases and findings and include with this evidence the reflective commentary
that serves to unveil how he or she arrived at a decision, which information or experiences were vital, and how his or her action plan evolved.
However, given the vast variety of evidence that individuals might potentially use to represent themselves, what should one select and how should
this be shared?
E-PORTFOLIOS FOR PROFESSIONAL DEVELOPMENT
Using an e-portfolio to support professional networking involves a predetermined and focused purpose. This purpose may be to foster better
communication between oneself and a mentor, or it may be to establish how what a nurse is doing fits with the goals of the institution or perhaps an
institution for which the individual would like to work. A professional e-portfolio is evidence based and uses this evidence to make a case that
highlights the individual’s capacity not only to perform but also to grow and develop professionally within his or her chosen field.
THE E-PORTFOLIO PROCESS
The four steps involved in developing an e-portfolio are recursive in nature, meaning that during the process one can backtrack to fill in missing
pieces or reevaluate earlier decisions that were made. The four steps are (1) collect, (2) select, (3) reflect, and (4) connect.
Collect
Evidence should demonstrate what a person knows, what he or she can do, or the values that the person holds as being important. When it comes to
developing e-portfolios, it is important to think of evidence in very broad terms. This evidence might include the results of what someone has learned
in courses taken as a student, especially in terms of demonstrating a new skill or increased knowledge of a subject. More importantly, evidence can
come from experiences that take place outside of the classroom. For instance, someone may have been involved in an internship or clinical
observation where he or she had the opportunity to connect what was learned in the classroom with how this information is applied in a real-world
setting. Not only is such an experience valuable, but it also represents the individual’s understanding of how this knowledge can be applied; thus it
enhances others’ perception of the depth of what the person knows.
Résumés are evidence documents. They are very important, and every professional should have an updated copy available. However, résumés
simply list an individual’s experiences or accomplishments. E-portfolios, by comparison, go beyond the résumé to emphasize personal attributes that
are very important in the nursing profession. These attributes include, but are not limited to, interpersonal skills, leadership skills, appreciation of
diversity, ability to work in a team, and self-sufficiency. These attributes are difficult, if not impossible, to demonstrate in a résumé. When reflective
commentary accompanies evidence of an individual’s involvement, these attributes and values can become the highlights of an e-portfolio.
Select
Everyone has his or her own unique pool of evidence from which to pull, and over time this evidence pool can become quite large. What will
someone choose to feature and why? Putting together a professional e-portfolio requires that two intertwining questions related to purpose and
audience be addressed.
What is the purpose? What is it that someone is attempting to gain by putting an e-portfolio together? Is the purpose related to personal
development (i.e., feedback and advice about the professional direction that is being taken)? Is the purpose to connect with colleagues? An individual
may, for example, be interested in using his or her e-portfolio to find a job or to gain admission into a graduate program.
Although an e-portfolio can link to everything that a person has accomplished, this may not be the best strategy. Instead, it is essential that an
individual consider the audience and establish a plan that enables the person to select the most appropriate pieces of evidence for his or her particular
purpose and audience. A helpful way to start is to select the top five pieces of evidence that support the plan. Next, the individual should consider
why he or she selected these pieces of evidence. What it is about each piece of evidence that makes it representative of who the individual is, what he
or she knows, what he or she can do, and what he or she values as important.
Reflect
Reflection and reflective commentary take an e-portfolio to the next level. This component may take the form of a single reflective statement or it
may be attached to the evidence throughout the e-portfolio. Reflective comments should open up a window into why an individual thinks this
evidence is important, the ways in which the individual values what he or she learned, or why the person thinks it is important for the larger
profession. For instance, the individual may present an experience where he or she was challenged to provide assistance. Describing this experience
would be important; however, the reflective comments can extend this description, enabling the person to talk about the alternatives considered as the
basis for how he or she made a decision to provide the specific type of assistance and the manner in which it was provided. By itself, a description of
https://www.taskstream.com/pub
http://www.typepad.com
this experience is good. With reflective comments, readers have a much more thorough perception of and insight into an individual’s professional
thinking. This is where having a blog element as part of an e-portfolio becomes extremely powerful.
Unlike static webpages, a blog page is a space designed to be interactive. The blog owner posts commentary, thoughts, and experiences for others
to read and respond. Regular entries on a blog give others a reason to return to one’s e-portfolio site over and over again. It is an opportunity to share
one’s perspective on topics of interest and critical to the chosen field. A blog is a place where conversation happens. It provides a nice
counterbalance to the static webpages, such as a résumé and project pieces.
Most blogging platforms allow users to select from a range of templates that include a blogging element along with static webpages. Such
platforms as WordPress, MoveableType, and Google are free for at least the basic service, enabling the user to create a dynamic e-portfolio without
having to build webpages. Most platforms allow entries to be in both text and multimedia format, so the blog becomes the perfect place for personal
expression. A blog is quickly becoming a standard part of an e-portfolio.
Connect (Connections) and Feedback
The connection and feedback step is important to validate the assertions someone makes about what it is he or she knows, understands, or values.
Individuals may choose to receive feedback from those who are close to them and from here reach out to others who may provide different
perspectives. For instance, if a nurse was thinking about using his e-portfolio to apply for a position, he might want to start by first getting feedback
from friends and family. He might also share his e-portfolio with a mentor or faculty, raising the bar by getting professionally grounded feedback
before sharing the e-portfolio with a prospective employer.
CHALLENGES AND ISSUES: PRIVACY AND SECURITY
The ease and popularity of both Web-based social networking and professional e-portfolio tools also raises several challenges and issues for users.
Never before has information for individuals been so accessible, and never before has such personal information been so readily made public. For
this reason, issues related to privacy and security need to be addressed. What might be appropriate socially can be deadly in a professional context.
E-PORTFOLIO PROCESS: SUMMARY COMMENTS
In summary, one might think about the process of developing a professional e-portfolio as boiling down to the telling of a rather simple story, albeit a
story that has three parts: looking back, looking around, and looking ahead. Readers should think of their own evidence pool as they answer these
questions:
1. Looking back: What have you done? In which activities and with which organizations have you been involved? Where have you been?
With whom have you worked? How did this help get you where you are today?
2. Looking around: In what are you currently involved? Why are you doing this? What are you getting out of it?
3. Looking ahead: Where would you like to be in 2 years? Where would you like to be in 5 years? Why do you feel this way? What makes you
think your goals are realistic?
REFERENCE
Barrett, H. (2012). ePortfolios. http://electronicportfolios.org/eportfolios/index.html
Although this chapter focuses on the technologies and can only summarize discussion of the issues, the technologies cannot be
divorced from or considered in isolation from their wider societal issues and the handling of increasingly large and complex volumes
of data. Readers are directed to the Post Congress Conference proceedings for more detailed discussion (Murray et al., 2007).
Among the issues explicitly identified and implicitly emerging from the NI2006 discussions was the tremendous increase in the
amount of data available about all aspects of health care. Genomics, proteomics, and the plethora of related “omics,” many of which
are often gathered in the general field of bioinformatics, will produce many challenges for the nature of health care, and for all forms
of health informatics. As Martin-Sanchez et al. (2004) have suggested, many technological challenges will arise, but wider ethical and
other issues will also need to be addressed. These authors point out that, although new genetic and proteomic data provide the
possibility of developing new therapies and of implementing new preventive measures, it is important to be able to deal with the large
amounts of data generated by use of such technologies. The new knowledge available and the new technologies will blur the
distinctions between clinical and molecular information, while increased amounts of knowledge will lead to a greater need for genetic
counseling and its emergence as an even more important part of clinical, hands-on care. Martin-Sanchez et al. see a place for culture
brokers (i.e., people who can translate between science and clinical care and between science and the self-caring citizen). As Turley et
al. (2007) also point out:
… from the genome information, we can determine the disease risks of a given patient, the drug allergies of that patient and to
some extent the optimal drug treatments for individual patients. These all fall clearly within the purview of nursing concerns
and the science of nursing, and have implications for truly personalized care. (p. 56)
Other issues will also emerge from this trend, such as the nature and amount of genomic data and information to be included in the
electronic health record and the length of time for which it must be held, as well as a set of issues around the impact of the changes and
emerging technologies on education for all health professionals. The necessity of collecting, storing, and manipulating huge amounts
of genomic data will influence the technologies to be used. Grid technologies (unique configurations of distributed computing) are
being explored as mechanisms for manipulating the data, but such issues as how the knowledge will be extracted from the data
repositories and how it will be represented in usable form present challenges for the development of new technologies (Kuhn, Wurst,
Bott, & Guise, 2006).
If, as many of the participants in the NI2006 Post Congress Conference suggested, one of the major roles of the nurse (and the
nurse informatician) of the future will be that of a clinical knowledge worker (Weaver et al., 2007), then nurses will need to be familiar
with the many new tools and technologies that will enable them to retrieve, use, manipulate, present, and share the new knowledge
with patients and other health professionals. The next section specifically explores some technologies and probable trends, but because
new applications are being developed on a seemingly daily basis, it does not dwell on any options in detail, but explores some of the
broader trends.
Some Emerging Technologies and Other Issues That Will Impact Nursing and Health Care
For many years, the concept of “e-health” has existed—a term for which Oh, Rizo, Enkin, and Jadad (2005) have identified 51 unique
published definitions. These definitions address not only health but also technology and commerce, with a general tendency for health
care to be viewed as a process rather than health as an outcome, and with technology seen both as a tool to enable a
process/function/service and as a means to expand, to assist, or to enhance human activities rather than as a substitute for them. The
European view of e-health, as developed by the Commission of the European Communities (2004), acknowledged the need for
providing citizen-centered healthcare systems, respecting the diversity of Europe’s multicultural, multilingual healthcare traditions.
Examples of e-health developments include electronic health records, health portals, telemedicine and telehealth services, and
wearable and portable monitoring systems.
The evolution of m-health (often termed mobile e-health, as opposed to simply “mobile health”), which according to some (e.g.,
WHO, 2007) includes health-related uses of mobile technologies including mobile telephones (generally referring to smartphones),
PDAs, tablet computers and sub-notebook microcomputers, remote diagnostic and monitoring devices, global positioning systems
(GPS) and geographic information system (GIS) mapping equipment. The 5th Annual mHealth Summit in 2013 reflects the interfacing
of technology, business, research, policy and healthcare (HIMSS, 2013).
M-health can be described in terms of the application to healthcare systems and processes of network technologies and mobile
communications and devices (Istepanian, Laxminarayan, & Pattichis, 2006). The development and potential applications of m-health
have been facilitated in recent years by the rapid rise in ownership and availability of mobile devices (cell phones, smartphones, PDAs,
and more recently, tabletsized devices, such as the iPad) in many countries. There are now many examples, especially within
developing countries in Africa and Asia, of m-health applications in such areas as AIDS/HIV monitoring and education (Michael
2009; Murray, van Middleton, & Meyer, 2010). There is growing evidence of its impact on underserved populations and of its
transformation of healthcare delivery in resource-poor environments (Akter & Ray, 2010).
More recently, recognizing the increasing pervasiveness of technology in all aspects of life, including health care, the concept of u-
health has emerged. U-health (or ubiquitous health or health care) is based in the concept of ubiquitous computing, which is
considered a third wave of computing wherein technologies become increasingly invisible, becoming incorporated into everyday use,
and so fading into the background (Weiser, 1991). With truly ubiquitous computing, which pervades all parts of the everyday
environment, unobtrusively providing information and services, and with the very concept of the single device (computer)
disappearing into the network (Bott, 2005; Trivedi, Sagar, & Vernon, 2010), the real possibility of providing nurses and other
healthcare professions with the information they need where and when they need it exists.
Oyri et al. (2007) suggest information and communications technologies will become ubiquitous within nursing, and that emerging
technologies including nanotechnology, wireless sensors, and minimally invasive technologies will support not only health care but
also wellness management. They suggest a model for u-nursing that “will focus on the provision of nursing for anyone or any
organization, anytime, anywhere, through any networks and any devices” (p. 32). According to Trivedi et al. (2010), a ubiquitous
network consists of
[A] federation of networks on which user-oriented services are provided anywhere and anytime to a target user in the most
appropriate way with null operational cost. A ubiquitous network allows all users to access and exchange information of any kind
freely at any time, from anywhere. (p. 72)
Linked to these concepts has been the emergence of a considerable amount of research, especially funded by the European
Commission, into body sensor networks—in particular, wireless body area networks (WBANs) (Lymberis, 2010). These allow for data
from a patient’s or person’s vital signs and movements to be collected by wearable or implantable sensors. The data, communicated
via short-range wireless technology, can provide for real-time monitoring of health status (Liolios, Doukas, Fourlas, & Maglogiannis,
2010; O’Donovan et al., 2010; Santra, 2010).
Clinical decision support systems are being used throughout health care and continue to evolve. They rely on a knowledge base or
machine learning to analyze the clinical data. By 2013, data mining concepts had become reality to improve nurses’ practice and
patient care. The goal in the future is to streamline data analysis while increasing decision support. Elevation of the level of the
analysis is also occurring; knowledge analytics are blending applicable knowledge with analytics (Slonim et al., 2012). Clinical
decision support tools are integrating clinical data analytics and data mining, so as to enhance business intelligence and foster better
healthcare decision making, thereby improving patient outcomes.
Nanotechnology (touched on briefly earlier in this chapter) is a broad term that has been developed and refined over the past few
years. Originally focused on exceptionally small-scale technology, typically on the order of atoms, it has evolved to incorporate more
than strictly engineering and structural meanings, such as its application to patient sensing, as mentioned by Oyri et al. (2007). The
potential for this technology is now becoming realized as clinicians are increasingly developing applications and investigating
potential uses in medicine, such as disease detection or treatment (Kenwright & Pifer, 2010; Me-too, 2009).
There is also discussion of whether society is shifting from a focus on information to a focus on knowledge. Generally considered
to have succeeded the Industrial Age, the era known as the Information Age is characterized by information use resulting in wide-
ranging social activity. This trend is manifested by the increasing popularity of applications, such as Facebook, MySpace, and Twitter.
These and other such social media applications are increasingly becoming tools for health organizations and patients.
Beyond Web 2.0
General consensus sees the Web as evolving into something beyond its current status of 2.0. The current nomenclature, as initially
bestowed by O’Reilly, lends itself to a sequential progression to 3.0. Web 3.0 is considered to be the semantic Web. In essence, Web
3.0 is a term used to define the many different ways for machines (computers) to understand words, and thus their meaning, on the
Internet. These different ways are applications formally proposed by the World Wide Web Consortium. Web 3.0 is slowly gaining
popularity but is not as common as Web 2.0.
Even the concept of Web 4.0—that is, the iteration after 3.0—has been already discussed on the Web (e.g., Murray, 2009). This
concept is even less clearly defined than Web 3.0, with many definitions having been proposed; however, it was formally presented at
NI2009 with a subsequent blog posting. The concept has ranged from wetware (direct body–brain interfaces) to implantables, such as
ocular viewing lens and more. It may seem without purpose to even conjecture about a concept like Web 4.0 and beyond, but how can
the future be known if we do not try to envision plausible aspects of it?
A Technology Wish List
There are doubtless many other technologies that might be covered, and some that readers will say should have been covered. Not
addressed here are the prospects for robotics in delivering aspects of care, even though there are well-documented examples of robotic
surgery and even robots for delivering nursing care. Similarly, not explored here are the possibilities of emerging virtual worlds, such
as Second Life, and simulations. Also unaddressed are many of the technologies, such as wearable devices, that are being used to
provide remote monitoring of physiologic parameters in care settings, including patients’ own homes. These are certainly interesting
and exciting developments, and one can only plead the excuse of lack of space for omitting them. Readers are urged to undertake their
own explorations of these and other technologies.
A short wish list for some new technologies that the authors would like to see emerge in the next few years concludes this
exploration. As with much of what was discussed previously, the seeds of these developments are already present, and they may be
with us sooner than expected. Our technology wish list includes the following items:
1. The kind of computer interface used in the film Minority Report: no mouse, no keyboard—just gesture-based interactions with
virtual images projected into a vertical space at head height.
2. Ubiquity of computational devices to the extent that conversation and discussion of these devices will disappear from everyday
social interactions; the technology will be transparent or invisible.
3. The ability to access information when and where it is wanted or needed, irrespective of modality, thereby maximizing
mobility and other personal resources. This may lead to cell phones becoming the main avenue of access for more than text
messages, speech, and video clips. Knowledge acquisition and use will then become ubiquitous and pervasive; new work roles
will emerge to cope with this new and different technology.
Summary
The future will be different; however, it is likely that many of the changes mentioned here are already in development or will be
extrapolations of current developments and trends. Of course, there is always the possibility of new developments, or unexpected
consequences of the development of emerging technologies.
This chapter has touched briefly on some of the emerging technologies that are likely to have an effect on nursing, nurse
informatics, and health care in the near future, and that might modify and interact with the Foundation of Knowledge model. Some of
the technologies have not been given as much coverage as hindsight will show that they ought to have received; this is, in part, because
some of the emerging technologies are evolving so rapidly that it is difficult to predict what might emerge within the next few months,
let alone the next few years, and the impacts they will have. Not much coverage has been given to the potential of Web 2.0, Web 3.0,
and beyond as they are likely to deserve, again because their evolution is difficult to predict. New interfaces with technology (e.g.,
Apple’s iPhone) have not been touched on, and little mention has been made of the emergence of virtual worlds.
The implications of the social networking technologies that are major elements of Web 2.0 will have a significant impact on the
amount of information and knowledge that is generated and the ways in which it is used. It is known that new healthcare technologies
will develop, most likely based in genomic and nanotechnology sciences, and that they will lead to huge new volumes of data, with
implications for what is captured and stored, for how long, and how it is used. It is also known that computing power and storage
capabilities will lead to faster, smaller, more mobile, and more powerful devices with vastly greater capacities for storing data.
Everyone will have the opportunity to be more connected, more readily, and more of the time, to many other people through the
growth of wireless networks.
The major challenges will be to find the best tools and methods for managing and making the best use of the information that will
be available not only 24/7, but 60/60/24/7 (i.e., every second of every day). For many, knowing what to keep up with—that is, what is
most relevant to practice, education, and research—will be a challenge. Nurse informaticians have a good track record in finding ways
to use information and will need to rise to that challenge.
The potential to generate more health data—for example, from new forms of physiologic monitoring, or as implications of the new
“omics” sciences—will raise important new needs for storing those data, in both the short and long terms, and for generating from
those data the information and knowledge needed to support clinical practice, research, and education. The key words to describe the
changes in the ways in which nursing and healthcare knowledge are acquired, processed, generated, and disseminated are smaller,
more integrated, more mobile, more wearable, more connected, and ubiquitous.
Devices for capturing and storing data will become smaller and more portable, and the amount of data that can be stored within a
device will increase. As smartphone technology evolves, devices the size of today’s cell phones are likely to provide all the
functionality of today’s desktop computers—and more. One can already see the integration of more functions into today’s cell phones
and newer, more portable tabletsized devices, such as Apple’s iPad, and this trend is likely to continue. Both of these trends make the
information processing power available to nurses more mobile, enabling nurses to spend more time with patients and to develop new
ways of interacting with patients and delivering care in a wide range of settings. Another emerging medium is cloud storage, which is
a model of online storage where data are held on, and possibly distributed across, several virtual servers, rather than being hosted on
dedicated servers. This arrangement has many implications that are only just beginning to be explored, especially in terms of legal and
ethical issues (e.g., if health data are stored on servers that may be located outside the jurisdiction of the country where the hospital or
other health facility is physically located).
All of this increased computing power in mobile devices will be of little use if mobile communications are restricted. Many health
service facilities, for example, currently restrict staff access to certain external information resources, especially those that may involve
social media. Access to information and use of information on the move will increase as new forms of wireless communications are
implemented, meaning that nurses will be less restricted to particular physical spaces to interact with patients’ records. One of the “hot
topics” for discussion at the time of writing this chapter is whether the rise of “apps” running on smartphones and other mobile devices
will mean the decline of Web-based Internet use (“app” is an abbreviation for application, meaning a piece of software that can run on
the Internet, on a computer, or on another device) (Anderson & Wolff, 2010). Finally, although wearable computing may have more
to offer in terms of capturing data from patients, emerging tools may mean that the nurse will not be restricted to holding and handling
physical devices, but may interact, through voice and other commands, with computing devices carried elsewhere on their persons.
As nurses work in environments where they can be always connected to ubiquitous computing resources, they will no longer be
able to say that they did not have access to the information they needed to undertake their roles. The challenge will be to find not only
technological ways of dealing with the available information, but also ways of using and prioritizing information, new ways of
thinking, and new ways of interacting with information resources, so that nurses have the right information, at the right time, to do the
right job, and are not overwhelmed with extraneous and irrelevant “noise.” The technology is the easy part; the nontechnological
issues will be the greater, and perhaps more urgent, challenge.
This chapter has explored some of the emerging technologies that are in general use as well as those that are in use and
development within health care, and discussed what the future might hold for nurses, nursing, and health care more generally.
Technology will not solve all our future problems; some emerging technologies could create more problems than they solve. However,
it is by having an awareness of likely and possible technological developments that nurses can assess what impact they might have on
the ways in which we work and live. This chapter has considered how some of the emerging technologies might address the four areas
of the Foundation of Knowledge model (1) knowledge acquisition, (2) knowledge processing, (3) knowledge generation, and (4)
knowledge dissemination or feedback. The focus here was not explicitly on changes in healthcare and nursing technologies, but rather
on the more general changes that might be adapted or adopted for use by nurses or within health care. In an ideal world, we would like
to see the development of easy-to-use tools for nurses to manage the coming knowledge “explosion” as more information becomes
available to support diagnosis, treatment, and care.
THOUGHT-PROVOKING
QUESTIONS
1. This chapter raises several important issues related to nursing knowledge, not the least of which might be the uniqueness of nursing knowledge.
Is this uniqueness requisite for future care by nurses or nursing informatics specialists?
2. Given that the future is relatively unpredictable, what might be said regarding what can be predicted and to what degree of certainty?
3. Will the new tools and technologies that nurses might use to manage the “knowledge explosion” be sufficient on their own, and how will they
need to interact with human factors?
4. What would be the top five pieces of evidence that you would select to be featured in your own portfolio? Why did you select these pieces of
evidence? What was it about them that made you think they would accurately represent who you are?
5. Some institutions that require students to develop e-portfolios as a part of their program of study also may use this evidence of student learning to
evaluate the program quality or generate evidence for accreditation reports. What do you think should be the driving purpose behind requiring e-
portfolios—professional development planning or institutional evaluation? What are the merits of each approach?
References
Akter, S., & Ray, P. (2010). mHealth: An ultimate platform to serve the unserved. In A. Geissbuhler & C. Kulikowski (Eds.), Yearbook of medical
informatics 2010 (pp. 94–100). Stuttgart, Germany: Schattauer.
American Medical Informatics Association (AMIA). (2012). 11th International Congress on Nursing Information: Advancing global health through
informatics. http://www.ni2012.org/Anderson, C., & Wolff, M. (2010). The Web is dead: Long live the Internet. Wired.
http://www.wired.com/magazine/2010/08/ff_webrip/all/1
Anderson, P., Blumenthal, J., Bruell, D., Rosenzweig, M., Conte, M., & Song, J. (2010). An online and social media training curricula to facilitate
bench-to-bedside information transfer. In Positioning the profession: The Tenth International Congress on Medical Librarianship. Brisbane,
Australia, August 31–September 4, 2009. http://espace.library.uq.edu.au/eserv/UQ:179795/n4_2_Thurs_Blumenthal_205
Anderson, T. (2005). Distance learning: Social software’s killer ap?
http://auspace.athabascau.ca:8080/dspace/bitstream/2149/2328/1/distance_learning
Ball, M. J. (2005). Nursing informatics of tomorrow. Healthcare Informatics, 2(5), 74–75. Barnett, G., Justice, N., Somand, M., Adams, J., Waxman, B.,
Beaman, P., … Greenlie, J. (1979). COSTAR: A computer-based medical information system for ambulatory care. In J. van Bemmel (Ed.),
Yearbook of medical informatics: The promise of medical informatics (pp. 262–273). New York, NY: Schattauer.
Blum, B. (1986). Clinical information systems. New York, NY: Springer-Verlag.
Blumenthal, D. (2010). Launching HITECH. New England Journal of Medicine, 362, 5.
Bott, O. J. (2005). Ubiquitous health care systems: A new paradigm for medical informatics? In R. Haux & C. Kulikowski (Eds.), IMIA yearbook of
medical informatics 2005: Ubiquitous health care systems (pp. 213–218). Stuttgart, Germany: Schattauer.
Boutros, S. (1993). Egyptian experience with microcomputers in monitoring and evaluating of health care programmes. In S. Mandil, K. Moidu, M.
Korpela, P. Byass, & D. Forster (Eds.), Health informatics in Africa HELINA 93 (pp. 58–63). Amsterdam, Netherlands: Excerpta Medica.
Brandejs, J. (1976). Health informatics Canadian experience. New York, NY: American Elsevier.
Chang, P., Sheng, Y-H., Sang, Y-Y., Wang, D-W., Hsu, Y-S., & Hou, I-C. (2006). Developing and evaluating a wireless speech-and-touch-based
interface for intelligent comprehensive triage support systems. In H-A. Park, P. J. Murray, & C. Delaney (Eds.), Consumer-centered computer-
supported care for healthy people: Proceedings of NI2006 (pp. 693–697). Amsterdam, Netherlands: IOS Press.
Collen, M. F. (1995). A history of medical informatics in the United States: 1950–1990. Indianapolis, IN: American Medical Informatics Association.
Commission of the European Communities. (2004). E-health: Making healthcare better for European citizens: An action plan for a European e-health
area. COM (2004) 356 final. Brussels, Belgium: Author.
Daini, O., Makanjuola, R., & Ojo, J. (1993). A hospital information system in a Nigerian university teaching hospital. In S. Mandil, K. Moidu, M.
Korpela, P. Byass, & D. Forster (Eds.), Health informatics in Africa HELINA 93 (pp. 86–89). Amsterdam, Netherlands: Excerpta Medica.
Danish eHealth Portal. (2008). https://www.sundhed.dk/service/english/
de Zegher, I., Venot, A., Milstein, C., Sene, B., deRosis, F., DeCarolis, B., … Dahlberg, B. (1995). OPADE: Optimization of drug prescription using
advanced informatics in health informatics in health. In M. F. Laires, M. J. Ladeira, & J. P. Christensen (Eds.), The new communications age: Health
care telematics for the 21st century (pp. 251–259). Amsterdam, Netherlands: IOS Press.
Development, Concepts and Doctrine Centre (DCDC). (2010). Strategic Trends Programme: Global strategic trends—out to 2040 (4th ed.). Swindon,
UK: DCDC, Ministry of Defence. Retrieved November 2010 from http://www.mod.uk/NR/rdonlyres/38651ACB-D9A9-4494-98AA-
1C86433BB673/0/gst4_update9_Feb10
Easteal, S., & Demosthenes, P. (2005). View from the future. Bio-ITWorld.com. http://lifescientist.com.au/content/lab-technology/article/view-from-the-
future-420033371
Elioutina, S., & Tarasov, V. (1995). Current state and perspectives of healthcare informatics in Russia. International Journal of Bio-Medical Computing,
39, 163–167.
Godwin-Jones, R. (2003). Emerging technologies. Blogs and wikis: Environments for on-line collaboration. Language Learning and Technology.
http://llt.msu.edu/vol7num2/pdf/emerging
Hannah, K., Ball, M., & Edwards, M. (1999). Introduction to nursing informatics (2nd ed.). New York, NY: Springer-Verlag.
Haux, R., Knaup, P., & Schmucker, P. (1999). Commentary: Medical and health information systems: The boundaries are still fading. In A. McCray & J.
van Bemmel (Eds.), Yearbook of medical informatics 1999 (pp. 235–237). New York, NY: Schattauer.
Hayes, G., & Barnett, D. (Eds.). (2008). UK health computing: Recollections and reflections. Swindon, UK: British Computer Society.
HIMSS (2013). mHealth Summit: 5th Annual. http://www.mhealthsummit.org/
Horner, V., Hanmer L., & Mbananga, N. D. (2006). A consumer decision support system for common health ailments in South Africa. In H-A. Park, P.
J. Murray, & C. Delaney (Eds.). Consumer-centered computer-supported care for healthy people: Proceedings of NI2006 (p. 1027). Amsterdam,
Netherlands: IOS Press.
Istepanian, R., Laxminarayan, S., & Pattichis, C. S. (Eds.). (2006). M-health: Emerging mobile health systems. Berlin, Germany: Springer.
Kenwright, K., & Pifer, L. (2010). Focus: Nanotechnology. Nanotechnology: Nanomedicine. Clinical Laboratory Science, 23(2), 112–116. Retrieved
from CINAHL Plus with Full Text database.
Kirovski, D., Oliver, N., Sinclair, M., & Tan, D. (2007). Health-OS: A position paper. Proceedings of the 1st ACM SIGMOBILE International
Workshop. http://portal.acm.org/citation.cfm?id=1248077
Kuhn, K. A., Wurst, S. H. R., Bott, O. J., & Guise, D. A. (2006). Expanding the scope of health information systems: Challenges and developments.
IMIA yearbook of medical informatics 2006. Stuttgart, Germany: IMIA & Schattauer.
Kurzweil, R. (2005). The singularity is near: When humans transcend biology. London, UK: Gerald Duckworth & Co.
Lawton, S., & Timmons, S. (2006). The relationship between technology and changing professional roles in health care: A case study in
teledermatology. In H-A. Park, P. J. Murray, & C. Delaney (Eds.), Consumer-centered computer-supported care for healthy people: Proceedings of
NI2006 (pp. 669–671). Amsterdam, Netherlands: IOS Press.
Lee, M., Delaney, C., & Moorhead, S. (2006). Building a personal health record from nursing perspective. In H-A. Park, P. J. Murray, & C. Delaney
(Eds.), Consumer-centered computersupported care for healthy people: Proceedings of NI2006 (pp. 25–29). Amsterdam, Netherlands: IOS Press.
Liolios, C., Doukas, C., Fourlas, G., & Maglogiannis, I. (2010). An overview of body sensor networks in enabling pervasive healthcare and assistive
environments. In PETRA ’10, Proceedings of the 3rd International Conference on Pervasive Technologies Related to Assistive Environment. Samos,
Greece, June 23–25, 2010. Arlington, TX: University of Texas at Arlington, TX.
Loescher, L. J., & Merkle, C. J. (2005). The interface of genomic technologies and nursing. Journal of Nursing Scholarship, 37(2), 111–119.
Lymberis, A. (2010). Advanced wearable sensors and systems enabling personal applications. Lecture Notes in Electrical Engineering, 75, 237–257.
Maag, M. (2005). The potential use of “blogs” in nursing education. CIN: Computers, Informatics, Nursing, 23(1), 16–24.
Maag, M. (2006). Podcasting and MP3 players: Emerging education technologies. CIN: Computers, Informatics, Nursing, 24(1), 9–13.
Mandil, S., Moidu, K., Korpela, M., Byass, P., & Forster, D. (Eds.). (1993). Health informatics in Africa HELINA93. Amsterdam, Netherlands: Excerpta
Medica.
Martin-Sanchez, F., Iakovidis, I., Norager, F., Maojo, V., de Groen, P., Vander Lei, J., … Vicente, F. (2004). Synergy between medical informatics and
bioinformatics: Facilitating genomic medicine for future health care. Journal of Biomedical Informatics, 37(1), 30–42.
Martindale, T., & Wiley, D. A. (2004). An introduction to teaching with weblogs.
https://web.archive.org/web/20110322050151/http://teachable.org/papers/2004_blogs_in_teaching McCormick, K. A. (2001). Future directions.
In V. K. Saab & K. A. McCormick (Eds.), Essentials of computers for nurses: Informatics for the new millennium (3rd ed., pp. 519–527). New York,
NY: McGraw-Hill.
McCormick, K. A., Delaney, C. J., Brennan, P. F., Effie, J. A., Kendrick, K., Murphy, J., … Westra, B. (2007). Guideposts to the future: An agenda for
nursing informatics. Journal of the American Medical Informatics Association, 14(1), 19–24.
Me-too, D. (2009). Practice development. Nanotechnology: The revolution of the big future with tiny medicine. British Journal of Nursing, 18(19),
1201–1206. Retrieved from CINAHL Plus with Full Text database.
Michael, P. (2009). mHealth in the Millennium Villages Project. Earth Institute at Columbia University.
http://www.amddprogram.org/d/sites/default/files/Millennium%20Villages%20mHealth%20Programs
Miller, R., People, H., & Myers, J. (1982). INTERNIST-1: An experimental computer-based diagnostic consultant for general internal medicine. New
England Journal of Medicine, 307, 468–476.
Mortensen, R., & Nielsen, G. (1995). Tokenising. In M. F. Laires, M. J. Ladeira, & J. P. Christensen (Eds.), Health in the new communications age:
Health care telematics for the 21st century (pp. 115–126). Amsterdam, Netherlands: IOS Press.
Murray, P. J. (2009, June 29). Looking towards “Web 4.0” in health and nursing. Blog post: http://www.hi-blogs.info
Murray, P. J., & Maag, M. (2006). Towards health informatics 2.0: Blogs, podcasts and Web 2.0 applications in nursing and health informatics
education and professional collaboration: A discussion paper. http://www.differance-
engine.net/hiblogs/media/publications/murraymaaghi20%20july06
Murray, P. J., Park, H-A., Erdley, W. S., & Kim, J. (Eds.). (2007). Nursing informatics 2020: Towards defining our own future. Proceedings of NI2006
Post Congress Conference, 108. Amsterdam, Netherlands: IOS Press.
Murray, P. J., van Middleton, I., & Meyer, S. (2010). Nursing informatics in South Africa: From a historical overview to the emergence of EHRs,
telehealth and m-health. In C. A. Weaver, C. W. Delaney, P. Weber, & R. L. Carr (Eds. pp. 145–161), Nursing informatics for the 21st century: An
international look at practice, education and EHR trends. Chicago, IL: HIMSS.
Murray, P. J., & Ward, R. (2004). Engaging in healthcare informatics: Let’s use the technology. British Journal of Healthcare Computing & Information
Management, 21(10), 14. O’Donovan, T., O’Donoghue, J., Sreenan, C., Sammon, D., O’Reilly, P., & O’Connor, K. (2010).
A context aware wireless body area network (BAN). 3rd International Conference on Pervasive Computing Technologies for Healthcare 2009. London,
England. http://dx.doi.org/10.4108/ICST.PERVASIVEHEALTH2009.5987
Oh, H., Rizo, C., Enkin, M., & Jadad, A. (2005). What is ehealth (3): A systematic review of published definitions. Journal of Medical Internet
Research, 7(1), e1. http://www.jmir.org/2005/1/e1
O’Reilly, T. (2005). What is Web 2.0: Design patterns and business models for the next generation of software. http://oreilly.com/web2/archive/what-is-
web-20.html
Oren, E., Shaffer, E. R., & Guglielmo, B. J. (2003). Impact of emerging technologies on medication errors and adverse drug events. American Journal of
Health-System Pharmacy, 60(14), 1447–1458.
Oyri, K., Balasingham, H., & Hogetveit, J. O. (2006). Implementation of wireless technology in advanced clinical practice. In H-A. Park, P. J. Murray,
& C. Delaney (Eds.), Consumercentered computer-supported care for healthy people: Proceedings of NI2006 (pp. 730–733). Amsterdam,
Netherlands: IOS Press.
Oyri, K., Newbold, S., Park, H-A., Honey, M., Coenen, A., Ensio, A., & Jesus, E. (2007). Technology developments applied to healthcare/nursing. In P.
J. Murray, H-A. Park, W. S. Erdley, & J. Kim (Eds.), Nursing informatics 2020: Towards defining our own future. Proceedings of NI2006 Post
Congress Conference (pp. 21–37). Amsterdam, Netherlands: IOS Press.
Park, H-A., Murray, P., & Delaney, C. (2006). Consumer-centered computer-supported care for healthy people: Proceedings of NI2006. Amsterdam,
Netherlands: IOS Press.
Saba, V. K., & Erdley, W. S. (2006). Historical perspectives of nursing and the computer. In V. K. Saba & K. A. McCormick (Eds.). Essentials of
nursing informatics (4th ed., pp. 9–28). New York, NY: McGraw-Hill.
Saba, V. K., & McCormick, K. A. (1986). Essentials of computers for nurses. Philadelphia, PA: J. B. Lippincott Company.
Sackett, K. M., & Erdley, W. S. (2002). The history of health care informatics. In S. P. Englebardt & R. Nelson (Eds.), Health care informatics: An
interdisciplinary approach (pp. 453–457). St. Louis, MO: Mosby.
Santra, T. (2010). Mobile health care system for patient monitoring. Communications in Computer and Information Science, 101(Pt 3), 695–700.
Saranto, K., Brennan, P. F., & Casey, A. (Eds.). (2009). Personal health information management: Tools and strategies for citizens’ engagement.
Kuopio, Finland: University of Kuopio. http://www.uku.fi/vaitokset/2009/isbn978-951-27-1321-9
Saranto, K., Brennan, P. F., Park, H-A., Tallberg, M., & Ensio, A. (Eds.). (2009). Connecting health and humans: Proceedings of NI2009, the 10th
International Congress on Nursing Informatics. Amsterdam, Netherlands: IOS Press.
Schmidt, K. F. (2007). Nanofrontiers: Visions for the future of nanotechnology. Project on Emerging Nanotechnologies 6. Woodrow Wilson
International Center for Scholars. http://www.nanotechproject.org/file_download/files/PEN6_NanoFrontiers
Schrader, U. (2006). Managing a lecture in nursing informatics in a blended learning format: A bottom-up approach to implement an open-source Web-
based learning management system. In H-A. Park, P. J. Murray, & C. Delaney (Eds.), Consumer-centered computer-supported care for healthy
people: Proceedings of NI2006 (pp. 559–562). Amsterdam, Netherlands: IOS Press.
Shortliffe, E., & Buchanan, B. (1975). A model of inexact reasoning in medicine. Math Biosci, 23, 351–379.
Siemens, G. (2005). Connectivism: A learning theory for the digital age.
http://www.ingedewaard.net/papers/connectivism/2005_siemens_ALearningTheoryForTheDigitalAge
Slonim, N., Carmeli, B., Goldsteen, A., Keller, O., Kent, C., & Rinott, R. (2012). Knowledge–analytics synergy in clinical decision support. Studies in
Health Technology and Informatics, 180, 703–707.
Spallek, H., O’Donnell, J., Clayton, M., Anderson, P., & Krueger, A. (2010). Paradigm shift or annoying distraction: Emerging implications of Web 2.0
for clinical practice. Applied Clinical Informatics, 1, 96–115.
Staggers, N., McCasky, T., Brazelton, N., & Kennedy, R. (2008). Nanotechnology: The coming revolution and its implications for consumers, clinicians,
and informatics. Nursing Outlook, 56(5), 268–274.
Stammer, L. (2000). Brazil and its neighbours. Healthcare Informatics, 17(8), 26–8, 30, 32.
Technology Informatics Guiding Education Reform (TIGER). (2007). The TIGER Initiative:
Evidence and informatics transforming nursing: 3-year action steps toward a 10-year vision.
http://www.tigersummit.com/uploads/TIGERInitiative_Report2007_Color
Technology Workgroup, Maryland Statewide Commission on the Crisis in Nursing. (2004).
Technology’s role in addressing Maryland’s nursing shortage: Innovations & examples. http://www.mbon.org/commission/nsg_innovation
Trivedi, P., Sagar, K., & Vernon. (2010). Emerging trends of ubiquitous computing. International Journal of Advanced Computer Science and
Applications, 1(3), 72–74.
Turley, J. P., Murray, P. J., Saranto, K., Ehnfors, M., & Seomun, G-A. (2007). What if nurses get what they have always sought: Totally personalized
care? Trends affecting nursing informatics. In P. J. Murray, H-A. Park, W. S. Erdley, & J. Kim (Eds.), Nursing informatics 2020: Towards defining
our own future: Proceedings of NI2006 Post Congress Conference (pp. 55–72). Amsterdam, Netherlands: IOS Press.
van Ginneken, B., Koenderink, J., & Dana, K. (1999). Texture histograms as a function of irradiation and viewing direction. International Journal of
Computer Vision, 31, 169–184.
Vocera. (2007). Vocera communications badge: Wearable instant voice communication.
https://web.archive.org/web/20100703051430/http://www.vocera.com/downloads/voc_sys_datasheet_1206
Warner, H., Olmsted, C., & Rutherford, B. (1972). HELP: A program for medical decision-making. Computers and Biomedical Research, 5(1), 65–74.
Weaver, C., Kennedy, R., Erdley, W. S., Kim, J., Chang, P., Schrader, U., & Maag, M. (2007). Health care in 2020. In P. J. Murray, H-A. Park, W. S.
Erdley, & J. Kim (Eds.), Nursing informatics 2020: Towards defining our own future: Proceedings of NI2006 Post Congress Conference (pp. 73–
83). Amsterdam, Netherlands: IOS Press.
Weiser, M. (1991, September). The computer for the twenty-first century. Scientific American, 265(3), 94–104.
White House. (2009). Weekly address: President Barack Obama discusses new White House report on an American recovery and reinvestment plan.
http://www.whitehouse.gov/the-press-office/weekly-address-president-barack-obama-discusses-new-white-house-report-american-rec
Williams, L. (2007). Nobel laureate James Watson receives personal genome in ceremony at Baylor College of Medicine. http://news.bio-
medicine.org/biology-news-3/Nobel-laureate-James-Watson-receives-personal-genome-in-ceremony-at-Baylor-College-of-Medicine-1153-1/
World Health Organization (WHO). (2007). WHO brainstorming session on mhealth. Mede-Tel Conference, Luxembourg, Luxembourg.
http://www.medetel.lu/download/2007/parallel_sessions/abstract/0418/WHO_m-HEALTH_Brainstorm_Notes
Zollner, H. (1995). The CARE telematics project. In M. F. Laires, M. J. Ladeira, & J. P. Christensen (Eds.), Health in the new communications age:
Health care telematics for the 21st century (pp. 279–289). Amsterdam, Netherlands: IOS Press.
Chapter 29
Nursing Informatics and the Foundation of Knowledge
Dee McGonigle and Kathleen Mastrian
OBJECTIVES
1. Assess nursing as a knowledge-intensive profession.
2. Explore the contribution of nursing informatics to the foundation of knowledge.
Key Terms
Codify
Comprehensive Health Enhancement Support System (CHESS)
Data
Data-centric
Information
Information technology (IT)
Knowledge
Knowledge acquisition
Knowledge-centric
Knowledge dissemination
Knowledge domain process (KDP)
Knowledge generation
Knowledge management system (KMS)
Knowledge repositories
Knowledge worker
Nursing informatics (NI)
Introduction
Throughout this text, the reader has learned about the many facets of nursing informatics (NI) and the interfacing of nurse knowledge
workers and technology. The Foundation of Knowledge model (Figure 29-1) has provided a framework for examining the dynamic
interrelationships among data, information, and knowledge used to meet the needs of healthcare delivery systems, organizations,
patients, and nurses. The importance of knowledge management in nursing is emphasized by taking this one last opportunity to ensure
that the reader understands and appreciates the value of knowledge management in the nursing profession and the role that technology
has in knowledge acquisition, knowledge generation, knowledge dissemination, and knowledge processing.
Figure 29-1 Foundation of Knowledge Model
Source: Designed by Alicia Mastrian.
Foundation of Knowledge Revisited
A review of the Foundation of Knowledge model that provides a framework for the development of this text is useful. At its base, the
model has bits, bytes (computer terms for chunks of information), data, and information in a random representation. Growing out of
the base are separate cones of light that expand as they reflect upward and represent knowledge acquisition, knowledge generation,
and knowledge dissemination. At the intersection of the cones and forming a new cone is knowledge processing. Encircling and
cutting through the knowledge cones is feedback, which acts on and may transform any or all aspects of knowledge represented by the
cones.
Now, imagine the model as a dynamic figure with the cones of light and the feedback rotating and interacting rather than remaining
static. Knowledge acquisition, knowledge generation, knowledge dissemination, knowledge processing, and feedback are constantly
evolving for nurse scientists. The transparent effect of the cones is deliberate and is intended to suggest that as knowledge grows and
expands, its use becomes more transparent; that is, the user is not even consciously aware of which aspect of knowledge he or she is
using at any given moment during her or his practice.
If you are an experienced nurse, think back to when you were a novice. Did you feel like all you had in your head were bits of data
and information that did not form any type of cohesive whole? As the model depicts, the processing of knowledge as an individual in
professional practice begins a bit later (imagine a timeline applied vertically), with early experiences on the bottom and expertise
growing as the processing of knowledge kicks in. Early on in nursing education, conscious attention is focused mainly on knowledge
acquisition, and learners depend on their instructors and others to process, generate, and disseminate knowledge. As learners become
more comfortable with the science of nursing, they begin to take over some of the other knowledge functions. However, to keep up
with the explosion of information in nursing and health care, one must continue to rely on the knowledge generation and dissemination
of others. In this sense, nurses are committed to lifelong learning and the use of knowledge in the practice of nursing science.
For nurse knowledge workers, information is their primary resource, and when one deals with information it is done in overlapping
phases. That is, the nurse is continually acquiring, processing or assimilating, retaining, and using this information to generate and
disseminate knowledge. However, it is not a sequential phasing; instead, there is a constant gleaning of data and information from the
environment, with the data and information massaged into knowledge bases so that they can be applied and shared (disseminated).
Knowledge may be thought of as either explicit or tacit. Explicit knowledge is the knowledge that one can convey in letters, words,
and numbers. It can be exchanged or shared in the form of data, manuals, product specifications, principles, policies, theories, and the
like. Nurses can disseminate and share this knowledge publicly or on the record and scientifically or methodically. A nursing model or
theory that is well developed and easily explained and understood is an example of explicit knowledge. Tacit knowledge, in contrast, is
individualized and highly personal or private, including one’s values or emotions. Knowing intuitively when and how to care is an
example of tacit knowledge. This type of knowledge is difficult to convey, transmit, or share with others because it consists of one’s
own insights or slant on things, perceptions, intuitions, sense, hunches, or gut feelings. Tacit knowledge reflects skills and beliefs,
which is why it is difficult to explain or communicate it to others. Lake (2005) states:
From close examination of and reflection on the literature it is possible to infer nursing prioritization of the patient need for
care as it is initially taught to nursing students and is then developed in practice and influenced by practice setting. The
process of nursing prioritization of the patient need for care involves discretionary judgment and ongoing assessment
throughout and between unfolding patient situations. It is best understood from studies addressing clinical decision-making in
nursing through the interpretive paradigm and in the plain language descriptions of nurse decision-making. The principles of
such decision-making are discussed only in very general terms and the rationale remains the tacit knowledge of nursing. (p.
152)
How nursing students and practicing nurses learn is directly affected by their practice experiences within their own personal frame
of reference. The quality of clinical decision making is directly related to experience and knowledge. Knowledge is situational.
Explicit and tacit knowledge are used to conduct assessments, diagnoses, intervention implementation, and evaluation of nursing
actions for each individual patient. Knowledge management systems (KMSs) must blend these knowledge needs and provide
knowledge bases and decision support systems to inform clinical decision making. Each person processes and assimilates knowledge
in a unique way influenced by his or her unique perspective.
Knowledge Use in Practice
One way to capture and codify tacit knowledge is to engage in reflection and reflective practice. Schutz (2007) believes that reflection
is both a way of learning about practice and a basis for changing practice. One must engage in reflective practice because this approach
can enable a practitioner to find a means in which to put this personal or experiential knowledge into words and to find a way of
considering why the situation turned out as it did and whether future practice might be different (p. 27).
Some healthcare organizations are encouraging reflective practice to codify tacit knowledge and thus to build an organization’s
knowledge base. Sharing experiences in a Nursing Practice Council is one means to encourage collaboration and knowledge sharing
among professionals. Joining a LISTSERV or a community of practice is another example of collaboration to build knowledge.
Watson (2007) describes knowledge audits, narratives, and storytelling as means of surfacing tacit knowledge and assessing the
knowledge resources within an organization. He describes the use of information technology (IT) tools for knowledge management
in an organization, such as intranets, extranets (shared intranets among several like organizations), knowledge directories, blogs, and
wikis. Recent research suggests that organizations that embrace and encourage knowledge transfer among workers not only sustain and
build professional competence and organizational engagement, but also enhance the quality of the work life for professionals (Leiter,
Day, Harvie, & Shaughnessy, 2007).
According to Gent (2007), there are three types of knowledge workers: (1) knowledge consumers, (2) knowledge brokers, and (3)
knowledge generators. This breakdown of knowledge workers is not mutually exclusive; instead, people transition between these states
as their situations and experience, education, and knowledge change.
Knowledge consumers are mainly users of knowledge who do not have the expertise to provide the knowledge they need for
themselves. Novice nurses can be thought of as knowledge consumers who use the knowledge of experienced nurses or who
search information systems for the knowledge necessary to apply to their practice. As responsible knowledge consumers, they
must also question and challenge what is known to help them learn and understand. Their questioning and challenging facilitate
critical thinking and the development of new knowledge.
Knowledge brokers know where to find information and knowledge; they generate some knowledge but are mainly known for
their ability to find what is needed. More experienced nurses and nursing students become knowledge brokers out of necessity,
needing to know.
Knowledge generators are the “primary sources of new knowledge” (para. 2). They include nursing researchers and nursing
experts—the people who know. They are able to answer questions, craft theories, find solutions to nursing problems or
concerns, and innovate as part of their practice.
The healthcare industry, the nursing profession, and patients all benefit as nurses develop nursing intelligence and intellectual
capital by gaining insight into nursing science and its enactment, practice. NI applications of databases, knowledge management
systems, and repositories where this knowledge can be analyzed and reused facilitate this process, enabling knowledge to be
disseminated and reused.
To be able to enhance the acquisition, processing, generation, dissemination, and reuse of nursing knowledge, nurses must codify
or be able to articulate knowledge structures so that they can be captured within the KMSs. According to Markus (2001):
Synthesis of evidence from a wide variety of sources suggests four distinct types of knowledge reuse situations according to the
knowledge reuser and the purpose of knowledge reuse. The types involve shared work producers, who produce knowledge they
later reuse; shared work practitioners, who reuse each other’s knowledge contributions; expertise-seeking novices; and
secondary knowledge miners. Each type of knowledge reuser has different requirements for knowledge repositories. (para. 1)
Markus refers to knowledge repositories as “organizational memory systems” (2001, para. 1). These memory systems gained
popularity among help desk personnel who could access and reuse the knowledge of solutions when clients sought help for similar
problems. Health care is an arena in which KMSs or knowledge repositories are clearly valuable. Hsia, Lin, Wu, and Tsai (2006)
recognize that nurses are “knowledge-intensive” (para. 4) professionals who are “required to take new nursing knowledge and
experience that can be acquired through various net-enabled applications or the Internet. Nursing professionals are being asked to do
more with less in such contexts, while their nursing care responsibilities have increased” (para. 4). Today, information technology
capabilities are expanding to develop and support a “knowledge-centric [boldface added] view rather than simply a data-centric
[boldface added] view” (para. 4). Nurse knowledge workers must be able to access, use, and share these new informatics tools because
“a well-designed IT-based knowledge management system (KMS) has become an ever more central force in improving the quality of
care in competitive e-health environments” (para. 4). Capturing the explicit and tacit forms of knowledge is paramount to truly harness
nursing knowledge. As knowledge repositories evolve to enhance sharing and repurposing of knowledge, nurses will be able to easily
access, process, evaluate, reuse, generate, and disseminate knowledge.
This text uses the Foundation of Knowledge model reflecting that knowledge is power, and for that reason, nurses focus on
information as a key resource. The application of the model was described in each section of the text to help the reader understand and
appreciate the foundation of knowledge in NI. All of the various nursing roles—practice, administration, education, research,
informatics—involve the science of nursing. Nurses are knowledge workers, working with information and generating information and
knowledge as a product. They are knowledge acquirers, providing convenient and efficient means of capturing and storing knowledge.
They are knowledge users, individuals or groups who benefit from valuable, viable knowledge. Nurses are also knowledge engineers,
designing, developing, implementing, and maintaining knowledge. They are knowledge managers, capturing and processing collective
expertise and distributing it where it can create the largest benefit. They are knowledge developers or generators, changing and
evolving knowledge based on the tasks at hand and information available.
Nursing science is dependent on knowledge generation, and NI should facilitate all aspects of nursing especially in the generation
of knowledge and translational research, where we attempt to bridge the gap between what we know (research) and what we do
(practice). Swan, Lang, and McGinley (2004) describe NI and a common nursing language as an important vehicle to access stores of
clinical information that can be used as the basis for research and to help answer the question, “What do nurses do?” “Embedding
nursing language within informatics structures is essential to make the work of nurses visible and articulate evidence about the quality
and value of nursing in the care of patients, groups, and populations” (para. 27). In this text, it has been established that NI is a vital
tool for clinical decision making, especially when one is able to demonstrate how nurses structure and process information. An
important direction for the future is to study the impact of NI on nursing science. For example, Goossen (2000) suggests the need for
further study of nursing decision making and the need to model this process. He also suggests that researchers need to focus on the
evaluation of technology systems themselves and the human–technology interface.
As the HITECH Act has been implemented, the use of electronic health records (EHRs) has become an expectation in the United
States. Such records must be designed to enhance patient outcomes through content enrichment and improved caregiver decision
making. As bioinformatics and computational biology continue to evolve, their integration into the EHR is inevitable. Nursing
informaticists must facilitate the inclusion of computational tools and algorithms to help handle the collection, organization, analysis,
processing, presentation, and dissemination of biological data to help address biological questions and unravel biological mysteries. It
is imperative that current research strategies, such as those used to search for biomarkers, and new pharmacologic treatments be
included in the EHR. In this bioinformatics era, one must be able to delineate biomarkers and have the necessary alerts, follow-ups,
and reminders built into the system to make all caregivers aware of the bioinformatics information, such as the analysis of genes
causing hypertension, cardiovascular disease, and diabetes. Bioinformatics and computational biology will complement all of the
current methods and aid in the analysis of populations and tracking of selected diseases’ progression. Consequently, nursing
informaticists must be proactive in the development of policies and ethically based solutions to safeguard the genetic data contained in
EHRs, manage the patient care implications of bioinformatics, and wisely use computational biology in this bioinformatics era.
A paradigm shift is occurring from healthcare facility–owned, machine-based computing to off-site, vendor-owned, cloud
computing. Web browser–based login accessible data, software, and hardware could link systems together and reduce costs. Hospitals
with shrinking budgets and extreme IT needs are exploring the successes in other industries, such as Amazon’s S3. As providers strive
to implement potent EHRs, they are looking for cloud-based models that will offer the necessary functionality without having to
assume the burden associated with all of the hardware, software, application, and storage issues. However, in the face of the HITECH
Act and its associated penalties, numerous challenges must be overcome to realize the benefits of this approach. The advantages and
disadvantages of cloud computing must be fully explored in light of the challenges faced by both healthcare providers, as they strive to
maintain security while relinquishing control, and the vendors that are responsible for developing and maintaining this new cloud-
based EHR environment.
NI can also be used to facilitate nursing administration and managerial studies of the work of nursing. Numerous opportunities for
data mining in NI have been described. Goossen (2000) suggests two approaches to data mining. The first involves a general search
looking for repeated instances or patterns without preconceived notions about what will be found. Findings from the eyes-wide-open
look at the database may suggest patterns that warrant more structured data mining, consistent with Goossen’s second approach. Some
larger healthcare systems store all of the clinical information from their affiliated hospitals and clinics in a central data warehouse.
General data scans and analyses looking for patterns may, for example, suggest a trend toward better outcomes for patients with
congestive heart failure in one of the affiliate hospitals. This identification of such a trend clearly begs for further analyses. A nurse
researcher or administrator could ask, “Which factors contribute to these better outcomes and how can they be put into practice across
the system?” Other research studies might focus on assessing the effectiveness of strategic planning and organizational goal setting or
studying workflow and communication processes in an organization. Poissant, Pereira, Tamblyn, and Kawasumi (2005), for example,
present the results of a systematic literature review on the time-efficiency impacts of EHRs in the clinical setting.
Numerous other examples of research aimed at advancing the state of the science of NI can be cited. Bakken (2006) provides one
of the most comprehensive overviews of the effects of NI on patient safety. In her opening remarks, she writes, “The health
information technologies deployed as part of the national framework must support nursing practice in a manner that enables prevention
of medical errors and the promotion of patient safety and contributes to the development of practice-based nursing knowledge as well
as best practices for patient safety” (para. 1). Shaw and colleagues (2006) summarize 15 years of research on the Comprehensive
Health Enhancement Support System (CHESS), a computer-based system designed to help underserved breast cancer patients
manage their disease. A study comparing routine instruction with computer-assisted video instruction about home exercise and the
effects on compliance with the regimen and patient satisfaction is yet another example of research opportunities related to NI (Lysack,
Dama, Neufeld, & Andreassi, 2005). As a final example, Hering, Harvan, D’Angelo, and Jasinski (2005) studied the effects of a
specially designed webpage related to surgical teaching on patient acquisition of information and overall satisfaction with the surgical
experience. We invite you to read each of these studies in more detail.
Knowledge management and transfer in healthcare organizations are likely to be studied in greater depth as understanding of
professional knowledge increases and processes to capture and codify it improve. The Foundation of Knowledge model is not perfect,
and others have developed models of knowledge that are more complex. For example, Evans and Alleyne (2009) constructed the
knowledge domain process (KDP) model to represent knowledge construction and dissemination in an organization. Yet, they
caution:
[T]he KDP model, like all models, is an abstraction aimed at making complex systems more easily understood. While the
model presents knowledge processes in a structured and simplified form, the nature and structure of the processes themselves
may be open to debate. (p. 148)
In the future, there will be many more attempts to capture, represent, and explain knowledge processes in professional practice. It
is hoped that the reader is convinced that for the nursing profession to evolve, knowledge must be dynamically generated,
disseminated, and assimilated. This ever-changing interplay means that as knowledge is generated, disseminated, and assimilated, new
questions about the impact of NI that will help new knowledge to be generated, assimilated, and so on will arise. The assimilation of
new knowledge in a profession is a multifaceted approach of individual perception, challenges, and collective thought applied to the
practice of nursing. Nurses challenge what is known and want to acquire, process, generate, and disseminate knowledge.
Summary
As a result of reading this text, you should have a deeper understanding of knowledge and informatics, as well as the power they have
to inform the science of nursing. It is hoped that you also gained valuable insights into the core principles of NI and the NI practice
specialty. The future is exciting. The previous chapter in this section was an excellent insight into what will and can be. The present
chapter should motivate you to continue to learn more and perhaps delve into the science of NI in a nursing research role. Readers are
invited to become active participants in molding the future of both nursing and informatics sciences.
THOUGHT-PROVOKING
QUESTIONS
Become informatics savvy and ask yourself the following questions:
1. How can I apply the knowledge I gain from my practice setting to benefit my patients and enhance my practice?
2. How can I help my colleagues and patients understand and use the current technology that is available?
3. How can I use my wisdom to help create the theories, tools, and knowledge of the future?
References
Bakken, S. (2006). Informatics for patient safety: A nursing research perspective. Annual Review of Nursing Research, 24, 219–254. Retrieved from
ProQuest Nursing & Allied Health Source database. Document ID: 1106698271.
Evans, M., & Alleyne, J. (2009). The concept of knowledge in KM: A knowledge domain process model applied to inter-professional care. Knowledge
and Process Management, 16(4), 147. Retrieved from ABI/INFORM Global. Document ID: 1890423631.
Gent, A. (2007). Three types of knowledge workers. http://incrediblydull.blogspot.com/2007/10/three-types-of-knowledge-workers.html
Goossen, W. (2000). Nursing informatics research. Nurse Researcher, 8(2), 42. Retrieved from ProQuest Nursing & Allied Health Source database.
Document ID: 67258628.
Hering, K., Harvan, J., D’Angelo, M., & Jasinski, D. (2005). The use of a computer website prior to scheduled surgery (a pilot study): Impact on patient
information, acquisition, anxiety level, and overall satisfaction with anesthesia care. AANA Journal, 73(1), 29–33. Retrieved from ProQuest Nursing
& Allied Health Source database. Document ID: 795995771.
Hsia, T., Lin, L., Wu, J., & Tsai, H. (2006). A framework for designing nursing knowledge management systems. Interdisciplinary Journal of
Information, Knowledge and Management. http://www.ijikm.org/Volume1/IJIKMv1p013-022_Hsia02
Lake, S. (2005). Nursing prioritisation of the patient need for care: Tacit knowledge of clinical decision making in nursing.
http://researcharchive.vuw.ac.nz/bitstream/10063/22/6/thesis
Leiter, M., Day, A., Harvie, P., & Shaughnessy, K. (2007). Personal and organizational knowledge transfer: Implications for worklife engagement.
Human Relations, 60(2), 259–283. Retrieved from ABI/INFORM Global. Document ID: 1260239891.
Lysack, C., Dama, M., Neufeld, S., & Andreassi, E. (2005). Compliance and satisfaction with home exercise: A comparison of computer-assisted video
instruction and routine rehabilitation practice. Journal of Allied Health, 34(2), 76–82. Retrieved from ProQuest Nursing & Allied Health Source
database. Document ID: 855324561.
Markus, M. (2001). Toward a theory of knowledge reuse: Types of knowledge reuse situations and factors in reuse success. Journal of Management
Information Systems, 18(1), 57–94.
Poissant, L., Pereira, J., Tamblyn, R., & Kawasumi, Y. (2005). The impact of electronic health records on time efficiency of physicians and nurses: A
systematic review. Journal of the American Medical Informatics Association, 12(5), 505–516. Retrieved from ProQuest Nursing & Allied Health
Source database. Document ID: 908812431.
Schutz, S. (2007). Reflection and reflective practice. Community Practitioner, 80(9), 26–29. Retrieved from ProQuest Nursing & Allied Health Source.
Document ID: 1331520781.
Shaw, B., Gustafson, D., Hawkins, R., McTavish, F., McDowell, H., Pingree, S., & Ballard, D. (2006). How underserved breast cancer patients use and
benefit from ehealth programs: Implications for closing the digital divide. American Behavioral Scientist, 49(6), 823–834. Retrieved from
ABI/INFORM Global database. Document ID: 974889131.
Swan, B., Lang, N., & McGinley, A. (2004). Access to quality health care: Links between evidence, nursing language, and informatics. Nursing
Economics, 22(6), 325–332. Retrieved from Health Module database. Document ID: 768191851.
Watson, M. (2007). Knowledge management in health and social care. Journal of Integrated Care, 15(1), 27–33. Retrieved from ProQuest Nursing &
Allied Health Source. Document ID: 1221495781.
Abbreviations
3D Three-dimensional
ABC Alternative billing codes
ADT Admission, discharge, and transfer system
AHRQ Agency for Healthcare Research and Quality
AI Artificial intelligence
ALA American Library Association
Alt Alternate key on the computer keyboard
ALU Arithmetic logic unit
ANA American Nurses Association
ANGEL A New Global Environment for Learning
ANSI American National Standards Institute
API Application programming interface
ARG Augmented-reality game
ARRA American Recovery and Reinvestment Act
b Bit
B Byte
BCMA Bar Code Medication Administration
BI Bioinformatics
BIOS Basic input/output system
bit/s or bps Bits per second
BMP Bitmap image
BRFSS Behavioral Risk Factor Surveillance System
CAI Computer-assisted instruction
CASE Computer-aided software engineering
CBIS Computer-based information system
CCC Clinical care classification
CD Compact disk
CD-R Compact disk—recordable
CD-ROM Compact disk—read-only memory
CD-RW Compact disk—recordable and rewritable
CDC Centers for Disease Control and Prevention
CDS/CDSS Clinical decision support/clinical decision support system
CHESS Comprehensive Health Enhancement Support System
CHF Congestive heart failure
CHI Consolidated health informatics
CI Cognitive informatics
CINAHL Cumulative Index to Nursing & Allied Health Literature
CIO Chief information officer
CIS Clinical information systems
CMIS Case management information system
CMP Civil monetary penalties
CMS
Course management system; content management system; Centers for Medicare and
Medicaid Services
CNPII Committee for Nursing Practice Information Infrastructure
COPD Chronic obstructive pulmonary disease
CPGs Clinical practice guidelines
CPOE Computerized physician order entry; computer-based provider order entry
CPU Central processing unit
CRA Community risk assessment
CRT Cathode ray tube
CSS Cascading style sheets
CTA Cognitive task analysis
CTO Chief technical officer; chief technology officer
Ctrl Control key on the computer keyboard
CWA Cognitive work analysis
DBMS Database management system
DHR Digital health record
DRAM Dynamic random-access memory
DSDM Dynamic system development method
DSS Decision support system
DVD Digital versatile disk; digital video disk
DVD-R Digital video disk—recordable
DVD-RW Digital video disk—recordable and rewritable
DW Data warehouse
EB Exabyte
EBP Evidence-based practice
EDI Electronic data interchange
EEPROM Electronically erasable programmable read-only memory
EHR Electronic health record
ELSI Ethical, legal, and social issues
EMR Electronic medical record
EPROM Erasable programmable read-only memory
ERD Entity–relationship diagram
ERIC Education Resources Information Center
ESC Escape key
ESLI Ethical, social, and legal implications
F key Function key on the computer keyboard
F/OSS or FOSS Free/open source software
FHIE Federal Health Information Exchange
FMEA Failure modes and effects analysis
FPROM Field programmable read-only memory
FPU Floating-point unit
GB Gigabyte
GHz Gigahertz
GLBA Gramm-Leach-Bliley Act
GUI Graphical user interface
HCI Human–computer interaction
HCT Human–computer technology
HGP Human Genome Project
HHA Home health agency
HIE Health information exchange
HIPAA Health Insurance Portability and Accountability Act
HIS Hospital information system
HIT Health information technology
HITECH Health Information Technology for Economic and Clinical Health Act
HL7 Health Level 7
HMIS Health management information system
HMO Health maintenance organization
HTI Human–technology interaction
HTML Hypertext Markup Language
I/O Input/output
ICNP International Classification of Nursing Practice
IDE Integrated drive electronics
IEEE Institute of Electrical and Electronics Engineers
IHIE Indiana Health Information Exchange
IM Instant message
IN Informatics nurse
INS Informatics nurse specialist
IP Internet Protocol
IS Information system
ISO International Standards Organization or International Organization for Standardization
IT Information technology
KB Kilobyte
KMS Knowledge management system
LAN Local area network
LCD Liquid crystal display
LOINC Logical Observation Identifiers Names and Codes
LOS Length of stay
LTC Long-term care
MAN Metropolitan area network
MB Megabyte
MCIS Managed care information system
MHDC Massachusetts Health Data Consortium
MHz Megahertz
MMIS
Medicaid management information systems MMORPG or simply MMO Massive multiplayer
online role-playing game Modem Modulator–demodulator
MOO Object-oriented MUD
Moodle Modular Object-Oriented Dynamic Learning Environment
MoSCoW Must have, Should have, Could have, and Would have
MP3 MPEG-1 Audio Layer-3
MPEG Moving Picture Experts Group
MPI Master patient index
MRI Magnetic resonance imaging
MUD Multiuser dungeon
MUSH Multiuser shared hack, habitat, holodeck, or hallucination
NANDA-I North American Nursing Diagnosis Association—International
NCPHI National Center for Public Health Informatics
NGC National Guideline Clearinghouse
NGI Next-generation Internet
NHANES National Health and Nutrition Examination Survey
NHII National Health Information Infrastructure
NHIN National Health Information Network
NHQR National Healthcare Quality Report
NI Nursing informatics
NIC Nursing Intervention Classification; network interface card
NIDSEC Nursing Information and Data Set Evaluation Center
NIS Nursing information system
NIST National Institute of Standards and Technology
NLS National language support
NMDS Nursing Minimum Data Set
NMMDS Nursing Management Minimum Data Set
NOC Nursing outcome classification
NPC Nonplayer character
NPI National provider identifier
OASIS Outcomes and Assessment Information Set
OCR Office of Civil Rights
ONC Office of the National Coordinator for Health Information Technology
OS Operating system
OSI Open systems interconnection
OWL Web ontology language
PACS Picture archiving and communication system
PADS Planned accelerated discharge protocols
PB Petabyte
PBL Problem-based learning
PC Personal computer
PCA Patient-controlled analgesia
PCI Peripheral component interconnection
PCIS Patient care information system
PDA Personal data assistant; personal digital assistant
PERS Personal emergency response system
PHI Protected health information; public health informatics
PHR Personal health record
PNDS Perioperative Nursing Data Set
POSIX Portable Operating System Interface for UNIX
PPS Prospective payment system
PROM Programmable read-only memory
PrtSc or Prnt Scrn Print screen key
PS/2 Personal System/2
PT/INR Prothrombin time/International Normalized Ratio
QA Quality assurance
RAD Rapid application development
RAM Random-access memory
RATS Readiness assessment tests
RDBMS Relational database management system
RDF Resource description framework
RFI Radiofrequency identifier
RFID Radio frequency identification
RHIO Regional health information organization
RIS Radiology information system
ROM Read-only memory
RSS Really simple syndication
RSVP Rapid Syndromic Validation Project
RU Research utilization
SCSI Small Computer System Interface
SDLC Systems development life cycle
SDO Standards developing organization
SDRAM Synchronous dynamic random-access memory
SGML Standard Generalized Markup Language
SNOMED CT Systematic Nomenclature of Medical Clinical Terms
SOX Sarbanes-Oxley Act
SPRC Suicide Prevention Resource Center
SQL Structured English Query Language
TB Terabyte
TCP Transmission Control Protocol
TELOS Technological and systems, economic, legal, operational, and schedule feasibility
TPO Treatment payment operations
URL Uniform resource locator
USB Universal serial bus
VNA Visiting Nurse Association
VoIP Voice-over-Internet Protocol
VR Virtual reality
W3C World Wide Web Consortium
WAN Wide area network
WWW World Wide Web
XML Extensible Markup Language
YB Yottabyte
YRBSS Youth Risk Behavior Surveillance System
ZB Zettabyte
Glossary
A New Global Environment for Learning (ANGEL) A course management system designed to support classroom learning in
academic settings.
Acceptable use A corporate policy that defines the types of activities that are acceptable on the corporate computer network, identifies
the activities that are not acceptable, and specifies the consequences for violations.
Accessibility Ease of accessing the information and knowledge needed to deliver care or manage a health service; the extent to which
a system is usable by as many users as possible.
Acquisition The act of acquiring; to locate and hold. We acquire data and information.
Active listening A therapeutic communication technique in which the nurse employs conscious attention to what a patient is saying,
reflects back feelings and phrases, and asks questions to clarify meaning.
Acuity system System that calculates the nursing care requirements for individual patients based on severity of illness, specialized
equipment and technology needed, and intensity of nursing interventions; determines the amount of daily nursing care needed for
each patient in a nursing unit.
Administrative processes The processes used by administration, such as the electronic scheduling, billing, and claims management
systems including electronic scheduling for inpatient and outpatient visits and procedures, electronic insurance eligibility
validation, claim authorization and prior approval, identification of possible research study participants, and drug recall support.
Admission, discharge, and transfer (ADT) system System that provides the backbone structure for the other types of clinical and
business systems; it contains the groundwork for the other types of healthcare information systems because it includes the patient’s
name, medical record number, visit or account number, and demographic information such as age, sex, home address, and contact
information. It is the central source for collecting this type of patient information and communicating it to the other types of
healthcare information systems, including clinical and business systems.
Advocate To act in patients’ best interest; to act and/or speak on patients’ behalf; to make the healthcare delivery system responsive to
patients’ needs.
Advocate/policy developer A nurse informatics specialist who is key to developing the infrastructure of health policy. Policy
development on the local, national, and international levels is an integral part of this role.
Agency for Healthcare Research and Quality (AHRQ) An agency within the U.S. Department of Health and Human Services that
supports health services research initiatives.
Aggregate data Any types of data that can be referenced as a single entity, but that also consist of more than one piece of data;
collected, gathered, and reported data that are related and kept together in a way that addresses their relationship. For example, the
population of a state is an aggregate of the populations of its cities, counties, and regions.
Alarm fatigue Multiple false alarms by smart technology that cause workers to ignore or respond slowly to them.
Alerts Warnings or additional information provided to clinicians to help with decision making; the action of the clinician or system
triggers the generation of an alert. For example, an alert could be generated if the patient’s serum potassium level is high and he is
on potassium chloride; the system would alert the nurse on the screen (soft copy alert) with or without audio and/or by a printed
(hard copy alert) warning. Also known as triggers.
Algorithm Step-by-step procedure for problem solving or calculating; a set of rules for problem solving. In data mining, it defines the
parameters of the data mining model; it is the recipe or method with which the data mining model is developed.
Allele One member of a pair or series of genes that occupy a specific position on a specific chromosome.
Alternatives Choices between two or more options.
American Library Association (ALA) A U.S.-based organization that promotes libraries and library education internationally.
American National Standards Institute (ANSI) An organization dedicated to promoting consensus on norms and guidelines related
to the assessment of health agencies.
American Recovery and Reinvestment Act (ARRA) An economic stimulus package enacted in February 2009 that was intended to
create jobs and promote investment and consumer spending during the recession. This act has also been referred to as the Stimulus
or Recovery Act. There was a push for widespread adoption of health information technology, and Title XIII of ARRA was given a
subtitle: Health Information Technology for Economic and Clinical Health (HITECH) Act. Through this act, healthcare
organizations can qualify for financial incentives based on the level of meaningful use achieved; the HITECH Act specifically
incentivizes health organizations and providers to become meaningful users.
Analysis Separating a whole into its elements or component parts; examination of a concept or phenomena, its elements, and their
relations.
Analytical model A method, process, and structure for analyzing and examining a data set.
Antiprinciplism Theory that emerged with the expansive technological changes in recent years and the tremendous rise in ethical
dilemmas accompanying these changes. Opponents of principlism include those who claim that its principles do not represent a
theoretical approach and those who claim that its principles are too far removed from the concrete particularities of everyday
human existence; the principles are too conceptual, intangible, or abstract; or the principles disregard or do not take into account a
person’s psychological factors, personality, life history, sexual orientation, religious, ethnic, and cultural background.
Antivirus software A computer program that is designed to recognize and neutralize computer viruses—that is, malicious codes that
replicate over and over and eventually take over the computer’s memory and interfere with its normal functioning.
App Software used on a smartphone or other mobile device.
Application The implementation software of a computer system. This software allows users to complete tasks such as word
processing, developing presentations, and managing data.
Archetype Broad or general, idealized model of an object or concept from which similar instances are derived, copied, patterned, or
emulated; the original model after which other similar things are patterned; the first form from which varieties arise or imitations
are made.
Arithmetic logic unit (ALU) Essential building block of the central processing unit of a computer that digitally performs arithmetic
and logical functions.
Art of nursing The relationship-centered aspects of nursing care, in which the focus is on communicating caring and providing
emotional support and comfort to the patient.
Artificial intelligence (AI) The field that deals with the conception, development, and implementation of informatics tools based on
intelligent technologies. This field attempts to capture the complex processes of human thought and intelligence.
Asynchronous That which is not synchronous; not in real time, or does not occur or exist at the same time, having the same period or
time frame. Learning anywhere and at any time using Internet and World Wide Web software tools (e.g., course management
systems, e-mail, electronic bulletin boards, webpages) as the principal delivery mechanisms for instruction.
Attribute Quality or characteristic; field or element of an entity in a database.
Audiopod Utility to download podcasts.
Augmented-reality game (ARG) The use of a device, such as a smartphone, to overlay on the real world and bring people together
physically and virtually to solve a series of challenges.
Authentication Processes to serve to authenticate or prove who is accessing the system.
Autonomy The right of an individual to choose for himself or herself.
Avatar Image on the Internet that represents the user in virtual communities or other interactions on the Internet; three-dimensional or
two-dimensional image representing one user on the Internet.
Bagging The use of voting and averaging in predictive data mining to synthesize the predictions from many models or methods or for
using the same type of a model on different data; it deals with the unpredictability of results when complex models are used to data
mine small data sets.
Bar-code medication administration (BCMA) A system using bar-code technology affixed to the medication, the patient ID
bracelet, and the nurse ID badge to support the five rights of medication administration.
Behavioral Risk Factor Surveillance System (BRFSS) An assessment system initially designed to collect information on the
movement of mentally impaired persons from state-operated facilities into community settings. The assessments have since been
expanded to include other populations and are designed to determine the effectiveness of programs in meeting the healthcare needs
of at-risk populations.
Beneficence Actions performed that contribute to the welfare of others.
Big data Voluminous amounts of data sets that are difficult to process using typical data processing; huge amounts of semistructured
and unstructured data that are unwieldy to manage within relational databases. Unstructured big data residing in text files represent
more than 75% of an organization’s data.
Binary system System used by computers; a numeric system that uses two symbols: 0 and 1.
Bioethics The study and formulation of healthcare ethics. Bioethics takes on relevant ethical problems experienced by healthcare
providers in the provision of care to individuals and groups.
Bioinformatics (BI) The application of computer science, information science, and cognitive science principles to biological systems,
especially in the human genome field of study; an interdisciplinary science that applies computer and information sciences to solve
biological problems.
Biomedical informatics Interdisciplinary science of acquiring, structuring, analyzing, and providing access to biomedical data,
information, and knowledge to improve the detection, prevention, and treatment of disease.
Biometrics Study of processes or means to uniquely recognize individual users (humans) based on one or more intrinsic physical or
behavioral attributes or characteristics. Authentication devices that recognize thumb prints, retinal patterns, or facial patterns are
available. Depending on the level of security needed, organizations will commonly use a combination of these types of
authentication.
BIOS Basic input/output system, binary input/output system, basic integrated operating system, or built-in operating system; a system
that resides or is embedded on a chip that recognizes and controls a computer’s devices.
Bioterrorism The use of pathogens or other potentially harmful biological agents to sicken or kill members of a targeted population.
Informatics database applications are used to track strategic indicators, such as emergency room visits, disease case reports,
frequency and type of lab testing ordered by physicians and/or nurse practitioners, missed work, and over-the-counter medication
purchases, that may indicate an outbreak that can be attributed to bioterrorism.
Bit (b) Unit of measurement that holds one binary digit, 0 or 1. The smallest possible chunk of data memory used in computer
processing, making up the binary system of the computer.
Blended An approach to education that combines traditional face-to-face instruction with technology-based (online) instruction. See
also hybrid.
Blog Interactive, online weblog. Typically a combination of what is happening on the Web as well as what is happening in the
blogger’s or creator’s life. A blog is as unique as the blogger or person creating it. Thought of as a diary and guide.
Blogger Someone who creates and maintains a blog; a person who blogs.
Boosting Increasing the power of models by weighting the combinations of predictions from those models to create a predicted
classification; an iterative process using voting or averaging to combine the different classifiers.
Borrowed theory Theories borrowed or made use of from other disciplines. As nursing began to evolve, theories from other
disciplines (e.g., psychology, sociology) were adopted to try to empirically describe, explain, or predict nursing phenomena. As
nursing theories continue to be developed, nurses are now questioning whether these borrowed theories were sufficient or
satisfactory in their relation to the nursing phenomena they were used to describe, explain, or predict.
Bracketing The process of setting aside preconceived notions or expectations in the patient encounter to allow the nurse to assess the
patient’s individual and unique response to the health challenge.
Brain The central information processing unit of humans. An organ that controls the central nervous system, is responsible for
cognition and the interpretation, processing, and reaction to sensory input.
Browser Software used to locate and display webpages. Also known as a web browser or Internet browser.
Brushing A technique whereby the user manually chooses specific data points or observations or subsets of data on an interactive data
display; these data can be visualized in two-dimensional or two-dimensional surfaces as scatterplots. Also known as graphical
exploratory data analysis.
Building block Basic element or part of nursing informatics such as information science, computer science, cognitive science, and
nursing science.
Bus Subsystem that transfers data between a computer’s internal components or between computers.
Byte (B) Unit of memory equal to eight bits or eight informational storage units, which represents one keystroke (e.g., any push of a
key on a keyboard such as pressing the space bar, a lowercase “a” or an uppercase “T”). It is considered the best way to indicate
computer memory or storage capacity.
Cache memory Smaller and faster memory storage used by a computer’s central processing unit to store copies of frequently used
data in main memory.
Call centers Registered nurse–staffed facilities at which nurses typically act as case managers for callers or perform patient triage.
Care ethics An ethical approach to solving moral dilemmas encountered in health care that is based on relationships and a caring
attitude toward others.
Care plan A set of guidelines that outline the course of treatment and the recommended interventions that will achieve optimal results.
Caring The nontechnical aspects of nursing interventions that communicate acceptance and concern for a patient.
Caritas processes Nursing interventions that communicate loving concern for the unique humanity of every patient.
Case management information system (CMIS) Computer programs and information management tools that interact to support and
facilitate the practice of case managers.
Case study An account of a nursing informatics activity, event, or problem containing some of the background and complexities
actually encountered by a nurse. The case is used to enhance one’s learning about nursing informatics principles, practices, and
trends. Each case describes a series of events that reflect the nursing informatics episode as it actually occurred.
Casuist approach An approach to ethical decision making that grew out of the concern for more concrete methods of examining
ethical dilemmas. Casuistry is a casebased ethical reasoning method that analyzes the facts of a case in a sound, logical, and
ordered or structured manner. The facts are compared to the decisions arising out of consensus in previous paradigmatic or model
cases.
Centering The act of taking a moment to clear one’s mind of clutter and focus one’s attention exclusively on a patient prior to
engaging in a therapeutic encounter.
Centers for Disease Control and Prevention (CDC) An agency of the U.S. Department of Health and Human Services that works to
protect public health and safety related to disease control and prevention.
Centers for Medicare and Medicaid Services (CMS) The largest health insurer in the United States, particularly for home healthcare
services, and for the elderly, for healthcare services.
Central processing unit (CPU) Processors that execute computer programs, thought of as the brain controlling the functioning of the
computer; the computer component that actually executes, calculates, and processes the binary computer code instigated by the
operating system and other applications on the computer. It serves as the command center that directs the actions of all other
components of the computer and manages both incoming and outgoing data.
Central stations Multifunctional telehealthcare platforms for receiving, retrieving, and/or displaying patients’ vital signs and other
information transmitted from telecommunications-ready medical devices.
Certification System for validating that a nurse possesses certain skills and knowledge or is competent to complete a task.
Competence and skill level are determined by or based on an external review, assessment, examination, or education.
Certified EHR technology An electronic health record (EHR) that meets specific governmental standards for the type of record
involved, either an ambulatory EHR used by office-based healthcare practitioners or an inpatient EHR used by hospitals. The
specific standards to be met are set forth in federal regulations.
Change A transition to something different.
Chat Real-time electronic communications; users type what they want to say, and their messages are displayed on the screens of all
participants in the same chat. Internet Relay Chat (IRC) is the Internet protocol for chat.
Chief information officer (CIO) Person involved with the information technology infrastructure of an organization. This role is
sometimes called chief knowledge officer.
Chief technology officer or chief technical officer (CTO) Person focused on organizationally based scientific and technical issues
and responsible for technological research and development as part of the organization’s products and services.
Chronic disease Long-term disease, such as congestive heart failure, diabetes, and respiratory ailments.
Civil monetary penalties (CMP) Fines laid out by the Social Security Act, which the Secretary of Health and Human Services can
assess for many types of noncompliant conduct.
Classification The technique of dividing a data set into mutually exclusive groups.
Classification and regression trees (CART) A decision tree method that is used for sorting or classifying a data set. A set of rules
that can be applied to a new data set that has not been classified; the set of rules is designed to predict which records will have a
specified outcome.
Clinical analytics Process of analysis by which clinical data are used to help make decisions and develop predictive analytics.
Clinical database A collection of related patient records stored in a computer system using software that permits a person or program
to query the data to extract needed patient information.
Clinical decision support (CDS) A computer-based program designed to assist clinicians in making clinical decisions by filtering or
integrating vast amounts of information and providing suggestions for clinical intervention. May also be called a clinical decision
support system (CDSS).
Clinical documentation system Array or collection of applications and functionality; an amalgamation of systems, medical
equipment, and technologies working together that are committed or dedicated to collecting, storing, and manipulating healthcare
data and information and providing secure access to interdisciplinary clinicians navigating the continuum of client care. Designed
to collect patient data in real time and to enhance care by putting data at the clinician’s fingertips and enabling decision making
where it needs to occur—at the bedside. Also known as clinical information system (CIS).
Clinical guidelines Recommendations that serve as a guide to decisions and provide criteria for specific practice areas.
Clinical information system (CIS) Array or collection of applications and functionality; an amalgamation of systems, medical
equipment, and technologies working together that are committed or dedicated to collecting, storing, and manipulating healthcare
data and information and providing secure access to interdisciplinary clinicians navigating the continuum of client care. Designed
to collect patient data in real time and to enhance care by putting data at the clinician’s fingertips and enabling decision making
where it needs to occur—at the bedside. Also known as clinical documentation system.
Clinical outcomes Patients’ results and consequences from clinical interventions.
Clinical practice council Group that uses the information generated by the clinical information systems to design clinical education
programs. Also called nursing practice council.
Clinical practice guidelines (CPGs) Informal or formal rules or guiding principles that a healthcare provider uses when determining
diagnostic tests and treatment strategies for individual patients. In the electronic health record, they are included in a variety of
ways such as prompts, pop-ups, and text messages.
Clinical transformation The complete alteration of the clinical environment; widespread change accompanies transformational
activities, and clinical transformation implies that the manner in which work is carried out and the outcomes achieved are
completely different from the prior state, which is not always true in the case of simply implementing technology. Technology can
be used to launch or in conjunction with a clinical transformation initiative; however, the implementation of technology alone is
not justifiably transformational ability. Therefore, this term should be used cautiously to describe redesign efforts.
Cloud computing Web browser–based login-accessible data, software, and hardware; could link systems together and reduce costs.
Cloud storage Data storage provided by networked online servers that are typically outside of the institution whose data are being
housed.
Coded terminology Nursing terminologies that are given a specific and standardized designation so that they can be easily entered
into computerized nursing documentation systems, searched for, and easily retrieved.
Codify To classify, reduce to code, or articulate.
Cognitive That which uses one’s capacity to think. The process of cognition is important to generate knowledge. Conscious
intellectual or mental activity such as thinking, reasoning, and remembering, it includes imagination or the ability to imagine and
the ability to learn.
Cognitive activity Any process or task (activity) that involves the capacity to think, reason, imagine, and learn.
Cognitive informatics (CI) Field of study made up of the disciplines of neuroscience, linguistics, artificial intelligence, and
psychology. This multidisciplinary study of cognition and information sciences investigates human information-processing
mechanisms and processes and their engineering applications in computing.
Cognitive science Interdisciplinary field that studies the mind, intelligence, and behavior from an information processing perspective.
Cognitive task analysis (CTA) Examination of the nature of a task by breaking it down into its component parts and identifying the
performers’ thought processes.
Cognitive walkthrough A technique used to evaluate a computer interface or a software program by breaking down and explaining
the steps that a user will take to accomplish a task.
Cognitive work analysis (CWA) A multifaceted analytic procedure developed specifically for the analysis of complex, high-
technology work domains.
Collaboration The sharing of ideas and experiences for the purposes of mutual understanding and learning.
Column Field or attribute of an entity in a database.
Communication science Area of concentration or discipline that studies human communication.
Communication software Technology programs used to transmit messages via e-mail, telephonically, paging, broadcast (such as
MP3), and Internet (such as instant messaging, Voice-over-Internet Protocol, or Listservs).
Communication system Collection of individual communications networks and transmission systems. In health care, it includes call
light systems, wireless phones, pagers, e-mail, instant messaging, and any other devices or networks that clinicians use to
communicate with patients, families, other professionals, and internal and external resources.
Communications hub A device that captures and assists in the transmission of information from peripheral equipment. A processor
organizes the data, appropriately encrypts the data to assure confidentiality, and transmits the encrypted data to appropriate
decision makers. Data can be transmitted via traditional phone lines, through the Internet, or over wireless networks. Typically the
hub will be a small box, to which peripheral equipment is connected.
Community risk assessment (CRA) A comprehensive examination of a community to identify factors that potentially affect the
health of the members of that community. Often used in public health program planning.
Compact disk—read-only memory (CD-ROM) Disk that can hold approximately 700 megabytes of data accessible by a computer.
Compact disk—recordable (CD-R) Compact disk that can be used once for recording.
Compact disk—rewritable (CD-RW) Compact disk that can be recorded onto many times.
Compatibility The ability to work with each other or other devices or systems; for example, software that works with a computer.
Competency A statement or description of goals, skills, or behaviors to be achieved.
Compliance Conforming or performing in an acceptable manner; correctly following the rules.
Comprehensive Health Enhancement Support System (CHESS) A computer-based system designed to help underserved breast
cancer patients manage their disease.
Computational biology The action complement of bioinformatics and, therefore, biomedicine; it is the actual process of analyzing
and interpreting data.
Computer A machine that stores and executes programs; a machine with peripheral hardware and software to carry out selected
programming.
Computer-aided software engineering (CASE) Systematic application of computer software tools and techniques to facilitate
engineering practice.
Computer-assisted instruction (CAI) Any instruction that is aided by the use of a computer.
Computer-based That which uses the computer to interact; the computer is the base tool.
Computer-based information system (CBIS) Combinations of hardware, software, and telecommunications networks that people
build and use to collect, create, and distribute useful data, typically in organizational settings.
Computer science Branch of engineering (application of science) that studies the theoretical foundations of information and
computation and their implementation and application in computer systems. The study of storage/memory, conversion and
transformation, and transfer or transmission of information in machines—that is, computers—through both algorithms and
practical implementation problems. Algorithms are detailed, unambiguous action sequences in the design, efficiency, and
application of computer systems, whereas practical implementation problems deal with the software and hardware.
Computerized physician order entry (CPOE) system A system that automates the way that orders have traditionally been initiated
for patients. Clinicians place orders within these systems instead of using traditional handwritten transcription onto paper. These
systems provide major safeguards by ensuring that physician orders are legible and complete, thereby providing a level of patient
safety that was historically missing with paper-based orders. They provide decision support and automated alert functionality that
was previously unavailable with paper-based orders.
Conceptual framework Framework used in research to chart feasible courses of action or to present a desired approach to a study or
analysis; built from a set of concepts that are related to a proposed or existing system of methods, behaviors, functions,
relationships, and objects. A relational model. A formal way of thinking or conceptualizing about a phenomenon, process, or
system under study.
Conferencing software Electronic communications system or software that supports and facilitates two or more people meeting for
discussion. High-end systems offer telepresence (a lifelike experience allowing people to feel as if they were present in person—it
would be as though the nurse were physically there with the patient—so people can work, learn, and play in person over the
Internet or have an effect at a remote location).
Confidentiality The mandate that all personal information be safeguarded by ensuring that access is limited to only those who are
authorized to view that information.
Connectionism A component of cognitive science that uses computer modeling through artificial neural networks to try to explain
human intellectual abilities.
Connectivity Ability to hook up to the electronic resources necessary to meet the user’s needs. The ability to use computer networks
to link to people and resources. The unbiased transmission or transport of Internet Protocol packets between two end points.
Consequences Outcomes or products resulting from one’s decision choices.
Consolidated health informatics (CHI) A collaborative effort to adopt health information interoperability standards, particularly
health vocabulary and messaging standards, for implementation in federal government systems.
Consultant A person hired to provide expert advice, opinions, and recommendations based on his or her area of expertise.
Context of care The setting, services, patient, environment, and professional and social interactions surrounding the delivery of
patient interventions.
Continuing education Coursework or training completed after achievement of a baccalaureate degree, often for the purpose of
recertification.
Continuous learner Person who gleans lessons or learns from success as well as failures, or who constantly searches for information
to add to his or her knowledge base.
Copyright A legal term used by many governments around the world that gives the inventor or designer of an original product sole or
exclusive rights to that product for a limited time; the same laws that cover physical books, artwork, and other creative material are
still applicable in the digital world.
Core business system System that enhances administrative tasks within healthcare organizations. Unlike clinical information systems,
whose aim is to provide direct patient care, these systems support the management of health care within an organization. They
provide the framework for reimbursement, support of best practices, quality control, and resource allocation. There are four
common types of core business systems: (1) admission, discharge, and transfer (ADT); (2) financial; (3) acuity; and (4) scheduling
systems.
Core sciences The branches of study and knowledge that form the foundation of nursing informatics, including nursing, computer, and
information sciences. Some, including the editors of this text, believe that cognitive science should also be included in the list of
NI foundational sciences.
Courage The strength to face difficulty.
Course management system (CMS) Software system designed for both faculty and students that supports educational episodes,
including tools for grading, learner assessment, content presentation/interaction, and communication. These systems provide for
the support of learning activities throughout course delivery; proprietary examples include ANGEL, Blackboard, WebCT,
Learning Space, and eCollege.
Covered entity A healthcare provider that conducts certain transactions in electronic form (a “covered healthcare provider”), a
healthcare clearinghouse, or a health plan that electronically transmits any health information in connection with transactions
(billing and payment for services or insurance coverage) for which the U.S. Department of Health and Human Services has
adopted standards; identified in the Administrative Simplification regulations.
Creativity software Programs that support and facilitate innovation and creativity (an intellectual process relating to the creation or
generation of new ideas, concepts, or new relationships between currently existing ideas or concepts); they allow users to focus or
concentrate more on creating new things in today’s digital age and less on the mechanics or workings of how they are created or
developed.
Crowdsourcing Information generated by individuals on social media.
Culture broker Person who can translate between science and clinical care and between science and the self-caring citizen.
Cumulative Index to Nursing and Allied Health Literature (CINAHL) A comprehensive nursing and allied health literature
database.
Data Raw facts that lack meaning.
Data-centric Data are the central focus.
Data dictionary Software that contains a listing of tables and their details, including field names, validation settings, and data types.
Data file A collection of related records.
Data gatherer One involved in the direct procurement of raw facts (data); raw facts (data) collector.
Data mart Collection of data focusing on a specific topic or organizational unit or department created to facilitate management
personnel making strategic business decisions. Could be as small as one database or larger, such as a compilation of databases;
generally smaller than a data warehouse.
Data mining A process of utilizing software to sort through data so as to discover patterns and ascertain or establish relationships.
This process may help to discover or uncover previously unidentified relationships among the data in a database.
Data set Collection of interrelated data.
Data warehouse (DW) An extremely large database or repository that stores all of an organization’s or institution’s data and makes
this data available for data mining. A combination of an institution’s many different databases that provides management personnel
flexible access to the data.
Database A collection of related records stored in a computer system using software that permits a person or program to query the
data so as to extract needed information; it may consist of one or more related data files or tables.
Database management system (DBMS) Software program/s and the hardware used to create and manage data.
Decision making Output of cognition; outcome of our intellectual processing.
Decision support Recommendations for interventions based on computerized care protocols. The decision support recommendations
may include such items as additional screenings, medication interactions, or drug and dosage monitoring.
Decision support/outcomes manager Person charged with reviewing the effects of interventions suggested by the computerized
decision support system.
Decision support system (DSS) Computer applications designed to facilitate human decision-making processes. Usually DSSs are
rule based, using a specified knowledge base and a set of rules to analyze data and information and provide recommendations to
users.
Decision tree A set of decisions represented in a treeshaped pattern; the decisions produce the rules for the classification of a data set.
Degradation Loss of quality; for example, in telecommunications, it is the loss of quality in the electronic signal.
Desktop Computer’s interface that resembles the top of a desk, where the user keeps things he or she wants to access quickly, such as
paper clips, pens, and paper. On the computer’s desktop, the user can customize the look and feel to have easy access to the
programs, folders, and files on the hard drive that the individual uses the most.
Digital divide The gap between those who have and those who do not have access to online information.
Digital health record (DHR) An electronic record of patient assessments that are collected over time, typically by a telemonitoring
device. For example, daily assessments of weight and blood pressure can be captured electronically and graphically displayed to
allow for the detection of subtle trends.
Digital pen A writing implement that can also digitally capture handwriting or drawings. This device is battery operated and generally
comes with a universal serial bus (USB) cradle that permits uploading captured materials to a desktop, laptop, or palmtop
computer. The user can use it as a ballpoint pen and write on regular paper just as he or she would with a normal pen or can capture
the output digitally after writing on digital paper.
Digital video disk/digital versatile disk (DVD) Optical disk storage format that can generally hold or store more than six times the
amount of data that a compact disk can.
Digital video disk—recordable (DVD-R) Disk on which a user can record once.
Digital video disk—recordable and rewritable (DVD-RW) Disk on which a user can record many times.
Dimension A collection of related attributes that provide information about the data or the context of the facts. In a multidimensional
database, a set of similar entities is known as a dimension; in a relational database, each field is considered a dimension.
Dissemination A thoughtful, intentional, goal-oriented communication of specific, useful information or knowledge.
Distance education Education provided from a remote location.
Document To capture and save information for later use.
Documentation Communication in the form of written or typed text, audio, video, graphics, photographs, pictures, or any blending of
these means used to describe some characteristics or elements of an object, system, or practice. For example, nursing
documentation generates information about a patient (individual, family, group, community, populations) that describes the care
and/or services that have been provided and allows for the communication necessary between nurses and other healthcare
providers.
Domain name A series of alphanumeric characters that forms part of the Internet address or URL (e.g., psu.edu denotes Penn State’s
address).
Drill-down A means of viewing data warehouse information by going down to lower levels of the database to focus on information
that is pertinent to the user’s needs at the moment.
Duty One’s feeling of being bound or obligated to carry out specific tasks or roles based on one’s rank or position.
Dynamic random-access memory (DRAM) Type of RAM chip requiring less space to store the same amount on a similar static
RAM (SRAM) chip; however, DRAM requires more power than SRAM because DRAM needs to keep its charge by constantly
refreshing.
Dynamic system development method (DSDM) An agile software development strategy based on the rapid application development
model, which is iterative and used in the system development life cycle and project management.
Dynamic webpage shells Webpages that can be custom scripted to provide realistic case scenarios during a simulation experience.
E-brochure Electronic brochure. Patient education material that is typically tied to an agency website and may include such
information as descriptions of diseases and their management, medication information, or where to get assistance with a healthcare
issue.
E-health Healthcare initiatives and practice supported by electronic or digital media. The most typical use is in patient and family
education where information is communicated electronically.
E-learning Electronic learning or learning that is facilitated by electronic means such as computers and the Internet. E-learning,
online, and Web-based education has caused a significant shift in student–teacher relationships in nursing education.
E-mail Electronic mail. To compose, send, receive, and store messages in electronic communication systems.
E-mail client Program that manages e-mail functions.
E-portfolio Personalized collections of evidence from coursework, experiences outside of the classroom, and reflective commentary
related to this evidence that can be shared with others electronically; categorized electronic presentation of one’s skills, education,
and examples of work and/or career achievements.
Earcons Auditory tones that are combined to represent relationships among data elements, such as the relationship of systolic blood
pressure to diastolic blood pressure.
Educational Resources Information Center (ERIC) A comprehensive educational resources database. An international database of
educational literature.
Educator Sage, leader, and/or guide who assists in the process or practice of learning.
eHealth Initiative Initiative developed to address the growing need for managing health information and to promote technology as a
means of improving health information exchange, health literacy, and healthcare delivery.
Electronic communication Any exchange of information that is transmitted electronically.
Electronic data interchange (EDI) Specific set of standards for exchanging information between or among computers (computer to
computer).
Electronic health record (EHR) A computer-based data warehouse or repository of information regarding the health status of a
client, which is replacing the former paper-based medical record; it is the systematic documentation of a client’s health status and
health care in a secured digital format, meaning that it can be processed, stored, transmitted, and accessed by authorized
interdisciplinary professionals for the purpose of supporting efficient, high-quality health care across the client’s healthcare
continuum. Also known as electronic medical record (EMR).
Electronic mailing list Automatic mailing list server such as LISTSERV that sends an e-mail addressed to the list to everyone who
has subscribed to the list automatically. Similar to an electronic bulletin board or news forum.
Electronic medical record (EMR) See electronic health record (EHR).
Electronically erasable programmable read-only memory (EEPROM) A nonvolatile storage chip used in computers and other
devices to store small amounts of volatile data (e.g., calibration tables or device configuration).
Emerging technologies New technology that is likely to impact health care in a significant way, such as nanotechnology or
biotechnology.
Empiricism Knowledge that is derived from our experiences or senses.
Empowerment Promotion of self-actualization; achievement of power or control over one’s own life.
End users Target users or consumers of software and computer technology. Software or computing applications should be designed
for the end user, the person who will ultimately be using them.
Enterprise integration Electronically linking healthcare providers, health plans, government, and other interested parties to facilitate
electronic exchange and use of health information among all stakeholders.
Entity See covered entity.
Entity–relationship diagram (ERD) Diagram that specifies the relationships among the entities in the database. Sometimes the
implied relationships are apparent based on the entities’ definitions; however, all relationships should be specified as to how items
relate to one another. There are typically three relationships: one to one, one to many, and many to many.
Entrepreneur Person who assumes the risks of beginning an enterprise or business and accepts responsibility for organizing and
managing the organization.
Enumerative approach Nursing terminology in which words or phrases are represented in a list or a simple hierarchy; gives an
explicit and exhaustive listing of all the objects that fall under the concept or term in question.
Epidemiology The field of study identifying things that come upon the people. Incidence, prevalence, and control of disease. Case
finding.
Epistemology Study of the nature and origin of knowledge; what it means to know.
Erasable programmable read-only memory (EPROM) Type of computer memory chip that retains its data when its power supply
is switched off and can be erased with ultraviolet light.
Ergonomics In the United States, this term is used to describe the physical characteristics of equipment—for example, the optimal fit
of a scissors to a human hand. In Europe, it is synonymous with human factors—that is, the interaction of humans with physical
attributes of equipment or the interaction of humans and the arrangement of equipment in the work environment.
Ethical decision making The process of making informed choices about ethical dilemmas based on a set of standards differentiating
right from wrong. The decision making reflects an understanding of the principles and standards of ethical decision making, as
well as philosophical approaches to ethical decision making. It requires a systematic framework for addressing the complex and
often controversial moral questions.
Ethical dilemma A difficult choice or issue that requires the application of standards or principles to solve. Issues that challenge us
ethically.
Ethical, social, and legal implications (ESLI) Consideration and understanding of the ethical, social, or legal connections or aspects
of an issue that relate to a moral question of right and wrong.
Ethicist Expert in the arbitrary, ambiguous, and ungrounded judgments of other people. Ethicists know that they make the best
decision they can based on the situation and stakeholders at hand.
Ethics A process of systematically examining varying viewpoints related to moral questions of right and wrong.
Eudaemonistic A system of ethical evaluation that involves consideration of which actions lead to being an excellent and happy
person.
Events Occurrences that might be significant to other objects in a system or to external agents; for example, creating a laboratory
request is an example of a healthcare event in a laboratory application. An event is defined and could be a triggering event for the
task or workflow; a task or workflow can have several triggering events.
Evidence Artifacts, productions, attestations, or other examples that demonstrate an individual’s knowledge, skills, or valued
attributes.
Evidence-based practice (EBP) Nursing practice that is informed by research-generated evidence of best practices.
Exabyte (EB) One quintillion bytes of computer memory.
Execute To carry out software’s or a program’s instructions.
Expert system A type of decision support system that implements the knowledge of one or more human experts.
Exploratory data analysis (EDA) Approach or philosophy that uses mainly graphical techniques to gain insight into a data set. It
identifies the most important variables. Conducted during the exploratory phase, EDA provides guidance into the complexity or
general nature of the various models that should be considered for implementation during pattern discovery.
Extensibility System design feature that allows for future expansion without the need for changes to the basic infrastructure.
Extensible Markup Language (XML) Computer language that began as a simplified subset of Standard Generalized Markup
Language (SGML). Its major purpose is to facilitate the exchange of structured data across different information systems,
especially via the Internet. XML is considered an extensible language because it permits its users to define their own elements,
allowing customization to enable purpose-specific development.
Face-to-face Most widely used teaching method among nurse educators, where the teacher and the learners meet together in one
location at the same time.
Failure modes and effects analysis (FMEA) A systematic evaluation of a process to determine how and why it failed to produce the
desired results.
Fair use Doctrine that permits the limited use of original works without the copyright holder’s permission; an example would be
quoting or citing an author in a scholarly manuscript.
Federal Health Information Exchange (FHIE) A federal information technology healthcare initiative that enables the secure
electronic one-way exchange of patient medical information from the Department of Defense’s legacy health information system,
the Composite Health Care System (CHCS), for all separated service members to the Veterans Affairs’ (VA) VistA Computerized
Patient Record System (CPRS). The point of care in veterans affairs.
Feedback Input in the form of opinions about or reactions to something such as shared knowledge. In an information system, feedback
refers to information from the system that is used to make modifications in the input, processing actions, or outputs.
Fidelity The extent to which a simulation mimics the processes of a real environment.
Field Column or attribute of an entity in a database.
Field study Study in which end users evaluate a prototype in the actual work setting prior to its general release. Also called field test,
alpha test, or beta test.
Financial system System used to manage the expenses and revenues accrued while providing health care. The finance, auditing, and
accounting departments within an organization most commonly use financial systems. These systems determine the direction for
maintenance and growth for a given facility. Financial systems often interface to share information with materials management,
staffing, and billing systems to balance the financial impact of these resources within an organization. These systems report the
fiscal outcomes so that these outcomes can be tracked against the organizational goals of an institution. Financial systems are one
of the major decision-making factors as healthcare institutions prepare their fiscal budgets. They often play a pivotal role in
determining the strategic direction for an organization.
Firewall A tool commonly used by organizations to protect their corporate networks when they are attached to the Internet. A firewall
can be either hardware or software, or a combination of the two. It examines all incoming messages or traffic to the network. The
firewall can be set up to allow only messages from known senders into the corporate network; it can also be set up to look at
outgoing information from the corporate network.
FireWire Apple Computer’s version of a high-performance serial bus used to connect devices to a computer.
Firmware Hardware and software programs or data written onto ROM, PROM, and EPROM.
Flash drive Small, removable storage device.
Flash memory Special type of EEPROM that can be erased and reprogrammed in blocks instead of one byte at a time. Many modern
PCs have their BIOS stored on a flash memory chip so that it can easily be updated if necessary.
Folksonomies Organization and classification of online content by users; derived from “folk” and “taxonomy.” Users tag information
with key words to make it easier to index and search vast amounts of information.
Foundation of Knowledge model Model proposing that humans are organic information systems constantly acquiring, processing,
generating, and disseminating information or knowledge in both their professional and personal lives. The organizing framework
of this text.
Free/open source software(F/OSS or FOSS) Software that enables users to freely copy and reuse or repurpose the software by
providing access to the source code; free and open use of software source code.
Futurologist Guru who is a forward thinker and looks to the future.
Game A structured activity undertaken for enjoyment.
Genome A body of genes. Hans Winkler is credited with merging “genesis” and “soma” (genome) to create this term.
Genomics The study of the genome.
Gigabyte (GB) Unit of measure used to express bytes of data storage and capability in computer systems; 1 gigabyte equals 1,000
megabytes.
Gigahertz (GHz) Unit of measure used to express speed and power of some components such as the microprocessor; 1 gigahertz
equals 1,000 megahertz.
Good Favorable outcome in ethics.
Google Glass A wearable computer from Google that can take pictures, play video, and display text messages without anyone else
knowing. Currently, it costs approximately $1,500.
Gramm-Leach-Bliley Act (GLBA) Federal legislation in the United States that controls how financial institutions handle the private
information they collect from individuals.
Graphical user interface (GUI) Software that provides a user-friendly desktop metaphor interface that is made up of the input and
output devices as well as icons that represent files, programs, actions, and processes.
Graphics card A board that plugs into a personal computer to give it display capabilities.
Gray gap A term used to reflect the age disparities in computer connectivity; there are fewer persons older than age 65 who use
computer technology than members of younger age groups.
Gulf of evaluation The gap between knowing one’s intention (goal) and knowing the effects of one’s actions.
Gulf of execution The gap between knowing what one wants to have happen (the goal) and knowing what to do to bring it about (the
means to achieve the goal).
Hacker Computer-savvy individual most commonly thought of as a malicious person who hacks or breaks through security to steal or
alter data and information; can also be any of a group of computer aficionados who band together in clubs and organizations or
who use their skills as a hobby.
Half-life of knowledge The time span from when knowledge is gained to when it becomes obsolete.
Haplotype Set of closely linked alleles on a chromosome that tends to be inherited together.
Hard disk Magnetic disk that stores electronic data.
Hard drive Permanent data storage area that holds the data, information, documents, and programs saved on the computer, even when
the computer is shut off. The actual physical body of the computer and its components.
Hardware Physical or tangible parts of the computer. Computer parts that one can touch and that are involved in the performance or
function of the computer, such as the keyboard and monitor.
Harm Physical or mental injury or damage. Unfavorable outcome in ethics.
Health disparities The health status differences between different groups of people, especially minorities and nonminorities; the gaps
between the health status of minorities and nonminorities in the United States are ongoing even with the advances in technology
and healthcare practices.
Health information exchange (HIE) Organization that prepares and organizes people and resources to manage healthcare
information electronically across organizations within a community or region.
Health Information Portability and Accountability Act (HIPAA) Law signed by President Bill Clinton in 1996 addressing the need
for standards to regulate and safeguard health information and making provisions for health insurance coverage for employed
persons who change jobs.
Health information technology (HIT) Hardware, software, integrated technologies or related licenses, intellectual property,
upgrades, or packaged solutions sold as services that are designed for or support the use by healthcare entities or patients for the
electronic creation, maintenance, access, or exchange of health information.
Health Information Technology for Economic and Clinical Health (HITECH) Act Title XIII of the American Recovery and
Reinvestment Act, which was enacted in February 2009. Under this act, healthcare organizations can qualify for financial
incentives based on the level of meaningful use achieved; the HITECH Act specifically incentivizes health organizations and
providers to become “meaningful users.”
Health Level 7 (HL7) An accredited standards-developing organization that is committed to developing standard terminologies for
information technology that support interoperability of healthcare information management systems.
Health literacy The acquisition of knowledge that promotes the ability to understand and to manage one’s health.
Health management information system (HMIS) An information system that is specially intended to support and help with the
planning, resource allocation, and management of health programming to make healthcare more effective and efficient; an
information system that plans and manages health programs, rather than the actual delivery of health care.
Healthcare information Information that is related to health and well-being of a person, especially information related to therapeutic
(care) interactions between people and healthcare providers.
Healthcare provider A qualified person delivering appropriate health care professionally to an individual, group, family, community,
or population in need of healthcare services; includes hospitals, skilled nursing facilities, nursing homes, long-term care facilities,
home health agencies, hemodialysis centers, clinics, community mental health centers, ambulatory surgery centers, group practices,
pharmacies and pharmacists, laboratories, physicians, and therapists.
HealthVault Microsoft’s online personal health record system.
Heuristic evaluation An evaluation in which a small number of evaluators (often experts in relevant fields such as human factors or
cognitive engineering) evaluate the degree to which an interface design complies with recognized usability principles (the
“heuristics”).
High-fidelity A high level of realism generated by the equipment used in simulations.
High-hazard drug A drug known to cause significant adverse side effects when administered inappropriately; a drug subject to
frequent administration errors.
Home health agency (HHA) Organization that delivers parttime and intermittent skilled services, including nursing and other
therapeutic services, in the patient’s home.
Home health care An alternative site for healthcare services typically focusing on post–hospital discharge patient needs.
Home telehealth care Home healthcare clinical and educational services provided via telecommunicationsready tools.
HONcode One of the two most common symbols that power users look for to identify trusted health sites.
Hospital information system (HIS) An information system intended to manage the clinical, financial, and administrative needs of the
hospital; refers to the paperbased as well as computer-based information processing that manages the functional aspects
(administrative, financial, and clinical) of a hospital.
Human–computer interaction (HCI) The study of how people use computers and software applications and the ways that computers
influence people.
Human–computer interface The hardware and software through which the user interacts with the computer.
Human factors engineering Recognizing the limitations of human performance and developing products to overcome these
limitations.
Human Genome Project (HGP) A 13-year project designed to identify all of the 20,000–25,000 genes in human DNA; determine the
sequences of 3 billion chemical base pairs in human DNA; create databases to store this information; and address the resultant
ethical, legal, and social issues.
Human–technology interaction (HTI) How users interact with technology. The study of that interaction.
Human–technology interface The hardware and software through which the user interacts with any technology (e.g., computers,
patient monitors, telephone).
Hybrid A descriptor for individual courses in which instruction is delivered using multiple formats such as online, face to face, print
based, or audio or videoconference (e.g., PicTel).
Hypertext Clickable words that allow users to access another document at a remote location.
Indiana Health Information Exchange (IHIE) A collaborative effort among institutions in Indiana to provide high-quality patient
care and enhance the safety and efficiency of health care.
Industrial Age Late 18th and early 19th centuries, when major changes occurred in manufacturing, farming, and transportation;
inventions and innovations led these changes.
Informaticist A person with specialized training or certification in informatics; one who is a specialist in using technology to manage
health data and information.
Informatics A field that integrates a specialty’s science, computer science, cognitive science, and information science to manage and
communicate data, information, knowledge, and wisdom in a specialty’s practice.
Informatics innovation Process of making enhancements or improvements and creative, novel, and inventive solutions in the
informatics specialty.
Informatics nurse (IN) A nurse with specialized skills, knowledge, and competencies in informatics. A registered nurse with an
interest or experience working in an informatics field. A generalist in the field of informatics in nursing.
Informatics nurse specialist (INS) A registered nurse with formal, graduate education in the field of informatics or a related field,
who is considered a specialist in the field of nursing informatics.
Informatics solution A generic term used to describe the product that an informatics nurse specialist recommends after identifying
and analyzing an issue. Informatics solutions may encompass technology and nontechnology products such as information
systems, new applications, nursing vocabulary, or informatics curricula.
Information Data that are interpreted, organized, or structured. Data processed using knowledge or data made functional through the
application of knowledge.
Information Age Period at the end of the 20th century, when information was easily accessible using computers, networks, and the
Internet.
Information literacy Recognizing when information is needed and having the ability to locate, evaluate, and effectively use the
needed information. An intellectual framework for finding, understanding, evaluating, and using information.
Information mediator A new nursing role arising out of the need for technology to support immediate access to up-to-date
knowledge anywhere and anytime.
Information science The science of information, studying the application and usage of information and knowledge in organizations
and the interfacings or interaction between people, organizations, and information systems. An extensive, interdisciplinary science
that integrates features from cognitive science, communication science, computer science, library science, and social sciences.
Information system (IS) The manual and/or automated components of a system of users or people, recorded data, and actions used to
process the data into information for a user, group of users, or an organization.
Information technology (IT) Use of hardware, software, services, and supporting infrastructure to manage and deliver information
using voice, data, and video, or the use of technologies from computing, electronics, and telecommunications to process and
distribute information in digital and other forms. Anything related to computing technology, such as networking, hardware,
software, the Internet, or the people who work with these technologies. Many hospitals have IT departments for managing the
computers, networks, and other technical areas of the healthcare industry.
Information user The person who accesses and makes use of information made available to her/him.
Informatique French term referring to the computer milieu.
Infrastructure Structural elements that provide the framework supporting a system. In the case of information technology,
infrastructure refers to the architecture of the computer system, its operating system, and various other systems that are
fundamental to its operation.
Input Data and information entered into a computer system.
Input devices Hardware and software used to enter data and information into a computer.
Input/output system (I/O) (Pronounced “eye-oh.”) Any program, operation, or device that transfers data to or from a computer and to
or from a peripheral device.
Instant message (IM) Form of real-time communication between two or more people based on typed text conveyed via computers
connected over a network.
Integrated drive electronics (IDE) Technology where the drive controller is located on the drive itself instead of being a separate
controller connected to the motherboard of a computer.
Integration Assimilating or combining to make whole; in computer terminology, the process through which different technologies—
software and hardware components—are synchronized and combined to make a functional and structural system.
Integrity Quality and accuracy. Employees need to have confidence that the information they are provided is, in fact, true. To
accomplish this, organizations need clear policies to clarify how data are actually input, to determine who has the authorization to
change such data, and to track how and when data are changed.
Intelligence Mental ability to think logically, reason, prepare, ideate, assess alternative solutions to problems, problem solve by
choosing a proposed solution, think abstractly, comprehend and grasp ideas, understand and use language, and learn.
Interactions Interfacing with users commonly using tasks or notifications.
Interactive technologies Technologies that promote or support user communication with other persons (e.g., e-mail) or technologies
that depend on a user response (e.g., games).
Interdisciplinary knowledge team A team composed of members of various disciplines in a healthcare organization, each of whom
contributes unique knowledge to the team in problem-solving or management situations.
Interface Mechanism or a system used by separate things to interact. For example, if one wants to change a CD in a CD player, one
could use a remote; the human user is not related to the CD player but can interact with it using the remote control. Therefore, the
remote control becomes the interface that enables that person to tell the CD player which CD to play.
International Organization for Standardization (ISO) An international network supporting collaboration among the standards-
developing agencies of numerous countries for the development of consistent standards in a multitude of industries to support a
global economy. ISO is best known in the technology industries for the ISO 9000 standards. See International Standards
Organization.
International Standards Organization (ISO) A nongovernmental group that connects and bridges the public and private sectors to
develop and publish international standards that assimilate the latest expert knowledge, with the goal being to provide practical
tools for tracking economic, environmental, and societal challenges. Also known as International Organization for Standardization.
Internet A global system of computer networks whose connectivity promotes worldwide communications via computers.
Internet2 A nonprofit consortium that develops and deploys advanced network applications and technologies, for education and high-
speed data transfer purposes. Led by 212 universities, it is also known as University Corporation for Advanced Internet
Development.
Internet browser Software used to locate and display webpages. Also known as web browser or browser.
Interoperability Ability of various systems and organizations to work together to exchange information.
Intranet A computer network that is contained within an enterprise and that has restricted access; it has the look and feel of the
Internet and often provides links to the Internet. The purpose of an intranet varies but can include provision of employee and
departmental directories, policies and procedures, internal and external resources, schedules, and updates on programs and
business. The benefits are browsing capabilities and the ability to maintain contact information and phone numbers in a central
location, with easy dissemination.
Intrusion detection devices Both hardware and software that allow an organization to monitor who is using its network and which
files that user has accessed.
Intrusion detection system Method of security that uses both hardware and software detection devices as a system that can be set up
to monitor a single computer or an entire network. Corporations must diligently monitor for unauthorized access of their networks.
Intuition A way of acquiring knowledge that cannot be obtained by inference, deduction, observation, reason, analysis, or experience.
Iowa model A model that facilitates the translation of research evidence into clinical practice. Also known as the Iowa model of
evidence-based practice.
iPod The name given to a family of portable MP3 players from Apple Computer.
Iteration Replication and refinement of a method until it meets a goal or provides the desired result; each repetition is referred to as an
iteration.
Jump drive Small, removable storage device.
Justice Fairness. Treatment of everyone in the same way.
Key field Within each database record, one of the fields identified as the primary key. It contains a code, name, number, or other bit of
information that acts as a unique identifier for that record. In a healthcare system, for example, a patient is assigned a patient
number or ID that is unique for that patient.
Keyboard Set of keys resembling an actual typewriter that permits the user to input data into a computer.
Know–do gap Situation that exists because solutions to global health problems are available but are not implemented in a timely
fashion because of the lack of access to important health information. The Internet connections in developing countries are widely
scattered, for example, and may not be efficient or sufficient for viewing healthcare information.
Knowledge The awareness and understanding of a set of information and ways that information can be made useful to support a
specific task or arrive at a decision; abounds with others’ thoughts and information. Information that is synthesized so that
relationships are identified and formalized. Understanding that comes through a process of interaction or experience with the world
around us. Information that has judgment applied to it or meaning extracted from it. Processed information that helps to clarify or
explain some portion of our environment or world that we can use as a basis for action or upon which we can act. Internal process
of thinking or cognition. External process of testing, senses, observation, and interacting.
Knowledge acquisition The act of getting knowledge.
Knowledge brokers People who know where to find information and knowledge. They generate some knowledge but are mainly
known for their ability to find what is needed. More experienced nurses and nursing students become knowledge brokers out of
necessity—needing to know.
Knowledge builder Person who examines, interprets, and compares clinical data and trends with an eye toward improving clinical
practice based on the available evidence.
Knowledge-centric Knowledge is the central focus.
Knowledge consumers Users of knowledge who do not have the expertise to provide the knowledge they need for themselves.
Knowledge dissemination Distribution and sharing of knowledge.
Knowledge domain process (KDP) model Model that represents knowledge construction and dissemination in an organization.
Knowledge exchange The product of collaboration when sharing an understanding of information promotes learning to make better
decisions in the future.
Knowledge generation The creation of new knowledge by changing and evolving knowledge based on one’s experience, education,
and input from others.
Knowledge generators Nursing researchers and nursing experts—the people who know; they are able to answer questions, craft
theories, find solutions to nursing problems or concerns, and innovate practice.
Knowledge management system (KMS) A repository of information that contains the latest collective expertise based on experience
and research. The knowledge is typically stored in a computerized system that promotes easy access for use.
Knowledge processing The activity or process of gathering or collecting, perceiving, analyzing, synthesizing, saving or storing,
manipulating, conveying, and transmitting knowledge.
Knowledge repositories Collections of information made available to an organization’s workers to support and inform their work.
Knowledge user Individuals or groups who benefit from valuable, viable knowledge.
Knowledge workers Those who work with information and generate information and knowledge as a product.
Lab-on-a-chip device A nanotechnology device designed to perform blood analyses.
Laboratory information system A system that reports on blood, body fluid, and tissue samples along with biological specimens that
are collected at the bedside and received in a central laboratory. These systems provide clinicians with reference ranges for tests
indicating high, low, or normal values so that they can make care decisions. Often the laboratory system provides result
information directing clinicians toward the next course of action within a treatment regimen.
Laptop Portable battery-powered computer that the user can take with him or her. Also known as a notebook.
Legacy system Old computer systems or programs that are not replaced because the institution does not want to expend the resources;
they can cause problems especially in interfacing with newer systems.
Liberty The independence from controlling influences.
Library science An interdisciplinary science that integrates law, applied science, and the humanities, to study issues and topics related
to libraries (collection, organization, preservation, archiving, and dissemination of information resources).
Local area network (LAN) Organizationally based network; joined together locally.
Logic A system of thinking that uses principles of inference and reasoned ideas to govern action.
Long-term care facility A healthcare institution designed to support the needs of those who need ongoing care, especially the aged.
Longevity Usability beyond the immediate clinical encounter. Long-term value.
Machine learning A subset of artificial intelligence that permits computers to learn either inductively or deductively. Inductive
machine learning is the process of reasoning and making generalizations or extracting patterns and rules from huge data sets—that
is, reasoning from a large number of examples to a general rule. Deductive machine learning moves from premises that are
assumed true to conclusions that must be true if the premises are true.
Main memory A computer’s internal memory.
Mainframe An extremely high-performance computer that is smaller than a supercomputer, used for highvolume, processor-intensive
computing. Computers used by some large businesses and/or for scientific processing purposes.
Malicious code Software that includes spyware, viruses, worms, and Trojan horses.
Malicious insider An insider or employee who sabotages or adds malicious code or hacks into systems to cause damage or to steal
data and information.
Malware Malicious software; an evil, malicious program that infects a device and is intended to steal information, take control,
irritate, damage, or destroy data, information, or the device.
Managed care information system An information system that crosses organizational boundaries so that data can be obtained at any
and all of the patient areas; these information systems make it possible for nurses and physicians to make clinical decisions while
being mindful of their financial ramifications.
Mapping How environmental facts (e.g., the order of light switches or variables in a physiologic monitoring display) are accurately
depicted by the information presentation.
Mask Method that a proxy server uses to protect the identity of a corporation’s employees while they are surfing the World Wide
Web. The proxy server keeps track of which employees are using which masks and directs the traffic appropriately.
Massachusetts Health Data Consortium (MHDC) A consortium of regional healthcare organizations that collects data, publishes
comparative information, supports and promotes electronic standards, educates, and researches.
Massive multiplayer online role-playing game (MMORPG, MMO) A game using the Internet to provide a shared, simultaneous
experience for dozens or even hundreds of players.
Master patient index A tool that identifies, compares, removes duplicate entries, combines, and cleans patient records so that they can
be added to a master index; it provides a comprehensive and single view of a patient via that person’s record while establishing a
master index of all patients.
Meaningful use The American Recovery and Reinvestment Act of 2009 specifies three main components of meaningful use: (1) the
use of a certified electronic health record (EHR) in a meaningful manner, such as e-prescribing; (2) the use of certified EHR
technology for electronic exchange of health information to improve quality of health care; and (3) the use of certified EHR
technology to submit clinical quality and other measures. The criteria for meaningful use will be staged in three steps. Stage 1
(2011–2012) sets the baseline for electronic data capture and information sharing. Stage 2 (2013) and Stage 3 (expected to be
implemented in 2015) will continue to expand on this baseline and be developed through future rule making.
Medicaid management information system (MMIS) An integrated group of procedures and computer processing operations
(subsystems) developed at the general design level to meet Medicaid’s principal objectives of control and administrative costs;
service to recipients, providers, and inquiries; operations of claims control and computer capabilities; and management reporting
for planning and control.
Medical home/health information exchange An information technology platform that enables the seamless exchange of important
patient information among many providers in a healthcare system. Typically the primary care physician (medical home) initiates
the collection of patient data, coordinates the care of the patient, and helps to maintain the accuracy of such data. Other care
providers access the information and add to it as they provide services to patients.
Medical informatics A specialty that integrates medical science, computer science, cognitive science, and information science to
manage and communicate data, information, knowledge, and wisdom in medical practice.
Medication management devices Range of telecommunications-ready medication devices to remind or otherwise alert patients to
medication compliance needs.
MEDLINE A database that contains more than 10 million records, maintained and produced by the National Library of Medicine.
Megabyte (MB) Unit of measure used to express the amount of data storage and capability in computer systems; 1 megabyte equals
1,000 kilobytes.
Megahertz (MHz) Unit of measure used to express the speed and power of some components such as the microprocessor.
Memory Data stored in digital format; generally refers to random-access memory (RAM).
Meta-analysis A form of systematic review that uses statistical methods to combine the results of several research studies.
Meta-learning Learning that combines the predictions from several data mining models with the goal of synthesizing these predicted
classifications to generate a final best predicted classification; also known as stacking.
Metadata Data about data; in data mining, data contained in the data mining model that describes other data. For example, metadata
would describe the patterns, trends, and relationships of the mined data.
Metrics Measurements or a set of measurements to quantify performance; they provide understanding about the performance of a
process or function. Typically, within clinical technology projects, one identifies and collects specific metrics about the
performance of the technology or metrics that capture the level of participation or adoption. Equally important is the need for
process performance metrics. Process metrics are collected at the initial stage of a project or problem identification. Current-state
metrics are then benchmarked against internal indicators. When there are no internal indicators to benchmark against, a suitable
course of action is to benchmark against an external source, such as a similar business practice within a different industry.
Microprocessor Chip that integrates the processor onto one circuit, incorporating the functions of the computer’s central processing
unit. Microprocessors continue to evolve in terms of their processing capacity.
Milestones A predetermined planned occurrence that indicates the completion or achievement of a deliverable.
Mind The brain’s conscious processing; encompasses thought processes, memory, imagination and creativity, emotions, perceptions,
and inner drive or will.
Mobile e-health (mobile health, m-health) Healthrelated uses of mobile technologies including mobile phones (and increasingly,
Internet-enabled, wirelessconnected smartphones), personal digital assistants, tablet computers and subnotebook microcomputers,
remote diagnostic and monitoring devices, and global positioning system (GPS)/geographic information system mapping
equipment.
Model of terminology use A domain content model that is optimized for the management of particular entities within an
informational and/or operational context.
Modem Hardware that allows a user to send and receive information over the phone or cable lines, for example, with a computer. It
enables Internet connectivity via a telephone line or cable connection through network adaptors situated within the computer
apparatus.
Monitor Computer display that allows the user to view text and graphic images.
Moral dilemma Situation for which there is no clear evidence that one of several alternatives is morally right or wrong.
Moral rights Ethical privileges.
Morals Social conventions about right and wrong human conduct that are socially constructed and tacitly agreed upon as good or
right.
MoSCoW Must have, Should have, Could have, and Would have; an approach where a team works with stakeholders to develop a
prioritized requirements list and a development plan.
Motherboard A key foundational computer component. All other components are connected to it in some way (either via local
sockets, attached directly to it, or connected via cables). The essential structures of the motherboard include the major chipset,
super I/O chip, BIOS, read-only memory, bus communications pathways, and a variety of sockets that allow components to plug
into it.
Mouse A small device that one can roll along or scroll to control the movement of the pointer or cursor on a display and click to search
for and/or execute features.
MP3 aggregator A program that can facilitate the process of finding, subscribing to, and downloading podcasts. A commonly known
aggregator is Apple Computer’s iTunes, which is a free program available as a download from apple.com. Using a program such as
iTunes gives the user the ability to search for podcasts based on many criteria, including category, author, or title. iTunes provides
access to audio downloads, which may be either songs or podcasts.
MPEG-1 Audio Layer-3 (MP3) Digital or electronic audio programming format.
Multidimensional database Type of database that combines data from numerous data sources and is optimized for online analytical
processing applications; uses multidimensional structures to organize the data, and each data attribute is considered as a separate
dimension.
Multimedia A computer-based technology that incorporates traditional forms of communication to create a seamless and interactive
learning environment.
Multispatial Relating to the need for educators in the age of technology to account for both physical and virtual spaces and their
relationship to the learning process.
Multiuser dungeon (MUD) A computer program, usually running over the Internet, that allows multiple users to participate in
virtual-reality role-playing games.
Multiuser shared hack, habitat, holodeck, or hallucination (MUSH) A computer program that allows the user to extend a virtual-
reality “world” by adding new rooms, objects, and features.
Nanotechnology Microscopic technology on the order of one billionth of a meter.
National Center for Public Health Informatics (NCPHI) Center created in 2005 by the Centers for Disease Control and Prevention
to provide leadership in the field of public health informatics.
National Guideline Clearinghouse (NGC) A comprehensive database of clinical practice guidelines developed as a result of
research. The NGC website allows users to browse for clinical guidelines, view abstracts and fulltext links, download full-text
clinical guidelines to personal digital assistive devices, obtain technical reports, and compare guidelines.
National Health and Nutrition Examination Survey (NHANES) A survey sponsored by the Centers for Disease Control and
Prevention that combines both questionnaires and physical examinations to collect data on the health and nutritional status of
adults and children in the United States.
National Health Information Infrastructure (NHII) An initiative intended to improve the effectiveness, efficiency, and overall
quality of health and health care in the United States. A comprehensive knowledge-based network of interoperable systems of
clinical, public health, and personal health information that would improve decision making by making health information
available when and where it is needed. The set of technologies, standards, applications, systems, values, and laws that support all
facets of individual health, health care, and public health. The NHII is voluntary and not a centralized database of medical records
or a government regulation.
National Healthcare Quality Report (NHQR) A report that explores the quality of health care in the United States; it has been issued
by the Agency for Healthcare Research and Quality every year since 2003.
National Institute of Standards and Technology (NIST) A nonregulatory federal agency within the U.S. Department of Commerce
that was founded in 1901; its mission is to promote U.S. innovation and industrial competitiveness by advancing measurement
science, standards, and technology in ways that enhance economic security and improve the quality of life. From automated teller
machines and atomic clocks to mammograms and semiconductors, innumerable products and services rely in some way on
technology, measurement, and standards provided by NIST.
National provider identifier (NPI) A standard 10-position unique identifier (code) mandated by HIPAA legislation and designed to
replace previous provider identifiers.
Nationwide Health Information Network (NHIN) An agency of the U.S. Department of Health and Human Services charged with
the development of a safe, secure, interoperable health information infrastructure.
Negligence A departure from the standard of due care—prudent, reasonable care—toward others, including intentionally posing risks
that are unreasonable as well as unintentionally, but carelessly, imposing risks.
Net generation Students used to surfing the Web and interacting online.
Network Connection of computers that can be local and/or organizationally based, joined together into a local area network, on a
wider area scope (such as a city or district) using a metropolitan area network, or from an even greater distance (e.g., a whole
country or continent or the Internet in general) using a wide area network configuration.
Network accessibility The ability of the network to be accessed by the right user to obtain what that person needs when he or she
needs it.
Network availability The state in which network information is accessible when needed.
Network security The specific precautions taken to ensure that the integrity of a network is safe from unauthorized entry and that the
data and information stored on the network are accessible only by authorized users.
Neural network A nonlinear predictive model. Such models learn by training and resembling the structure of biological neural
networks. Neural networks model the neural behavior of the human brain and are a way to bridge the gap between computers and
humans.
Neuroscience The study of the nervous system.
New England Health EDI Network (NEHEN) An example of an implementation model for building regional health information
organizations that are functional, sustainable, and growing while reducing administrative costs.
Next-Generation Internet (NGI) A government project to develop new, faster technologies to enhance research and communication.
Nicomachean An approach to ethical thinking based on the work of Aristotle.
Nonmaleficence Doing no harm.
Nonplayer character (NPC) An individual in a simulation, virtual world, or game that is controlled by the program, not another
person.
Nonsynchronous That which is not in real time or does not occur or exist at the same time, having the same period or time frame.
Occurring anywhere and anytime using Internet and World Wide Web software tools (e.g., course management systems, e-mail,
electronic bulletin boards, webpages) as the principal delivery mechanisms.
Nursing informatics (NI) A specialty that integrates nursing science, computer science, and information science to manage and
communicate data, information, knowledge, and wisdom in nursing practice.
Nursing informatics competencies A set of essential skills related to informatics deemed appropriate for various levels of nursing
practice.
Nursing knowledge A body of facts accumulated over time from experience, education, and research that are used to make nursing
decisions.
Nursing science The ethical application of knowledge acquired through education, research, and practice to provide services and
interventions to patients so as to maintain, enhance, or restore their health; to advocate for health; and to acquire, process, generate,
and disseminate nursing knowledge to advance the nursing profession.
Nursing terminology Body of the terms used in nursing.
Nursing theory Concepts, propositions, and definitions that represent a methodical viewpoint and provide a framework for organizing
and standardizing nursing actions.
Object-oriented MUD (MOO) Similar to a multiuser dungeon—a computer program, usually running over the Internet, that allows
multiple users to participate in virtual-reality role-playing games—MUD, but with more advanced programming features.
Office of Civil Rights (OCR) Part of the U.S. Department of Health and Human Services and responsible for enforcing the Health
Insurance Portability and Accountability Act. It provides significant information and guidance to clinicians who must comply with
the Privacy and Security Rules. It has been tracking complaints and investigating violations since 2003.
Office of the National Coordinator for Health Information Technology (ONC) An office within the U.S. Department of Health
and Human Services that was established through the HITECH Act. The ONC is headed by the national coordinator, who is
responsible for overseeing the development of a nationwide health information technology infrastructure that supports the use and
exchange of information.
Office suite Software that is generally distributed together with a consistent user interface that is designed for knowledge workers and
clerical personnel. These software packages can interact with each other to enhance productivity and ease of use.
Online Something accomplished while connected to or using a computer.
Online analytical processing (OLAP) A fast analysis of shared data stored in a multidimensional database that allows the user to
easily and selectively extract and view data from different points of view. OLAP and data mining complement each other even
though they are quite different.
Online chat Synchronous interaction with another person facilitated by an Internet connection technology.
Ontological approach Theory that considers ontology development (domain analysis) and its mapping to object models (specification
of infrastructure). Based on enumerating all concepts used in a domain and in providing their formal definitions according to
suitable formalisms (usually logic based).
Ontology Study of that which is compositional in nature and a partial representation of the entities within a domain and the
relationships that hold between them. An explicit specification of a conceptualization.
Open Access Initiative A worldwide movement to make a library of knowledge available to anyone with Internet access.
Open source software (OSS) Computer software where the source code is made available for use and/or modification without charge.
The developers share code in the hopes that the software will evolve as others modify and improve upon the base.
Open systems interconnection (OSI) A model of standardization for communications in a network developed to ensure that various
programs would work efficiently with one another.
Operating system (OS) The most important software on any computer. It is the very first program to load on computer start-up and is
fundamental for the operation of all other software as well as the computer’s hardware.
Order entry management A program that allows a clinician to enter medication and other care orders directly into a computer,
including orders for laboratory, microbiology, pathology, radiology, nursing, and medicine; supply orders; ancillary services; and
consults.
Order entry system A system that automates the way that orders are initiated for patients. Clinicians place orders within these
systems instead of using traditional handwritten transcription onto paper. Such systems provide major safeguards by ensuring that
physician orders are legible and complete, thereby providing a level of patient safety that was historically missing with paper-based
orders. They also provide decision support and automated alert functionality that was previously unavailable with paper-based
orders.
Outcome Changes, results, and/or impacts from inputting and processing.
Output Changes that exit a system and that can activate or modify processing.
Palmtop/palm computer Miniature or small computer that fits in the palm of the hand.
Parallel port Interface for connecting an external device that is capable of receiving more than one bit at a time.
Password A code established by the user to identify himself or herself when the user enters the system. Most organizations today
enforce a strong password policy. Strong password policies include using combinations of letters, numbers, and special characters
such as plus (+) signs and ampersands (&). Policies typically include the enforcement of changing passwords every 30 or 60 days.
Patient care information system (PCIS) Patient-centered information systems focused on collecting data and disseminating
information related to direct care. Several of these systems have become mainstream types of systems used in health care. The four
types of systems most commonly found in healthcare organizations include (1) clinical documentation systems, (2) pharmacy
information systems, (3) laboratory information systems, and (4) radiology information systems.
Patient care support system System of components that make up each of the specialty disciplines within health care and their
associated patient care information systems. The four types of systems most commonly found in healthcare organizations include
(1) clinical documentation systems, (2) pharmacy information systems, (3) laboratory information systems, and (4) radiology
information systems.
Patient-centered Focused on the patient/person (rather than on the illness/healthcare professional), with patients becoming active
participants in their own healthcare initiatives. Patients as active participants receive services designed to meet their individual
needs and preferences, under the guidance and counsel of their healthcare professionals. Data, observations, interventions, and
outcomes focused on direct patient care.
Patient informed consent A document that a patient signs to agree to treatment. A document that a home healthcare patient signs to
agree to receive telehealthcare services in addition to conventional home health care.
Patient outcomes Measurable effects resulting from best practice treatment interventions that improve or stabilize the course of health
over time.
Patient support The total array of tools and software that can be used to provide information and assistance to help meet the
healthcare needs of consumers.
Payer organization An organization that contracts with healthcare agencies and service providers to attempt to manage healthcare
costs.
Perception The process of acquiring knowledge about the environment or situation by obtaining, interpreting, selecting, and
organizing sensory information from seeing, hearing, touching, tasting, and smelling. Sensory experience foundational to
formulating knowledge.
Performance improvement Enhancement of performance. A quality indicator.
Performance improvement analyst Person who analyzes performance improvement initiatives. Person who is intimately involved in
the design of the system used by nursing.
Peripheral biometric (medical) devices A variety of telecommunications-ready measurement devices, such as blood pressure cuffs
and blood glucose meters, that typically use the household telephone jack to transmit patient data to a central server location.
Peripheral component interconnection (PCI) Mechanism for attaching peripheral devices to a motherboard via computer bus,
expansion slots, or integrated circuits.
Peripheral devices Devices with a digital readout typically used in home telehealth and whose output is capable of being captured by
computer. Generally this equipment is self-administrated by the patient or family caregiver. Examples of commonly used
peripheral devices include a weight scale, blood pressure monitor, pulse oximeter, thermometer, glucometer, spirometer,
prothrombin/International Normalized Ratio meter, digital camera (to capture images of wounds), and a personal digital assistant–
based or telephonic self-reporting device.
Personal computer (PC) Computer made for individual use or directly used by an end user.
Personal digital assistant (PDA) A handheld device, miniature or small computer, or palmtop that uses a pen for inputting data
instead of a keyboard. Also called a handheld computer. Also known as personal digital assistive.
Personal emergency response systems (PERS) Signaling devices that enable patients to access emergency and other care needs.
Petabyte (PB) A unit of information or computer storage equal to 1 quadrillion bytes, or 1,000 terabytes.
Pharmacy information system Information system that facilitates the ordering, managing, and dispensing of medications for a
facility. It also commonly incorporates allergy and height/weight information for effective medication management; it streamlines
the order entry, dispensing, verification, and authorization process for medication administration while often interfacing with
clinical documentation and order entry systems so that clinicians can order and document the administration of medications and
prescriptions to patients while having the benefits of decision support alerting and interaction checking.
Picture archiving and communication system (PACS) System that is designed to collect, store, and distribute medical images such
as computed tomography (CT) scans, magnetic resonance images, and X-rays; it replaces traditional hard copy films with digital
media that are easy to store, retrieve, and present to clinicians. This system may be a stand-alone system, separate from the main
radiology system, or it can be integrated with a radiology information system and a computer information system. The benefit of
PACSs is their ability to assist in diagnosis and to store vital patient care support data.
Plug and play The ability to add new devices to a computer easily without having to manually install and reconfigure the computer to
accept the device.
Podcast A digital media file or collection of related files that are distributed over the Internet using syndication or subscription feeds
for playback on portable media players such as MP3 players, laptops, and personal computers; the subscription relies on RSS
feeds. Online media delivery. Enhanced podcasts contain slides and pictures; vodcasts contain videos.
Policy Basic principle that guides behavior and performance and is enforced. For example, in a corporation, corporate policy would be
enforced by corporate administration; the U.S. government enforces public policy.
Population health management A term adopted by healthcare management companies to express their goal of achieving optimal
health outcomes at a reasonable cost. The management process involves data collection and trend analyses that are used to predict
clinical outcomes in a group of people.
Port Interface between a computer and other devices or other computers.
Portability Ability to be transported easily. For example, users can easily take handheld computers wherever they go.
Portable Operating System Interface for UNIX (POSIX) A uniform set of standards adopted by the Institute of Electrical and
Electronics Engineers and the International Standards Organization that define an interface between programs and operating
systems. The standardization ensures that software can be easily ported to other POSIX-compliant operating systems.
Portals Tools for organizing information from webpages into simple menus on one’s desktop. Also, multifunctional telehealthcare
platforms for receiving, retrieving, and/or displaying patients’ vital signs and other information transmitted from
telecommunications-ready medical devices.
Portfolio A collection of evidence used to demonstrate knowledge and skill achievement. A nursing portfolio provides the opportunity
for a student to document a variety of sometimes unquantifiable skills, such as creativity, communication, and critical thinking.
Power supply A device that supplies electrical energy or power; the device that provides the electrical energy or power to the
computer. A battery can be a source of energy or power.
Presence The act of being fully there and being fully with patients; exclusively focusing on patients and their unique needs.
Presentation Act of presenting or showing; typically uses presentation software in a slide show format. The most commonly used
presentation software in the United States is Microsoft PowerPoint.
Primary key A field within a record (also known as the key field) that contains a code, name, number, or other bit of information that
acts as a unique identifier for that record. In a healthcare system, for example, a patient is assigned a patient number or ID that is
unique for that patient.
Principlism A foundation for ethical decision making. Principles are expansive enough to be shared by all rational individuals,
regardless of their background and individual beliefs.
Privacy An important issue related to personal information, about the owner or about other individuals, that focuses on sharing this
information with others electronically and the mechanisms that restrict access to this personal information.
Problem-based Typically refers to a type of studentcentered instructional strategy where students collaboratively solve problems and
reflect on their experiences.
Problem solving Cognitive process of critically thinking through a problem or issue to determine a course of action.
Process analysis Breaking down the work process into a sequential series of steps that can be examined and assessed to improve
effectiveness and efficiency; explains how work takes place, gets done, or how it can be done.
Process owners Those persons who directly engage in the workflow to be analyzed and redesigned and have the ultimate
responsibility for the performance of the process. These individuals can speak about the intricacy of the process, including process
variations from the normal. When constructing a team, it is important to include individuals who are able to contribute information
about the exact current-state workflow and offer suggestions for future-state improvements.
Processing Acting on something by taking it through established procedures so as to convert it from one form to another. Examples
include the processing of information into data and the processing of a credit application to get a loan.
Product developer One who designs, creates, and builds a product, such as a computer program, network, and/or system. One who
employs productivity software to create a product.
Productivity software Programs or software that help us compose, create, or develop. An example is the Microsoft Office suite of
productivity tools, which offers word processing, spreadsheet, database, presentation, and Web tools to help us complete both
professional and personal tasks.
Professional development Acquisition of skills required for maintaining a specific career path or general skills offered through
continuing education, including the more general skills area of personal development. It can be seen as training to keep current
with changing technology and practices in a profession or as part of the concept of lifelong learning.
Professional networking Connecting with other professionals in a field with a predetermined and focused purpose as well as an
identified target audience in mind.
Programmable read-only memory (PROM) Form of digital memory where the setting of each bit is locked on a chip by a fuse or
antifuse. PROM is used to store programs permanently, so it is useful in applications where the programming needs to be
permanent. The device cannot be erased, so it must be replaced if changes are deemed necessary in the system.
Project manager Person responsible for the success of a project, who manages the planning and enactment of the project.
Protected health information (PHI) Any and all information about a person’s health that is tied to any type of personal identification.
Prototype Original mockup or first model; original form that is studied, tested, and processed before duplication.
Proxy server Hardware security tool to help protect an organization against security breaches.
Psychology The field that studies the mind and behavior.
PsycINFO A comprehensive database in the field of education and psychology.
Public health The science of protecting the well-being of communities and the population through education, research, intervention,
and prevention.
Public health informatics (PHI) An aspect of informatics focused on the promotion of health and disease prevention in populations
and communities.
Public health interventions Actions taken to promote and secure the well-being of a population or a community.
Publishing The process of production and dissemination of information.
Qualified electronic health record An electronic record containing health-related information on an individual, which consists of the
individual’s demographic and clinical health information, including medical history and a list of health problems, and supports
entry of physician orders. A qualified electronic health record can capture and query information relevant to healthcare quality and
exchange electronic health information with and assimilate such information from other sources to provide support for clinical
decision making.
Qualitative study A type of research design that focuses on the human experience of a phenomenon using words, concepts, language,
and meanings rather than numbers to capture the essence of the subject under study. Subjective study.
Quality A level or grade of excellence; relative merit; a distinct or essential characteristic, attribute, or property.
Quality assurance (QA) The systematic process of assessing and testing to verify that a product or service being developed or used is
meeting its specified requirements; focuses on discovering and correcting defects before they become part of the final product.
Quantitative study Research that looks at the what, where, and when to provide understanding of phenomena based on quantifying
data and using statistical measures; depending on the research, a study may ascertain cause-and-effect relationships. Objective
study.
Query A form of questioning. A request for information; an example would be a database query.
QWERTY Name given to the typical computer keyboard layout, derived from the six letters in the first row below the numeric or
number row.
Radio frequency identification (RFID) chip An identification chip that stores information for retrieval.
Radio frequency identifier (RFI) A reprogrammable chip that communicates with a reader to aid in identifying an object.
Radiology information system (RIS) Information system designed to schedule, report, and store information as it relates to
diagnostic radiology procedures. One common feature found in most radiology systems is a picture archiving and communication
system (PACS). The benefit of RISs and PACSs is their ability to assist in diagnosing complex cases and storing vital patient care
support data.
Random-access memory (RAM) Volatile, temporary storage system that allows a computer’s processor to access program codes and
data while working on a task. RAM is lost once the system is rebooted, shut off, or loses power.
Rapid application development (RAD) A method using prototyping and reiteration to develop products faster and of superior
quality.
Rapid Syndromic Validation Project (RSVP) System where local healthcare professionals report cases such as influenza. Data are
analyzed centrally, and the resulting information is shared with appropriate local authorities in an attempt to identify outbreaks
early and prevent the spread of contagious diseases.
Rationalism An ethical position that contends knowledge is derived from deductive reasoning and not from the senses.
RDF site summary Resource description framework site summary. See really simple syndication.
Read-only memory (ROM) Essential permanent or semipermanent, nonvolatile memory that stores saved data and is critical in the
working of the computer’s operating system and other activities. ROM is primarily stored in the motherboard but may also be
available through the graphics card, other expansion cards, and peripherals.
Real environment data Patient data collected in the home during telehealth monitoring. These data are typically more reflective of the
true patient situation because they are collected in the real environment and not the artificial environment of a healthcare agency.
Real time Human time; occurs live, with users or learners interacting at the same time.
Real-time telehealth Live interactions between two or more clinicians, usually performed with videoconferencing equipment.
Really simple syndication (RSS) A form of web feed used to publish frequently updated content in podcasts, blog entries, or even
news headlines. Subscribers receive update notices whenever new content is added or a site is updated. Also known as RDF site
summary (RSS 1.0 and RSS 0.90) and rich site summary (RSS 0.91).
Reasoning Way of thinking, calculating, interpreting, or introspectively rethinking or critically thinking through an issue; reflective
thought to analyze or think through one’s ideas and alternatives.
Record Row in a relational database representing one patient, for example; also called a tuple. Group of related fields in a database.
To record or capture audio and video using specific devices.
Reflective commentary Narrative comments that focus on why an individual thinks specific evidence is important, the ways in which
the individual values what he or she has learned, or why the individual thinks the evidence is important for his or her profession.
Regional health information exchange See regional health information organization.
Regional health information organization (RHIO) A regional network of healthcare organizations and providers who exchange
information related to the health of the population. The goal is to work together without duplication to provide cost-effective health
care and promote community well-being.
Relational database A database that can store and retrieve data very rapidly. “Relational” refers to how the data are stored in the
database and how they are organized.
Relational database management system (RDBMS) A system that manages data using the relational model. A relational database
could link a patient’s table to a treatment table, for example, by a common field such as the patient ID number field.
Reporting The act of using of documents or information system outputs to convey information to stakeholders.
Reporting and population health management The data collection tools to support public and private reporting requirements,
including data represented in a standardized terminology and machine-readable format.
Reports Documents that contain data or information based on a query or investigation designed to yield customized content in relation
to a situation and a user, group of users, or an organization. Designed to inform, reports may include recommendations or
suggestions based on programming and other embedded parameters.
Repository Central place where data are collected, stored, and maintained. Central location for multiple databases or files that can be
distributed over a network or directly accessible to the user. Location for files and databases so that the data can be reused,
analyzed, explored, or repurposed.
Research utilization (RU) The process of moving new understandings generated in research into practice.
Research validity A conclusion that can be drawn about the conduct of research based on an analysis of the research design and
methods (internal validity) and the applicability of the findings to the general population (external validity).
Resource description framework (RDF) A structure of consistent semantics adopted by the World Wide Web Consortium (W3C) to
promote encoding, exchange, and reuse of metadata.
Results management An approach to evaluating the outcomes of a process to determine whether that process was useful or valuable.
Reusability The extent to which software or other workrelated artifacts can be used in more than one computing program or software
system.
Rights Privileges; include the right to privacy, confidentiality, and so on.
Risk assessment Determination of risk or danger, such as assessing for risk factors related to heart disease.
Robotics The designing, development, and implementation of robots or machines to carry out tasks typically performed by people.
Role playing Situation that allows students to try on reallife scenarios by filling either prescripted or ad-libbed roles (e.g., doctor,
nurse, patient, clinician) without the fear or pressure of putting another’s life at risk while trying to determine the best course of
action or find a solution to a fictitious patient’s health issue.
Root cause analysis Similar to failure modes and events analysis; analysis to discover why a process is faulty or produces an
undesired result.
Row A record in a database; also known as a tuple.
Safety culture An organizational commitment to patient safety and the prevention of medical errors.
Sarbanes-Oxley Act (SOX) Legislation that was put in place to protect shareholders as well as the public from deceptive accounting
practices in organizations.
Scaffolding Adding initial support for a task and then gradually removing that support over time.
Scenario Mock description of a situation or series of events.
Scheduling system A system designed to track resources within a facility while managing the frequency and distribution of those
resources. For example, resourcescheduling systems provide information about operating room utilization, or availability of
intensive care unit beds and regular nursing unit beds.
Scoring The data mining process of applying a model to new data.
Second Life A proprietary virtual reality tool that allows users to create virtual communities.
Secure information Information that is protected from error, unauthorized access, and other threats that can compromise its integrity
and safety.
Security Protection from danger or loss. In informatics, one must protect against unauthorized access, malicious damage, and
incidental and accidental damage and enforce secure behavior and maintain security of computing, data, applications, information,
and networks.
Security breach Any security violation.
Self-control Self-discipline. Strength of will.
Sensor and activity monitoring systems Systems for tracking activities of daily living of seniors and other at-risk individuals in their
places of residence. Additional applications’ use of sensors to detect anomalies or problems such as faucets and stoves left turned
on.
Serial port An interface for connecting an external device that is capable of receiving only one bit at a time, such as a mouse, a
modem, and some printers.
Server A computer or a group of computers that link computers together or provide services to a group of computers.
Shoulder surfing Watching over someone’s back as he or she is working on a computer. This is still a major way that confidentiality
is compromised.
Simulated documentation A replicated documentation system that nursing students can use to learn how to access electronic health
records and document nursing care.
Simulation An imitation of a real-life event or circumstance; in nursing education, the replication of a clinical scenario developed to
provide an opportunity for practice in a mock situation.
Simulation scenario A virtual case situation developed in a simulation laboratory to mimic a clinical practice situation.
Simulator A mechanical or electronic device that provides an environment in which a simulation can occur. Some of these may be
quite large.
Situational awareness The ability to detect, integrate, and understand critical information that leads to an overall understanding of a
problem or situation.
Six Sigma/Lean Business management tactic that seeks to improve the quality of process outputs by identifying and removing the
causes of imperfections (errors) and reducing inconsistency and variability in processes; Lean and Six Sigma are a complementary
combination of activities that focus on doing the right steps and actions (Lean) and doing them right the first time (Six Sigma).
Small Computer System Interface (SCSI) Set of standards for physically connecting and transferring data between computers and
peripheral devices. The SCSI standards define commands, protocols, and electrical and optical interfaces. Standardization among
commercial products helps to ensure that devices will interface with many different systems.
Smart pump Machine used to infuse medication that includes dose-checking technology and safeguards designed to help avert
medication errors.
Smart room Patient room that is equipped with technologies to increase patient safety and improve patient care.
Smartphone A cell phone that has limited personal digital assistant capabilities. Smartphones have limited personal computer
functionality; they have an operating system and facilitate the use of e-mail and other applications.
Social bookmarking Saving bookmarks or Internet URLs to a public website instead of on the user’s private computer. The purpose is
to share and grow the list of websites related to a specific topic. As users add bookmarks, they also typically add keyword tags that
aid in search and organization processes.
Social engineering The manipulation of a relationship based on one’s position in an organization. For example, someone attempting
to access a network may pretend to be an employee from the corporate information technology office, who then simply asks for an
employee’s digital ID and password. Another example of social engineering is a hacker impersonating a federal government agent.
After talking an employee into revealing network information, the hacker basically has an open door to enter the corporate
network.
Social media Communication tools such as Twitter and Facebook that promote real-time information exchange.
Social networking Subscribing to and utilizing Web-based applications for the purpose of sharing personal information with others.
Social sciences Collection of academic/scientific fields or disciplines concerned with the study of the human aspects of our
world/environment.
Software Anything that can be stored electronically. Software is divided into two types: system software (includes the operating
system and other software necessary for the computer to function) and application software (allows users to complete specific
tasks, such as word processors, spreadsheet software, presentation software, database managers, and media players).
Sound card A computer expansion card that facilitates the input and output of audio signals to and from a computer under control of
computer programs. Also known as an audio card.
Spreadsheet Text and numbers located in cells on a grid and the software necessary to process formulas and other computations such
as creating graphs and charts.
Spyware A program that may contain malicious code that may attack or attempt to “take over” a computer. Spyware may also be
nonmalicious in intent and monitor the user’s behavior in an attempt to gain information about the user for targeted advertising.
Stacking The process of synthesizing the predictions from several models.
Staff development The process of providing opportunities for professional growth and skills development. Computer information
systems are frequently used to assist with the ongoing education and development of nursing staff members, as this medium can
embed prompts, information, and related questions in the nursing documentation system with a link to an appropriate clinical
protocol.
Stakeholder An individual or group with the responsibility for completing a project and influencing the overall design, and that is
most impacted by success or failure of the system implementation.
Standard Benchmark. Criterion. Rule. Norm. Principle.
Standard Generalized Markup Language (SGML) Metalanguage; markup language for documents. Extensible Markup Language
(XML) began as a simplified subset of SGML.
Standardized nursing terminology A body of terms used in nursing that is in some ways approved by an appropriate authority or by
general consent.
Standardized plan of care A plan that presents clinicians with treatment protocols to maximize their outcomes and support best
practices.
Standards-developing organization (SDO) An organization that creates guidelines, standards, and rules to help healthcare entities
collect, store, manipulate, dispose of, and exchange secure protected health information.
Static medium Something that cannot be updated; for example, a print-based brochure may be outdated almost as soon as it is printed.
Store-and-forward telehealth transmission An application of telehealth care, in which images and other clinical data are captured
and transmitted to specialist clinicians.
Structured English Query Language (SQL) (Pronounced “sequel.”) A database querying language, rather than a programming
language. SQL is a standard language for accessing and manipulating databases. It simplifies the process of retrieving information
from a database in a functional or usable form while facilitating the reorganization of data within the database.
Suicide prevention community assessment tool Risk assessment method that addresses general community information, prevention
networks, and the demographics of the target population as well as community assets and risk factors.
Summary A condensed version of the original designed to highlight its major points.
Supercomputer The fastest computer; designed to run special applications that require numerous calculations.
Surveillance The act of watching for trends in healthrelated data for early detection of health threats.
Surveillance data system A networked computer system designed to use health-related data trends to predict the probability of an
outbreak of a contagious or infectious disease or to detect morbidity and mortality trends in a geographic area as a precursor to
public health planning or response.
Synchronous Real time or occurring at the same time; having the same period or time frame. Learning anywhere and anytime in real
time using delivery modalities such as traditional face-to-face, Internet, and World Wide Web software tools (e.g., course
management systems, chat, e-mail, electronic bulletin boards, audio–video communication tools).
Synchronous dynamic random-access memory (SDRAM) The most common type of dynamic random-access memory found in
personal computers.
Syndromic surveillance A specialized system of data collection that seeks to detect trends in the incidence and severity of a specific
disease or health-related syndrome and plan the public health response.
Synthesis Combining parts of existing material or ideas into a new entity or concept.
Systems development life cycle (SDLC) Stages involved in the life of a system, typically an information system; a model used in the
project management of a system’s development effort, spanning from feasibility to its demise.
Table A collection of related records in a database.
Tags/tag clouds A collection of keywords (tags) that describe the contents of websites related to a topic of interest, which are then
organized by importance using differing colors and font sizes and styles (cloud). Many tag clouds are navigable; that is, the tags
are hyperlinks to webpages.
Task analysis Analytic technique that focuses on how a task must be accomplished, including detailed descriptions of task-related
activities, task characteristics and complexity, and the environmental conditions required for a person to perform a given task.
Tasks Actions that are value added and necessary. For example, some tasks come about because of workarounds or for other
unsubstantiated reasons. Tasks that are considered non–value added and are not necessary for the purpose of compliance or
regulatory reasons should be eliminated from the future-state process.
Technologist Person skilled in the use of technology.
Technology Method by which people use knowledge and tools. Knowledge used to solve problems, control and adapt to our
environment, and extend human potential. Generally people use technology to refer to machines or devices such as computers and
the infrastructure that supports them. For example, cell phones and planes are technologies that are tangible—one can see and
touch them—but cannot see and touch the vast infrastructures supporting them, such as the wireless communications between the
device (cell phone) and the cell towers, and the electronic guidance used by the device (plane) to navigate the skies.
Telecommunications Broadcasting or transmitting signals over a distance from one person to another person or from one location to
another location for the purpose of communication.
Telehealth Telecommunication technologies used to deliver health-related services or to connect patients and healthcare providers to
maximize patients’ health status. A relatively new term in the medical/nursing vocabulary, referring to a wide range of health
services that are delivered by telecommunications-ready tools such as the telephone, videophone, and computer.
Telehealth care Health services delivered by telecommunications-ready tools, usually supervised by a nurse or other clinician.
Telehealth hardware Equipment that captures objective vital signs data. Some systems use interactive self-reporting devices to
capture subjective information on how a patient feels as well. The values obtained from the patient are then collected and
transmitted by a communication hub. Peripheral devices used in home telehealth can include any item with a digital readout.
Generally this equipment is self-administered by the patient or family caregiver.
Telehealth software Computer programs designed to collect and interpret health data gathered remotely via a telehealth
communications system.
Telemedicine Health services delivered by telecommunications-ready tools, supervised or directed by a physician.
Telemonitoring Remote measurement of patients’ vital signs and other necessary data.
Telenursing Health services delivered by telecommunications-ready tools, supervised or directed by a nurse.
Telepathology Use of telecommunications technology to facilitate the transmission and transfer of pathology data for the purposes of
diagnosis, education, and research. Transmission and exchange of image-rich pathology data between remote locations.
Telephony Telephone monitoring of patients at their residences by off-site telenurses.
Teleradiology Use of telecommunications technology to electronically transmit and exchange radiographic patient images with the
consultative text or radiologist reports from one location to another.
TELOS strategy An approach that provides a clear picture of the feasibility of a project; TELOS stands for “technologic and systems,
economic, legal, operational, and schedule feasibility.”
Terabyte (TB) A measurement term for data storage capacity. One terabyte equals 1,024 gigabytes.
Term At its simplest level, a word or phrase used to describe something concrete (e.g., leg) or abstract (e.g., plan).
Terminology Vocabulary of technical terms used in a particular field, subject, science, or art; concerned with the collection,
description, processing, and presentation of terms belonging to specialized areas of usage of one or more languages; nomenclature.
Thick (fat) client A computer connected to a network designed primarily for data processing and not communications or storage.
Thin client A computer that conveys input and output from the user to the server and back, but does no processing.
Three dimensional (3-D) A geometric model of the physical universe in which we live; the three dimensions are typically length,
width, and depth (or height), although any three directions can be chosen, as long as they do not lie in the same plane.
Three-dimensional (3-D) computer graphics Graphics that use three-dimensional representations of geometric data stored in the
computer for the purposes of performing calculations and rendering images. These images may be stored for later viewing or
displayed in real time.
Throughput The amount of work a computer can do in a given time period; a measure of computer performance that can be used for
system comparison.
Thumb drive Small removable storage device.
TIGER initiative The work of the Technology Informatics Guiding Education Reform team. This team of nursing leaders developed a
vision for utilizing information technology to transform nursing practice.
Touch screen A display used as an input device for interacting with or relating to the display’s materials or content. The user can
touch or press on the designated display area to respond, execute, or request information or output.
Transistor Solid-state semiconductors that resulted in the second generation of computers. Digital devices much smaller and faster
than analog computers.
Translational research Research that is conducted with a vision toward transforming clinical nursing practice (translating the results
into practice).
Transparent Done without conscious thought.
Transparent technology Technology that is not visible or recognizable by the user, therefore allowing the user to focus on the
function or output and not the challenges of the technology itself.
Transput Input and output activities, collectively.
Treatment payment operations (TPO) The treatment of patients, the payment for services, or the operations of the entity. Providers
and other covered entities were not originally required to include in the accounting any disclosures that were made to facilitate the
treatment of patients, the payment for services, or the operations of the entity (the “TPO exception”); this exception ended in
January 2011 for providers that recently implemented electronic health record (EHR) systems. For those providers with EHR
systems that were implemented before passage of the HITECH Act, the TPO exception ended in January 2014. It is easy to
understand why this exception ended: Because all providers must implement comprehensive EHR systems, it will be very easy to
generate an electronic record with an accounting of anyone who accessed a patient’s record.
Trend General movement; a line of development. The process of getting others to emulate one’s actions.
Trending The process of collecting patient data and analyzing those data collected over time via telehealth technology. Trending
analysis provides a more accurate picture of health status than the analysis of episodic data collected during an agency visit.
Triage The process of assessing patients who are ill or injured and determining the need for intervention based on the severity of the
health issue. Some software programs used in telehealth monitoring systems provide this function by comparing actual data with a
preset standard and then alerting clinicians that an intervention is necessary.
Trojan horse Malicious code, capable of replicating within a computer, that is hidden in data or a program that appears to be safe.
Trust-e One of the two most common symbols that power users look for to identify trusted health sites.
Truth Fact. Certainty. Sincere action, character, and fidelity.
Tuple A record in a database; also known as a row.
Tutorial Learning materials available to the learner, who must then be self-directed to study the specific topical area presented.
U-health See ubiquitous health.
U-nursing Based on the concept of ubiquitous computing, the concept that nurses will provide care to anyone, anytime, anywhere
using emerging transparent (ubiquitous) technologies and devices that support nursing care and practice.
Ubiquitous Existing or being everywhere at the same time; widespread.
Ubiquitous health (u-health) The concept of health care that is so present in one’s daily life that it seems invisible or in the
background. Based on the concept of ubiquitous computing, wherein technologies become increasingly invisible as they are
incorporated into everyday use—so much so that they are not even thought of while being used.
Ubiquity State of being everywhere at once (or seeming to be everywhere at once). Presence in many places especially
simultaneously. With changing models of healthcare delivery, information and knowledge should be available anywhere.
Uncertainty Ambiguity. Insecurity. Vagueness.
Universal serial bus (USB) A means of connecting a myriad of plug-in devices, such as portable flash drives, digital cameras, MP3
players, graphics tablets, light pens, and so on, using a plug and play connection without rebooting the computer.
Unstructured data Data that are not contained in a database; data residing in text files, which can represent more than 75% of an
organization’s data; data that are not organized or that lack structure.
Usability The ease with which people can use an interface to achieve a particular goal. Issues of human performance during computer
interactions for specific tasks within a particular context.
User friendly Programs and peripherals that make it easy to interact or use computers. Design of a program to enhance the ease with
which the user can utilize and maximize the productivity from computer programs.
User interface Mechanisms or systems used by users to interact with programs.
Value Relative worth of an object or action, such as aesthetic beauty or ethical value.
Veracity Right to truth.
Video adapter card A board or card that is inserted or plugged into a computer to provide display capabilities.
Videopod Self-contained system with a video transmitter.
Virtual memory The use of hard disk space on a temporary basis when the user is running many programs simultaneously. This
temporary use frees up RAM to allow programs to run simultaneously and seamlessly.
Virtual peers Virtual populations of individuals who are genetically and behaviorally alike.
Virtual reality (VR) Technology that simulates reality in a virtual medium.
Virtual world World that exists in cyberspace where people can establish avatars, purchase land, and interact with others. Emerging
virtual worlds such as Second Life are changing the meaning of social networking.
Virtue A certain ideal toward which we should strive that provides for the full development of our humanity. Attitude or character trait
that enables us to be and to act in ways that develop our highest potential; examples are honesty, courage, compassion, generosity,
fidelity, integrity, fairness, self-control, and prudence. Like habits, virtues become characteristics of a person. The virtuous person
is the ethical person.
Virtue ethics Theory that suggests individuals use power to bring about human benefit. One must consider the needs of others and the
responsibility to meet those needs.
Virus Malicious code that attaches to an existing program and executes its harmful script when opened.
Visiting Nurse Association (VNA) A nonprofit home healthcare agency.
Voice recognition A type of software that allows the user to input data or to navigate the Web using voice commands. Voice
interactivity should help to reduce the disparity associated with those who have limited keyboard or mousing skills.
Waterfall model An early systems development life cycle model that is linear in nature; when one phase ends, you move onto the next
phase and do not go back, unlike in its modern counterparts that stress iterative development.
Wearable computing Devices that a person can don or put on like other articles of clothing or watches, jewelry, and other accessories.
Wearable devices are being used to provide remote monitoring of physiologic parameters in care settings, including patients’ own
homes.
Wearable technology The study or practice of inventing, designing, building, or using miniature body-borne computational and
sensory devices. Wearable computers may be worn under, over, or in clothing, or may actually be the clothes themselves.
Web 2.0 Developing tools for social networking. The implications of the social networking technologies that are major elements of
Web 2.0 will have significant impacts on the amount of information and knowledge that are generated and the ways in which they
are used.
Web-based Originating from the World Wide Web.
Web-enhanced That which uses the World Wide Web to enhance or promote functions or tasks such as effective learning and skill
acquisition.
Web publishing The design and development of webpages that include links to digital files that are uploaded to web servers, thereby
making these files accessible to others via web browsers.
Web quest A search of the World Wide Web for information.
Web servers Multifunctional telehealthcare platforms for receiving, retrieving, and/or displaying patients’ vital signs and other
information transmitted from telecommunications-ready medical devices.
Webcast Media distributed over the Internet as a broadcast, which relies on streaming media technology to facilitate downloading and
participation. Such broadcasts could be distributed in real time, live, or recorded for asynchronous interaction.
Webinar Web-based seminar. Web conferencing that allows a presenter to share his or her computer screen/files and collaborate with
the audience; attendance is controlled by an access code.
Weblog A website that contains the contributions of single or multiple users about a particular topic or issue. Similar in nature to a
threaded discussion board or a personal diary, weblogs (also known as blogs) can provide insight into the perceptions of the
contributors about the topic.
Wetware Direct body–brain interfaces. A reference to the human mind or central nervous system when interfaced with a computer;
derived from computer terminology such as “software” and “hardware.”
Wi-Fi A wireless technology brand owned by Wi-Fi Alliance, which is used to improve the interoperability of wireless networking
devices.
Wiki Server software that allows users to create, edit, and link webpage content from any web browser. Server software that supports
hyperlinks. The simplest online database; used to develop collaborative websites.
Wisdom Knowledge applied in a practical way or translated into actions; the use of knowledge and experience to heighten common
sense and insight so as to exercise sound judgment in practical matters. Sometimes thought of as the highest form of common
sense, resulting from accumulated knowledge or erudition (deep, thorough learning) or enlightenment (education that results in
understanding and the dissemination of knowledge). Wisdom is the ability to apply valuable and viable knowledge, experience,
understanding, and insight while being prudent and sensible. It is focused on our own minds; it is the synthesis of our experience,
insight, understanding, and knowledge. Wisdom is the appropriate use of knowledge to solve human problems. It is knowing when
and how to apply knowledge.
Word processing Creating documents using a word processing software package such as Microsoft Word.
Work process See workflow.
Workaround A way invented by users to bypass the system to accomplish a task; usually indicates a poor fit of the system or
technology to the workflow or user. A devised way to beat a system that does not function appropriately or is not suited to the task
it was developed to assist with. For example, a nurse might remove the armband from the patient and attach it to the bed if the bar-
code reader fails to interpret bar codes when the bracelet curves tightly around a small arm.
Workflow A progression of steps (tasks, events, and interactions) that constitute a work process; involve two or more persons; and
create or add value to the organization’s activities. In a sequential workflow, each step depends on the occurrence of the previous
step; in a parallel workflow, two or more steps can occur concurrently. The term “workflow” is sometimes used interchangeably
with “process” or “process flow,” particularly in the context of implementations. A sequence of connected steps in the work of a
person or team of people—that is, the process or flow of work within an organization; a virtual illustration of the “real” work or
steps (flow) that workers enact to complete their tasks (work). The purpose of examining and redesigning workflow is to
streamline the work process by removing any unnecessary steps that do not add value or might even hinder the flow of work.
Workflow analysis Not an optional part of clinical implementations, but rather a necessity for safe patient care fostered by
technology. The ultimate goal of workflow analysis is not to “pave the cow path,” but rather to create a future-state solution that
maximizes the use of technology and eliminates non–value-added activities. Although many tools and methods can be used to
accomplish workflow redesign (e.g., Six Sigma, Lean), the best method is the one that complements the organization and supports
the work of clinicians.
World Wide Web (WWW) An international network of computers and servers that offers access to stored documents written in
HTML code, and access to graphics, audio, and video files.
Worm A form of malicious code. A self-replicating computer program that uses a network to send multiple copies of itself to other
computers, subsequently tying up bandwidth and incapacitating networks.
Yottabyte (YB) A unit of information or computer storage equal to 1 septillion bytes.
Youth Risk Behavior Surveillance System (YRBSS) An epidemiologic survey conducted by the Centers for Disease Control and
Protection to identify and track the most common health risk behaviors that lead to illnesses and mortality among youth.
Zettabyte (ZB) A unit of information or computer storage equal to 1 sextillion bytes.
Index
The index that appeared in the print version of this title was intentionally removed from the eBook. Please use the search function on your eReading
device to search for terms of interest. For your reference, the terms that appear in the print index are listed below.
A
AAAI. See Association for the Advancement of Artificial Intelligence
AACN. See American Association of Colleges of Nursing
abbreviations used in PHI
Academic Center for Evidence-Based Practice (ACE) website
Academic Education Solution
access
Access (software)
accessibility
policy
right of access
accounting rules
accuracy
ACE Star model
acquisition
acquisition. See also knowledge acquisition
of data and information
of information
active learning
active listening
activity data
acuity systems monitor
administrative information systems
information systems
types of healthcare organization information systems
case management information systems
communication systems
core business systems
department collaboration and exchange of knowledge and information
order entry systems
patient care support systems
administrative processes
admission
Advanced Practice Nursing Network (APNN)
adventure games
advocate/policy developer
advocates
adware software packages
Agency for Healthcare Research and Quality (AHRQ)
National Healthcare Disparities Report of 2012
patient safety initiatives
safety culture, features of
strategies for developing a safety culture
TRIP
TRIP-II
AHRQ. See Agency for Healthcare Research and Quality
AI. See artificial intelligence
AIM. See AOL Instant Messenger
ALA. See American Library Association
alarm fatigue
alerts
algorithms
alleles
Allen, Paul
Alliance for Patient Safety (WHO)
Alliance of Nursing Informatics (ANI)
Allison
alternatives
alphabetic data
alternatives
for ethical decisions, case analysis demonstration
Amazon’s S3
American Association of Colleges of Nursing (AACN)
American Association of Health Plans
American Cancer Society
American Library Association (ALA)
American Medical Association
American Medical Informatics Association (AMIA)
work group
American National Standards Institute (ANSI)
American Nurses Association (ANA)
approved nursing languages
code of ethics
Code of Ethics for Nurses with Interpretive Statements
Committee on Nursing Practice Information Infrastructure (CNPII)
defined areas of practice for INS
definition of nursing
Nursing Informatics: Scope and Standards of Practice
recognized terminologies
requirements for competence in nursing practice
American Nurses Credentialing Center
American Nursing Association of Occupational Health Nurses
American Nursing Informatics Association (ANIA)
American Psychological Association American Public Health Association
American Recovery and Reinvestment Act (ARRA)
minimum data requirements for EHRs
requirement for certified EHR
American Telemedicine Association
AMIA. See American Medical Informatics Association
ANA. See American Nurses Association
analysis
data analysis
meta-analysis
systems development phase
Androwich, Ida
anesthesia machines
ANI. See Alliance of Nursing Informatics
ANIA. See American Nursing Informatics Association
ANSI. See American National Standards Institute
antiprinciplism
antivirus software
AOL Instant Messenger (AIM)
APNN. See Advanced Practice Nursing Network
Apollo 13
Apple Computer
iPods
iTunes
iWork
apps. See applications
applications
application software
Aristotle
arithmetic logic units
ARRA. See American Recovery and Reinvestment Act
Array-Express
art of nursing
artificial intelligence (AI)
assessment
methods of
outcomes assessment
Association for the Advancement of Artificial Intelligence (AAAI)
asynchronous
asynchronous Web-based courses
attachment special interest group
attribute
audio data
audiopod
auditory displays
augmented-reality games
authentication of users
authentication procedures
Autodesk
automated dispensing machines
automated systems
automated weight scales
autonomy
availability
avatar
B
Babbage’s difference engine
bagging
bar code
bar-code medication administration (BCMA)
errors, recommendations to reduce
bar-coded identification cards
basic input/output system (BIOS)
BCMA. See bar-code medication administration
beginning nurse
Behavioral Risk Factor Surveillance System
Bell, Alexander Graham
beneficence
Ben’s Game
beta testing
BI. See bioinformatics
big data
unstructured
binary digits (bit)
binary system
biochip
bioethical standards
bioethics
bioethics. See also ethics
consensus-based approach to
Husted decision-making model
bioinformatics (BI)
definition
future
importance
subdisciplines
biology computational. See computational biology
biomedical informatics
definition
future
importance
wireless biomedical sensors
biomedicine
biometrics
BIOS. See basic input/output system
biosense
bioterrorism
bit
blackboard Blackwell Publishing
blended hybrid
blended learning model
blogging
blogs (weblogs)
health-related
bluetooth
BMJ Publishing
body area networks
boosting
boot firmware
borrowed theory
bracketing
Brailer, David
brain
breach
breathing exercise
Brewer, Steven L., Jr.
Briggs, Joanna
bring your own device (BYOD) policy
brushing
building blocks
bus communications pathways
Bush, George W.
business associates
business study
business understanding, CRISP-DM model
BYOD policy. See bring your own device policy
byte
C
cache memory
CAI. See computer-assisted instruction
call centers
scope of practice
telephony-based
Cancer Game
Cancer Genome Atlas Project
Capital Area Roundtable on Informatics in Nursing (CARING)
carative factors
cardinal virtues
care ethics
care plans
computerized nursing care plans CARE Telematics Project
CARING. See Capital Area Roundtable on Informatics in Nursing
caring
functions
strategies for
theories
caritas processes
CART. See classification and regression trees
case analysis demonstration
alternative
arguments for
ethical dilemma and examine outcomes
examine ethical dilemma
hypothesize ethical arguments
case management information systems
case scenarios
for assessment
for SDLC
case studies
casting to future
home healthcare
home telehealth nursing KDD
nursing research
telehealth
telemonitoring
casual games
casuist approach
casuistry
catalogs, electronic library
CBISs. See computer-based information systems
CD drive. See compact disk drive
CD-R. See compact disk-recordable
CD-ROM. See compact disk read-only memory
CDS. See clinical decision support
CE. See continuing education
Center for Evidence-Based Practices (CEBP)
Center for Public Health Informatics (CPHI)
centering
Centers for Disease Control and Prevention (CDC)
current initiatives
health literacy and e-health initiatives
integrated surveillance system plan
National Electronic Telecommunications System for Surveillance
Office of Minority Health and Health Disparities
patient education
surveillance criteria
Wide-ranging Online Data for Epidemiologic Research
Centers for Medicare and Medicaid Services (CMS)
central nervous system
central processing unit (CPU)
central stations
Centre for Evidence Based Medicine
Cerner Corporation
Cerner’s Academic Education Solution
Certification Commission for Healthcare Information Technology
certifications
examination for
recertification
certified EHR technology
certified pediatric nurse (CPN)
Chalmers, Iain
change
demographic
emerging technologies and
change management
informatics as change agent
suggested conditions for
change theories
chats
CHF. See congestive heart failure
chi square automatic interaction detection (CHAID)
chief information officers (CIOs)
chief technical officers (CTOs)
chief technology officers. See chief technical officers
child nodes
childhood obesity
Children’s Nutrition Research Center
chronic disease, management of
CI. See cognitive informatics
CIOs. See chief information officers
CISs. See clinical information systems
civil monetary penalties (CMP)
class hierarchy
classification
classification and regression trees (CART)
climate symphony
clinical analyst/system specialist
Clinical and Translational Science Award (CTSA)
clinical data
clinical databases
clinical decision support (CDS)
tools
clinical documentation systems
clinical information systems (CISs)
EHR as
clinical outcomes
EBP
staff development tool
impact
informational elements of
clinical practice
clinical practice guidelines
clinical transformation
clinical wisdom
cloud-based EHR
cloud computing
cloud storage
clouds
CMP. See civil monetary penalties
CMS. See Centers for Medicare and Medicaid Services
CMSs. See course management systems
Cochrane, Archie
Cochrane Center
Cochrane Collaboration
Cochrane Qualitative Research Methods Group (CQRMG)
Codd, Edgar F.
Code of Ethics for Nurses with Interpretive Statements
codes of ethics
American Nurses Association
E-Health
International Council of Nurses
codify
codifying tacit knowledge
cognitive activity
cognitive informatics (CI)
and nursing practice
cognitive science
Cognitive Science Journal
Cognitive Science Society
cognitive task analysis
cognitive walkthrough
cognitive work analysis
cohort research
collaboration
department
TIGER vision for
collaborative fieldwork
collaborative learning
promoting
significant elements for success
Colossus
commercial software
Committee on Nursing Practice Information Infrastructure (CNPII) (ANA)
communication(s)
electronic communication and connectivity
mobile
telecommunications
TIGER vision for
tools
communication inefficiencies
communication science
communication software
communication systems
communities of practice (CoPs)
community health
case scenario
feedback to improve responses and promote readiness
community risk assessment (CRA)
compact disk (CD) drive
compact disk read-only memory (CD-ROM)
compact disk-recordable (CD-R)
compatibility
completeness
compliance
Comprehensive Health Enhancement Support System (CHESS)
computational biology
definition
future
subdisciplines
computer
as tools for managing information and generating knowledge
components of
connection ports
desktop
historical development of
input components of
laptop
output components
palm computers
personal computers (PCs)
processing components of
research uses
second-generation
supercomputers
throughput/processing components
computer-aided software engineering (CASE) tools
computer-assisted instruction (CAI)
computer-based information systems (CBISs)
computer-based provider order entry (CPOE) system
computer-based simulations
computer-based technology
computer graphics
computer monitors
computerized decision support system
computer science
and knowledge
concepts and tools from
computer-stored ambulatory record (COSTAR)
computerized information system
computerized nursing care plans
computerized patient care system
computerized physician order entry (CPOE)
computerized provider order entry (CPOE)
computing history
Computing Technology Industry Association
conditional knowledge
conferences
conferencing software
confidentiality
policy
congestive heart failure (CHF), protocol for
Connecting for Health
connection ports
connectionism
consensus-based approach
consequences
Consolidated Health Informatics (CHI)
construction and building games
consultant
consumer-directed health care
consumer education
consumer health informatics
consumer information and education
demand for
future directions
health initiatives for
context of care
continuing education (CE)
gaming in
refresher programs
continuity of care document
continuous focus during interface design
continuous learners
control task analysis
COPD.
copyright
core business systems
Corel WordPerfect Suite
cost-effectiveness
cost–benefit analysis, of health information technology
COSTAR. See computer-stored ambulatory record
courage
course management systems (CMSs)
covered entities
CPN. See certified pediatric nurse
CPOE. See computerized physician order entry
CPU. See central processing unit
CQRMG. See Cochrane Qualitative Research Methods Group
CRA. See community risk assessment
creativity software
CRISP-DM. See Cross-Industry Standard Process for Data Mining
critical thinking
Cross-Industry Standard Process for Data Mining (CRISP-DM)
Crossing the Quality Chasm report
crowdsourcing
CTOs. See chief technical officers
CTSA. See Clinical and Translational Science Award
culture
“just culture” approach
safety. See safety culture
TIGER vision for
Cumulative Index to Nursing and Allied Health Literature (CINAHL)
D
Danish eHealth Portal
data
See also information
acquisition of
alphabetic
audio
definition of
image
information vs.
integrity and quality of
in nursing science
output or dissemination of
qualitative
quantitative
synthesis of
types
video
data analysis
future
qualitative
quantitative
tools for
data-centric view
data collection
electronic tools
informatics tools for
quantitative
timely
tools for reporting
data dictionary
data files
data gatherer
data management
programs
data mart
data mining
benefits of
capabilities
concepts
defining
ethics of
models
CRISP-DM
SEMMA
Six Sigma
phase
exploration
knowledge deployment
pattern discovery
problem identification
research
software
techniques
data preparation, CRISP-DM model
data processing
future
tools for
Data Protection Act
data regulations
data set
“data tsunami” data understanding, CRISP-DM model
data warehouse (DW)
database
clinical
professional
relational
software applications
database construction
database management system
Davies Award (HIMSS)
decision making. See also ethical applications of informatics: ethical decision making
casuist approach
elements that facilitate
Husted bioethical decision-making model
knowledge and wisdom in
organizational
decision support
decision support/outcomes manager
decision-support programs
decision-support systems (DSSs)
expert systems
decision trees
analysis
declarative knowledge
deductive machine learning
deductive reasoning
Defense Agency Research Projects Agency (DARPA)
define, measure, analyze, improve, and control (DMAIC)
degradation
demographic
Denmark
department collaboration
Department of Health and Human Services (HHS)
healthfinder
HITECH Act requirements
OCR
ONC
Department of Veterans Affairs’ MyHealtheVet
deployment, CRISP-DM model
design team, building
desktop computer
desktop publishing
detection system
digital classrooms
digital divide
digital natives
digital pen
digital versatile disk/digital video disk (DVD)
digital video disk (DVD) drive
digital video disk-recordable (DVD-R)
digital video disk-rewritable (DVD-RW)
digital workstations
direct observation
disaster planning and preparation
discharge
discount usability evaluation
discussions
disease management, monitoring outbreaks
disease surveillance
dissemination
of data
tools
distance education
DMAIC
“do no harm”
documentation systems
incorporating into learning environments
search strategy for meta-analysis
documents
domain name
dot pitch factor
DRAM. See dynamic random access memory
drill-down analysis
Drucker, Peter
drugs, high-hazard
Drummond Group
DSDM. See dynamic system development method
DSSs. See decision-support systems
Duke Medical Center
Dutch Data Protection Authority
duty
DVD drive. See digital video disk drive
DVD-R. See digital video disk-recordable
DVD-RW. See digital video disk-rewritable
DW. See data warehouse
DxPlain
dynamic random access memory (DRAM)
dynamic system development method (DSDM)
dynamic webpage shells
E
e-brochure
e-health
definition of
example developments
mobile e-health
programs in health literacy
videos
E-Health Code of Ethics
e-health initiative
e-learning
e-mail
client
communication with physicians
scanning software
e-portfolio
earcons
early focus during interface design
EB. See exabytes
EBP. See evidence-based practice
economics
feasibility
earcons
early focus during interface desig
EB. See exabytes
EBP. See evidence-based practice
economics
feasibility
EDA. See exploratory data analysis
EDI. See electronic data interchange
education
CAI
CE
consumer information and. See consumer information and education
delivery modalities
distance education
face-to-face delivery of
Foundation of Knowledge model and
group education class
HCO approaches to
hybrid/blended delivery of
incorporating EHRs into
Internet tools for
nursing informatics applications
online delivery of
refresher programs
strategies
teaching opportunities
technology tools for
TIGER vision for
via mediated immersion
web-based
web-enhanced instruction
educational games
Educational Resources Information Center (ERIC)
educator
“edutainment”
EEPROM.
eGov initiative (CHI)
eHealth Initiative
examples of
EHR. See electronic health records
electronic communication
electronic data collection
electronic data interchange (EDI)
electronic documentation systems
electronic health record (EHR)
as a part of
advantages of
BCMA
certified EHR technology
benefits
CPOE
certification criteria
change management plan
clinical information system
cloud-based
components of
deadline for
expandability of
flexibility of
future directions
implementation of
incorporating into learning environment
major considerations for
meaningful use of
minimum data requirements
monetary incentives to use
nationwide system
ownership of
prelive activities
qualified EHR
resistance to implementation
right of access to
simulated
training
use in practice
variants of
vendor selection process
electronic library catalogs
electronic mailing lists
electronic medication administration system (eMAR)
Electronic Numerical Integrator and Computer (ENIAC)
electronic portfolio (e-portfolio)
electronic posters
electronic protected health information (EPHI)
electronic records
electronic security
authentication of users
off-site use of portable devices
securing network information
security tools
threats to security
electronic transaction and code set standards
Elsevier’s Simulation Learning System
scanning software
embedded testing
eMedonline
emerging technologies
changes related to
future directions
impacting nursing and healthcare
implications of
NI2006 issues
empiricism
employee/patient data access
employees
authentication of
training
empowerment, consumer
end users
adoption of EHR
enhancing, strategies for
enterprise integration
entity
entity–relationship diagram
entrepreneur
Entrez PubMed
enumerative approach
EPHI. See electronic protected health information
Epidemic Information Exchange
epidemiology
processing knowledge and information for
Wide-ranging Online Data for Epidemiologic Research
epistemology
equanimity, practice of
erasable programmable read-only memory (EEPROM)
ergonomics
ERIC. See Educational Resources Information Center
Essentials of Baccalaureate Education for Professional
Nursing Practice (AACN)
ethernet
ethical applications of informatics
applying ethics to informatics
bioethics
case analysis demonstration. See case analysis demonstration
ethical decision making
ethical dilemmas and morals
ethical issues
new frontiers in
and social media
ethics. See ethics
healthcare ethics, theoretical approaches to
ethical decision making
case analysis demonstration
case study
casuist approach
ethical model for
hypothesizing arguments
ethical design
ethical dilemma
case analysis demonstration
and examine outcomes in reflecting on ethical decision
ethical issues
ESLI
in telehealth
new frontiers in
and social media
in telehealth
ethical model for ethical decision making
ethical, social, and legal implications
ethical, social, and legal issues (ESLI)
ethicists
ethics
applying to informatics
bioethics
care
Code of Ethics for Nurses with Interpretive Statements
of data mining
E-Health Code of Ethics
Nicomachean principles
theoretical approaches to healthcare
virtue
eudaemonistic principles
European Bioinformatics Institute
European Data Protection Directive
evaluation, CRISP-DM model
events
evidence
categories
definition
in context of care
evidence-based practice (EBP)
Bakken’s recommendations
barriers to and facilitators of
bridging gap between research and
CIS
future
guidelines
history of
Iowa model of
model approaches to
online resources for
Stetler model
exabytes (EB)
Excel
exchange of knowledge and information
executes
experienced nurse
expert systems
exploration of data mining
exploratory data analysis (EDA)
extensibility
Extensible Markup Language (XML)
extranets
F
Facebook
face-to-face delivery
failure modes and effects analysis (FMEA)
fair use
of information
Family Web project
FDA. See Food and Drug Administration
feasibility
Federal Health Information Exchange (FHIE)
Federal HIT Strategic Plan
feedback
to improve responses and promote readiness
in nursing science
public health informatics
FHIE. See Federal Health Information Exchange
fidelity
field study
fields (columns)
financial systems
firewalls
sources of information on
FireWire (IEEE 1394)
firmware
fit between individuals, tasks, and technology (FITT) model
five-element breathing sequence
5 Million Lives Campaign
flash drives
flash memory
flexibility
EHR
focus group
FMEA. See failure modes and effects analysis
Fold.IT (game)
folksonomies
Food and Drug Administration (FDA)
bar code medication
formal assessments
formal evaluation
formal nursing informatics educational programs
formal security position
formal usability test
Foundation of Knowledge model
home telehealth and
information science and
nursing education and
nursing informatics and
nursing research and
functional model iteration
future directions
futurologists
G
games
augmented-reality games
casual games
choosing between games, simulations, and virtual worlds
educational
emerging
future of
in continuing education
massive multiplayer online role-playing
role-playing
types of
websites
Games for Health
Gates, Bill
GB. See gigabytes
Gene Expression Omnibus
Generation Y
genome
Cancer Genome Atlas Project
HGP
genomic health care
genomics
“genoming”
gesture recognition
GHz. See gigahertz
gigabytes (GB)
gigahertz (GHz)
Gilligan, Carol
GLBA. See Gramm-Leach-Bliley Act
good action
Google Glass
GoogleHealth
“Googlezon”
Gramm-Leach-Bliley Act (GLBA)
graphical exploratory data analysis
graphical user interface (GUI)
graphics cards
gray gap
grid technologies
group education class
GUI. See graphical user interface
gulfs of execution and evaluation
H
hackers
half-life of knowledge
haplotypes
hard disk
hard drive
Hardiker, Nicholas
hardware
educational
historical development of
security tools
harm
HCI. See human–computer interactions
HCOs. See healthcare organizations
Health Alert Network
health care
networks
health course, concepts of
health data
health disaster planning and preparation
health disparities
health education
future directions for
Internet use for
programs
Health Evaluation through Logical Processing (HELP) system
health information
in EHRs
Internet as source of
protected
Health Information and Management
Systems Society
health information exchange (HIE)
health information kiosks
Health Information Privacy Code 1994
Health Information Technology for Economic and Clinical Health (HITECH) Act
enforcement of
HIPAA changes
HIPAA enhancements
implications for nursing practice
overview
purposes of
health information technology (HIT)
definition of
HIT Policy Committee
HIT Standards Committee
meaningful use of
national infrastructure
technologies impacting nursing
health initiatives
Health Insurance Portability and Accountability Act (HIPAA)
HITECH Act changes
HITECH Act enhancements
implications for nursing practice
organizations assisting
overview
Privacy Rule
requirements for compliance with
Security Rule
Health Level 7 (HL7)
health literacy
goal of
HCO approaches to
promotion in school-aged children
health management information system
Health on the Net (HON) code
health-related blogs
health-related Internet sites
health risk assessment
health service provider data
healthcare
consumer-directed
costs
containing and reducing
for people with chronic diseases
delivery
emerging technologies and
errors
industry
shortage of workers in
technologies
telehealth care
healthcare-associated infections
healthcare ethics, theoretical approaches to
healthcare informatics
historical development of
nursing contributions to
healthcare information
Healthcare Information and Management Systems Society (HIMSS)
Davies Award
Informatics Nurse Impact Survey
“Stage 7” award
healthcare information exchange and interoperability (HIEI)
healthcare organization information systems, types of
case management information systems
communication systems
core business systems
department collaboration and exchange of knowledge and information
order entry systems
patient care support systems
healthcare organizations (HCOs)
approaches to education
healthcare provider
definition of
future of
healthfinder site
HealthVault (Microsoft)
Healthy People 2010
Healthy People 2020
HELP system. See Health Evaluation through Logical Processing system
heuristic evaluation
HHS. See Department of Health and Human Services
HIE. See health information exchange
HIEI. See healthcare information exchange and interoperability
high-fidelity equipment
high-hazard drugs
HIMSS. See Healthcare Information and Management Systems Society
HIMSS 2012 Security Survey
HIPAA. See Health Insurance Portability and Accountability Act
Hippocrates
HIS. See hospital information system
HIT Policy Committee
HIT Standards Committee
HIT. See health information technology
home health care
case study
practice and protocols
telenursing applications in
home telehealth care
call centers
case studies
characteristics
definition of
Foundation of Knowledge model
patient’s role
practice and protocols
software communications
data access and information sharing
trending
triage
telenursing practice
tools of central stations
medication management devices
peripheral biometric (medical) devices
personal emergency response systems
portals
sensor and activity-monitoring systems
special needs telecommunications readydevices
telephones
videocameras and videophones
web servers
value of
home telemonitoring
home telenursing
HONcode. See Health on the Net code
Hospital Consumer Assessment of Health Providers and System survey
hospital information system (HIS)
historical development of
hotspots
human care essentials
human–computer interactions (HCI)
human factors engineering
Human Genome Project (HGP)
ESLI questions raised by
human–technology interaction
human–technology interface
evaluation of
future directions
improving
problem
humanistic nursing theory
Husted bioethical decision-making model
hybrid/blended learning model
hypertext
hypothesize ethical arguments
HµREL Corporation
I
IA technology. See intelligent agent technology
IBM 704 computer
ICTs. See information and communication technologies
ICUs. See intensive care units
ID card. See identification card
IDE. See integrated drive electronics
identification (ID) card
IHI. See Institute for Healthcare Improvement
IHIE. See Indiana Health Information Exchange
Iliad
image data
IMC. See Internet-based medical chart
in-depth interviews
Indiana Health Information Exchange (IHIE)
inductive machine learning
Industrial Age
infection-control nurse
infections, healthcare-associated
informaticists
informatics. See also nursing informatics (NI)
applying ethics to
bioinformatics
biomedical
as change agent
for community/population health
competencies for nurses
definition and goal of
ethical applications of. See ethical applications of informatics
and healthcare technologies
KSAs
role in research
technologies for patient safety
TIGER vision for design
tools for communication and dissemination
informatics innovator
Informatics Nurse Impact Survey
informatics nurse (IN)
informatics nurse specialist (INS)
ANA-defined areas of practice
future of
standards of practice and performance
informatics solutions
information. See also consumer information and education; data;
knowledge acquisition of
consumer demand for
definition of
exchange of
latent
management
manifest
in nursing science
quality
vs. data
Information Age
information and communication technologies (ICTs)
information communication
information literacy
Information Literacy Competency Standards for Higher Education
information mediator
information needs
“information prescriptions”
information processing
Information & Resources for Nurses Worldwide
information science
concepts and tools from
and Foundation of Knowledge model
information storage
information success model
information systems (ISs)
computer-based
definition of
international examples
information technology (IT)
Bakken’s recommendations
elements of
historical development of
for patient safety
TIGER vision for
information user
informed consent releases
infrastructure
input devices
INS. See informatics nurse specialist
instant message (IM)
Institute for Health and Social Care Research (Salford, UK)
Institute for Healthcare Improvement (IHI)
steps for smart pump safety
strategies for developing safety culture
Institute of Medicine (IOM)
Committee on the Quality of Health Care
Crossing the Quality Chasm
definition of EHRs
definition of role of public health
To Err Is Human
health literacy
integrated decision support tools
integrated drive electronics (IDE)
controller
integrating communication systems
integration
Integration and Interoperability Steering Committee
integrity
of data
Intel Corporation
intelligence
artificial intelligence
intelligent agent (IA) technology
intensive care units (ICUs)
interactions
interactive behavior change technology
interactive technologies
interactive tutorials
interdisciplinary knowledge team
interdisciplinary team, workflow redesign team
interfaces
International Classification of Nursing Practice (ICNP)
International Congress on Nursing Informatics
NI2006
NI2009
International Council of Nurses
code of ethics
International HapMap Project
International Medical Informatics Association
International Nurse Practitioner/Advanced Practice Nursing Network (INP/APNN)
International Organization for Standardization (ISO) standard
International Radio Medical Center
International Standards Organization (ISO)
ISO 20302
OSI
Internet
browser
evidence-based online resources
knowledge acquisition through
net generation
and patient education information
security, sources of information on
use for health education
Voice over Internet protocol
Internet2
Internet-based medical chart (IMC)
Internet Healthcare Coalition
Internist-1
interoperability
intranets
intravenous drug administration
intrusion detection devices
intrusion detection systems
intuition
IOM. See Institute of Medicine
Iowa model of EBP
steps for translating research into practice
iPods
ISO 20302
ISO standard. See International Organization for Standardization standard
ISs. See information systems
iteration phase
iTunes
J
Joanna Briggs Institute
Johns Hopkins Bloomberg School of Public Health
Johns Hopkins Evidence Based Practice Center
Joint Commission
Journal of Healthcare Information Management, The
Journal of the American Medical Informatics Association (JAMIA), The
journals
jump drives
“just culture” approach
justice
K
k-nearest neighbor
KDD. See knowledge discovery and data mining
KDP model. See knowledge domain process model
Kenney, Julie A.
key field
keyboard
King’s College Hospital, London
kiosks
KMSs. See knowledge management systems
know–do gap
knowledge
knowledge. See also information; wisdom
assessment methods
conditional
creating and deriving new
in decision-making process
declarative
definition of
exchange of
five rights of
half-life of
nature of
needs
nursing knowledge in evolution
procedural
processing
sharing of
sources of
tacit
use in practice
and wisdom
knowledge acquisition
disaster planning and preparation
in home telehealth
through Internet and library holdings
tools for
Knowledge Age
knowledge brokers
knowledge builder
knowledge-centric view
knowledge consumers
knowledge deployment
knowledge directories
knowledge discovery
knowledge discovery and data mining (KDD)
benefits of
case study
and research
knowledge discovery in data
knowledge discovery in databases
knowledge dissemination
in home telehealth
for patient education
knowledge domain process (KDP) model
knowledge exchange
knowledge generation
emerging technologies and
in home telehealth
meta-analysis and
in nursing science
questions posed for
through nursing research
knowledge management systems (KMSs)
knowledge needs
knowledge processing
in home telehealth
in nursing science
knowledge repositories
knowledge, skills, and attitudes (KSAs)
knowledge user
knowledge work
knowledge workers
characteristics and nursing characteristics, comparison of
concept
and health care
nurses as
types of
KOffice
Kotter, John
KSAs. See knowledge, skills, and attitudes
Kurzweil, Ray
L
lab-on-a-chip device
laboratory information systems
Landmarks of Tomorrow (Drucker)
language, standardized
laptop
latent information
Latter Day Saints/LDS Hospital (Salt Lake City)
leadership
for change
TIGER vision for
Lean
learner-interface interactions
learning
active
blended learning model
collaborative
e-learning
face-to-face
incorporating EHRs into
neomillennial learning style
online
problem-based
web-enhanced learning
learning management system
learning-to-be skills
learning-to-do skills
learning-to-know skills
legal feasibility
legal issues. See also ethical, social, and legal issues (ESLI)
legislation
and nursing practice
privacy and data regulations
length of stay (LOS)
liberty
library catalogs, electronic
library resources
library science
LINC
LISTSERV
literacy. See health literacy
logic
Logitech digital pen system
longevity
Longuet-Higgins, Christopher
LOS. See length of stay
loving kindness, practice of
loyalty
M
m-health (mobile e-health)
machine learning
main memory
mainframes
malicious code
malicious insiders
malware
managed care information systems
management, TIGER vision for
manifest information
mapping
Mark I
mask
Massachusetts General Hospital utility multiprogramming system (MUMPS)
Massachusetts Health Data Consortium (MHDC)
massive multiplayer online role-playing games
master patient index (MPI)
maturity of environment
Maya
MB. See megabyte
McCarthy, John
meaningful use (MU)
of electronic health records
objective for providers and hospitals
requirements for
Medicaid
Medical Center Hospital, Burlington
medical devices
lab-on-a-chip devices
shared safety data on
medical errors
failure modes and effects analysis of
national database of
“no pay for errors” initiative
root-cause analysis of
medical home models
medical identity theft
medical informatics
Medical Product Safety Network
medical simulations. See also simulations Medicare
medication administration
after discharge
BCMA
future technologies for
rights of
smart pump technologies for
technologies to support
medication management devices
meditative exercise
Meditel
MEDLINE
MedlinePlus
megabyte (MB)
megahertz (MHz)
memory
cache memory
computer
flash memory
main memory
organizational systems
RAM
ROM
virtual memory
mentors
Merriam-Webster Online Dictionary
meta-analysis
documentation search strategy
and knowledge generation
steps
meta-learning
metastructures
Metcalf, Bob
methicillin-resistant Staphylococcus aureus (MRSA) nosocomial infections
metrics
MHDC. See Massachusetts Health Data Consortium
microprocessor
Microsoft Corporation
HealthVault
Microsoft Surface
Microsoft Windows
milestones
millennial generation
mind
study of
mini-games. See casual games
Minnesota Health Information Exchange
mobile communications
mobile devices
mobile e-health
mobile networking applications
model building
modeling
modeling software
modem
Modular Object-Oriented Dynamic Learning Environment (Moodle)
monitor
monitoring disease outbreaks
monitoring patients. See patient monitoring; telemonitoring
Moodle. See Modular Object-Oriented Dynamic Learning Environment
moral dilemmas
moral rights
morals
Morgan Stanley Children’s Hospital of New York
MoSCoW approach
motherboard
mouse
MP3 aggregator
MP3 players
MPEG-1 Audio Layer-3 (MP3)
MPI. See master patient index
MSN Messenger
MU. See meaningful use
multidimensional databases
multidisciplinary team
multimedia
multimedia classrooms
multiple illnesses, home telemonitoring of
multiuser dungeons
multiuser shared hallucinations
MUMPS. See Massachusetts General Hospital utility multiprogramming system
MYCIN
N
nanotechnology
National Aeronautics and Space Administration (NASA)
National Center for Biotechnology Information (NCBI)
National Center for Patient Safety
National Center for Public Health Informatics (NCPHI)
national coordinator
national database of medical errors
National Electronic Disease Surveillance System (NEDSS)
National Electronic Telecommunications System for Surveillance
National Guideline Clearinghouse (NGC)
National Health and Nutrition Examination Survey
National Health Information Infrastructure (NHII)
National Health Information Network (NHIN)
national health information network
National Health Service (UK)
National Healthcare Disparities Report of 2012
national healthcare quality report (NHQR)
National Human Genome Research Institute (NHGRI)
national informatics conference
National Institute of Standards and Technology (NIST)
certification criteria for EHRs
National Institutes of Health (NIH)
Biomedical Information Science and Technology Initiative Consortium
CTSA
Human Genome Project
National League for Nursing (NLN)
National Library of Medicine (NLM)
Medlars
MEDLINE
UMLS
National Network Libraries of Medicine
National Patient Safety Foundation
National Patient Safety Goals
National Quality Forum
nearest neighbor analysis
negligence
NEHEN. See New England Health EDI Network
neomillennial learning style
Net Generation
network accessibility
network availability
network security
network security policies
networking
social
networks
neural networks
neuroscience
New England Health EDI Network (NEHEN)
New Mexico
Next-Generation Internet (NGI)
NGC. See National Guideline Clearinghouse
NGI. See Next-Generation Internet
NHII. See National Health Information Infrastructure
NHIN. See National Health Information Network
NHQR. See national healthcare quality report
NI. See nursing informatics
NIC. See Nursing Intervention Classification
Nicomachean principles
Nightingale, Florence
Nightingale Tracker System
Ninewells Hospital, Scotland
NIST. See National Institute of Standards and Technology
NLM. See National Library of Medicine
NLN. See National League for Nursing
“no pay for errors” initiative
NOC. See Nursing Outcomes Classification
nomenclature. See also terminology
Systemized Nomenclature of Medicine (SNOMED)
non-value added activities/step
nonknowledge work
nonmaleficence
nonplayer character
nonsynchronous
North American Nursing Diagnosis Association (NANDA)
North Shore–Long Island Jewish Health System
nurse informaticists
certification
impact on patient safety
organizations and journals
role of
roles
specialty education and certification
nurse-managed telehealth services
nurse–patient interaction framework
nurses
as creating and deriving new knowledge
informatics nurse (IN)
informatics nurse specialist (INS)
information needs
as knowledge acquirers
as knowledge developers or generators
as knowledge engineers
as knowledge managers
knowledge needs
as knowledge professionals
as knowledge users
as knowledge workers
network of care, key role of
registered nurses (RNs)
role of home telehealth nurses
shortage of
social networks by, uses
tele–intensive care
nursing
ANA-approved languages
ANA’s definition of
cognitive informatics and
health information technologies impacting
key concepts of
knowledge use in practice
legislation and practice
phenomena of
scope of practice in call centers
vs. knowledge worker characteristics
nursing care
nursing care plans, computerized
nursing contributions to healthcare informatics
nursing data
nursing data standards
nursing diagnoses, NANDA
nursing education
continuing education
delivery modalities
Foundation of Knowledge model
future directions
groups promoting informatics content in
incorporating EHRs into
nursing informatics and
nursing informatics competencies in
refresher programs
sample content for baccalaureate education
technology-related outcomes for baccalaureate graduates
technology tools for
Web-based
Web-enhanced
nursing informatics competencies
in nursing education
by skill level
Nursing Informatics Impact survey
nursing informatics (NI)
ANA definition of
building blocks of
concepts and tools from information
science and computer
science
conceptual framework for
definition of
driving trends
and Foundation of Knowledge model
future of
goal of
ideal practice
legislative aspects of
metastructures of
and organizational decision making
organizations and journals
overview of
practice, rewards of
role in research
sciences underpinning
scope and standards
specialty education and certification
specialty practice
structured language for
Web-based education
Nursing Informatics
nursing informatics systems
nursing information systems
Nursing Intervention Classification (NIC)
nursing interventions
nursing knowledge
conceptual framework for study of
definition
in evolution
generating
Nursing Management Minimum Data Set (NMMDS)
Nursing Minimum Data Set (NMDS)
Nursing Outcomes Classification (NOC),101
nursing portfolios
nursing practice, standardized terminologies to support
nursing research
case study
data collection and storage in
Foundation of Knowledge model
knowledge generation through
nursing science
definition of
informatics and
nursing terminology
nursing theory
NVivo
O
Obafemi Awolowo University Teaching Hospital
Obama, Barack
objectivity
object-oriented modeling
object-oriented multiuser dungeons
object-oriented programming languages
object-oriented systems development (OOSD) model
Occupational Safety and Health Administration (OSHA)
OCR. See Office of Civil Rights
Office of Civil Rights (OCR)
Office of Minority Health and Health Disparities
Office of the National Coordinator for Health Information Technology (ONC)
office suites
most popular software applications
off-site use of portable devices
older legacy systems
omics
ONC. See Office of the National Coordinator for Health Information Technology
online analytic processing (OLAP)
online browser-based games
online chats
online communities of practice (OCoPs)
online databases, professional
online discussion boards
online discussions
Online Journal of Nursing Informatics
online learning. See also Internet
ontological approach
ontology
OPADE
open access
Open Access Initiative
Open Office
open source software (OSS)
educational
Open Systems Interconnection (OSI)
operating system (OS)
basic processes
operational feasibility
operations
optimization
order entry management
order entry systems
organizational memory systems
organizations
OS. See operating system
OSHA. See Occupational Safety and Health Administration
OSI. See Open Systems Interconnection
OSS. See open source software
outbreaks
H1N1 flu outbreak
monitoring disease
outcomes
EHR, CIS
examining
outcomes assessment
output
output devices
P
Pacific Northwest National Laboratory (PNNL)
PACS. See picture archiving and communication system
Palm
palm computers
paper-based workflow
paper technology
papers and projects
parallel ports
parental authority, privileged
Pareto principle
password
patient area network
patient care information system
patient care support systems
patient care system, computerized
Patient Care System (PCS)
patient centered
patient-centered care
patient confidentiality. See also confidentiality
patient education. See also consumer information and education
clinician’s view on
considerations for
consumer information and education. See consumer information and education
HCOs approaches to
Internet as a source
teaching opportunities
websites for
patient health records
patient informatics
patient informed consent
patient monitoring
activity-monitoring systems
home telemonitoring
remote monitoring (telemonitoring)
sample clinical pathway
sample protocol
telephone monitoring (telephony)
patient populations, telehealth
assisted living facilities/subacute care centers
chronic diseases
concerned patients and families
emergency response situations
employers and wellness programs
hospitalized patients
isolated patients
at-risk populations
patient privacy
patient rights
patient safety
additional technologies for
behaviors leading to compromises
case scenario
informatics technologies for
nurse informaticists and
research initiatives
websites for
Patient Safety and Quality Improvement Act
Patient Safety Culture Survey
Patient Safety Institute
patient safety organizations
patient support
patient tracking mechanisms
patients’ health outcomes
pattern discovery, data mining
pattern identification
Pawlenty, Tim
payment
PDA. See personal digital assistants
Pediatric Nursing and Certification Board
peers, virtual
Penn State School of Nursing
Penn State University
Penn State University Libraries
perception
performance-based assessment
peripheral biometric (medical) devices
peripheral component interconnection (PCI) bus
peripheral component interconnection (PCI) card
personal computer
Personal Data Act
Personal Data File Act (Finland)
personal digital assistants (PDAs)
applications for
how to use
personal emergency response systems
personal health information management systems (PHIMS)
personal health records (PHR)
Personal Information Protection and Electronic Documents Act
Pew Internet and American Life Project survey report
pharmacy information systems
PHI. See protected health information
PHIMS. See personal health information management systems
PHR. See personal health records
picture archiving and communication system (PACS)
Pinnacle Health
plain old telephone system lines (POTS)
Plato
PLATO
plug and play
PNNL. See Pacific Northwest National Laboratory
Pocket PC
podcasts
policy
TIGER vision for
POMR. See problem-oriented medical record
population health
virtual populations
population health management
port
portability
portable devices, off-site use of
portable disk devices
portable operating system interface for UNIX (POSIX)
portable usability laboratory
portals
educational
portfolios
poster presentations
postproject phase
postsecondary education, e-portfolios in
potential, surveys of
POTS. See plain old telephone system lines
power supply
PowerChart application
PowerPoint presentations
preceptors
predictive data mining
presence
description of
strategies for
types of
presentation
primary key
principlism
printer
privacy
of health information
HIPAA rule for
legislative protections
patient
protection of
Privacy and Data Protection (Argentina)
privacy and data regulations
privacy rule
private health information (PHI)
privileged parental authority
problem-based learning
problem identification, of data mining
problem-oriented medical information systems (PROMIS)
problem-oriented medical record (POMR)
problem solving
procedural knowledge
process analysis
process map
process metrics
process owners
processing
data processing
information
information processing
knowledge
product developer
productivity software
professional associations
professional development
e-portfolios for
professional networking
professional online databases
programmable read-only memory (PROM)
project life cycle phase
project manager
projects
PROM. See programmable read-only memory
PROMIS. See problem-oriented medical information systems
ProModel
protected health information (PHI)
proteomics
prototypes
psychology
PsycInfo
public databases
public health
core functions
feedback to improve responses and promote readiness
functions
future
improving
role
Public Health Data Standards Consortium
public health–enabled EHR
public health informatics (PHI)
abbreviations used in
community health risk assessment
competencies needed
disaster planning and preparation
epidemiology and monitoring disease outbreaks
feedback use in
organizations important to
principles that define and guide
scope of practice
Public Health Information Network
public health information systems
Public Health Institute
public health interventions
publishing
PubMed Central (PMC)
PulseNet USA
Q
QMR
qualified electronic health record
qualitative data analysis
qualitative methods
qualitative studies
quality
of data
Quality and Safety Education for Nurses
Quality and Safety Education in Nursing initiative
quality, research, and public health (QRPH)
quantitative data analysis
quantitative studies
query
QWERTY keyboard
R
RAD. See rapid application development
radio frequency badges
radiofrequency identifier (RFID)
tags
radio frequency identity chip
radiology information system (RIS)
random access memory (RAM)
randomized controlled trial (RCT)
rapid application development (RAD)
rapid prototyping
rapid requirements-gathering phase
Rapid Syndromic Validation Project (RSVP)
rationalism
Rational Rose
readiness
read-only memory (ROM)
real-life scenarios
really simple syndication (RSS)
real-time chats
real-time demonstrations
real-time/interactive telehealth
real-time telehealth
reasoning
recertification
records
COSTAR
patient health records
PHR
POMR
public health–enabled EHR
Web-based patient health records
reflective commentary
reflective practice
refresher programs
RefWorks
regional health information exchanges
registered nurses (RNs). See also nurses
regulatory issues
relational database
relational database management system (RDMS)
relevance
reliability
remote access telehealth
remote monitoring
remote patient monitoring
Remote Presence Robot (RP-7)
removable storage device
reporting
reports
repository
reproducibility
research. See also nursing research; translational research
bridging gap between practice and
elements essential for success
Foundation of Knowledge model
informatics role in
KDD and
quantitative studies
telehealth
translational
Wide-ranging Online Data for Epidemiologic Research initiative
researcher
research utilization
definition
Stetler model
research validity
resource data
resource description framework (RDF) site summary
resource-scheduling system
results management
return demonstration method
return on investment (ROI)
reusability
RFID. See radiofrequency identifier
rights
of knowledge
moral
OCR
patient rights
rigorous experimental
RIS. See radiology information system
risk assessment
community health risk assessment
community risk assessment
steps ascribed to
threat and risk assessment
RNs. See registered nurses
Robert Wood Johnson Foundation
robotics
Roget, Peter M.
ROI. See return on investment
role playing
role-playing games
ROM. See read-only memory
root-cause analysis
root node
RSS. See really simple syndication
RSVP. See Rapid Syndromic Validation Project
rule of thumb
S
Safe Harbor
Safety Attitudes Questionnaire
safety culture
key features of
strategies for developing
sample, explore, modify, model, assess (SEMMA)
Sarbanes-Oxley (SOX) Act
scaffolding
scenarios
real-life
symbolic
schedule feasibility
scheduling systems
school-aged children
older, Web quest for
promoting health literacy in
school of nursing (University of Colorado)
scoring
Scrum and Extreme Programming
SCSI. See small computer system interface
SDLC. See systems development life cycle
SDOs. See standards-developing organizations
SDRAM. See synchronous dynamic random-access memory
Seagondollar, Troy
Seaman’s Church Institute of New York
search engines
searching
“seasoned nurses.” See experienced nurse
Second Life
secure information
securing network information
security
of health information
HIPAA rule for
legislative protections
of operating system
protection of
threats to
violations of
security breaches
security budget
security technologies, use of
security tools
SEEDS. See Simulated E-hEalth Delivery System
self-assessment tools
self-control
semantic Web
sensor and activity-monitoring systems
SEQUEL. See Structured English Query Language
sequential workflow
serial ports
serious game
Serious Games Initiative
server
severe acute respiratory syndrome (SARS)
Shannon, Claude E.
shared hallucinations, multiuser
sharing information
shoulder surfing
Sigma Theta Tau
silicon chips
Simon, Herbert
SIMpill Medication Adherence System
simplicity
simulated documentation
Simulated E-hEalth Delivery System (SEEDS)
Simulation Learning System (Elsevier)
simulations
for assessment
challenges and opportunities
choosing between games, simulations, and virtual worlds
educational
future of
goal of
realistic nature of
simulation scenario
simulator
range of devices
single-nucleotide polymorphisms (SNPs)
situational awareness
Six Sigma
small computer system interface (SCSI)
smart classrooms
smart inhalers
smartphones
smart pump
recommended steps to ensure safety with
smart rooms
Snow, John
social bookmarking
social communication software
social engineering
social media
social networking
use of
virtual social networks
social–organizational analysis
social sciences
Socrates
software
categories of
commercial
criteria for
historical development of
Microsoft Office suite
modeling software
open source
OS
packages
productivity
sound card
Southeast Michigan e-Prescribing Initiative
speakers
special needs telecommunications-ready devices
spreadsheets
spyware
SQL. See Structured Query Language
Squires Quest
stacking
staff development tool, CIS
“Stage 7” award (HIMSS)
stakeholders
standardized language
standardized nursing terminology
standardized plan of care
standards
bioethical
for ethical development of health-related Internet sites
HIT
standards-developing organizations (SDOs)
State Boards of Nursing
static medium
Statistical Package for Social Sciences (SPSS)
stethoscope
Stetler model of evidence-based practice
storage
cloud storage
devices, future directions
informatics tools for
store-and-forward telehealth
strategies analysis
strategy games
StratWorld
Structured English Query Language (SEQUEL)
structured language
Structured Query Language (SQL)
Suicide Prevention Community Assessment Tool (SPRC)
summaries
supercomputers
surveillance data systems
surveillance systems
National Electronic Telecommunications System for Surveillance
syndromic
surveys of potential
swim-lane technique
symbolic scenarios
synchronous dynamic random-access memory (SDRAM)
syndromic surveillance systems
synthesis
syringe pumps
systematic reviews
Systemized Nomenclature of Medicine Clinical Terms (SNOMED CT)
systems development life cycle (SDLC)
analysis phase
CASE
case scenario
design phase
DSDM
implement phase
interoperability
maintenance phase
OOSD
open source software and free/open source software
rapid prototyping/rapid application development
test phase
waterfall model
T
tables
tacit knowledge
tags and tag clouds
task analysis
task based simulations
tasks
task technology fit model
TB. See terabyte
teaching opportunities
teams
multidisciplinary team
workflow redesign team
Technicon, El Camino
Technicon Medical Information System (TMIS)
techniques, data mining
technologist
technology
art of caring in
for baccalaureate nursing graduates
computer
current technologies
educational tools
emerging technologies
future directions
game and simulation technology
health information technology (HIT)
informatics, for patient safety
interactive behavior change
for medication administration
nanotechnology
in research
robotics
telehealth
transparent
voice recognition
wearable devices
wish list
workflow and
technology acceptance model
Technology Informatics Guiding Education Reform (TIGER)
key purpose
pillars of
team
telecommunications
National Electronic Telecommunications System for Surveillance
telehealth
advanced forms of
clinical uses of
driving forces for
chronic diseases and conditions
demographics
economics
educated consumers
nursing and healthcare worker shortages
Foundation of Knowledge Model and home telehealth
future directions
history of
home telehealth
case study
practice and protocols
legal, ethical, and regulatory issues
nursing aspects of
patient populations
assisted living facilities/subacute care centers
chronic diseases
concerned patients and families
emergency response situations
employers and wellness programs
hospitalized patients
isolated patients
at-risk populations
patient’s role
research
software for
technologies. See telehealth technology
telenursing. See telenursing
tools of. See home telehealth care, tools of
telehealth care
telehealth interfaces
telehealth technology
clinical and nonclinical transmission formats and applications of
real-time/interactive telehealth
remote monitoring
store-and-forward telehealth
telephony
nonclinical uses of
telemedicine
worldwide markets
telemonitoring
applications of. See home telehealth care
case study
examples
home telemonitoring
sample clinical pathway
sample protocol
telenursing
home telenursing
TELENURSING project
telepathology
telephone monitoring (telephony)
telephones
telephony
teleradiology
TELOS strategy
Tennes Volunteer eHealth Initiative
terabyte (TB)
term
terminology
ANA-recognized terminologies
Systemized Nomenclature of Medicine (SNOMED)
terrorism
testing
embedded
systems development phase
Theory of Human Caring
therapeutic communication
threat and risk assessment
three-dimensional (3D) computer graphics
3DiMD
throughput
thumb drives
timeliness
To Err Is Human
tools of special needs telecommunications readydevices
touch screen
TPO exception
transfer systems
transistor
Translating Research into Practice Initiative (TRIP)
Translating Research into Practice Initiative (TRIP-II)
translational research
Bakken’s recommendations
future
T1
T2
transparency
transparent technology
transparent wisdom
treatment
trending
trends
triage, home telehealth software for
Trip Database
Trojan horses
Trust-e
truth
tuples
Turley, James
tutorials
Twitter
Tyler, Denise
U
ubiquitous
ubiquitous health
ubiquitous nursing (u-nursing)
ubiquity
u-health
uncertainty
Unified Medical Language System (UMLS)
unintended consequences of CPOE implementation
United States
organizations assisting HIPAA
privacy and data regulations
UNIVAC. See Universal Automatic Computer
Universal Automatic Computer (UNIVAC)
universal serial bus (USB)
University of Colorado at Boulder
University of Iowa College of Nursing
University of Texas Health Science Center at San
Antonio
unstructured big data
u-nursing (ubiquitous nursing)
U.S. Department of Health and Human Services
U.S. Department of Veterans Affairs’ MyHealtheVet
U.S. National Library of Medicine
usability
usability evaluation
discount methods
formal tests
usability laboratory
user-centered design
user friendliness
user-friendly aspects
user interface
GUI
user surveys
Utah Health Information Network
utilitarian approach
utility
V
validity, research
value-added activities/steps
values
variation
veracity
verifiability
Vermont Health Information Technology Plan
very-large-scale integration (VLSI) chips
video adapter cards
videocameras
video data
videophones
video podcasts
videopods
virtual communities of practice
virtual memory
virtual peers
virtual populations
virtual reality (VR)
educational
high-fidelity virtual reality trainers
virtual social networks
virtual worlds
case scenario
choosing between games, simulations, and virtual worlds
definition
future of
virtue
virtue ethics
viruses
Visible Analyst
vital signs
VLSI chips. See very-large-scale integration chips
voice-activated communicators
Voice over Internet Protocol
voice recognition
Von Neumann, John
VR. See virtual reality
W
Wake Area Health Education Center
waste
waterfall model
waterfall phases
Watson, James
Watson, Jean
WBANs. See wireless body area networks
wearable computing
wearable devices
wearable technology
Web 2.0
Web 3.0
Web 4.0
Web-based courses
Web-based education
Web-based nursing informatics education
Web-based patient health records
Webcasts
WebCT
Web-enhanced courses
webinars
Web presence
Web publishing
Web quest
Web servers
websites
for ANA-approved nursing languages
dynamic webpage shells
health-related
for patient education
for patient information
standards for ethical development of
user surveys for design
Wellness Alliance
Wellness Program Coordinator
wetware
WHO. See World Health Organization
Wide-ranging Online Data for Epidemiologic Research
Wi-Fi
wikis
Wired for Health Care Quality Act (2005)
wireless area networks
wireless biomedical sensors
wireless body area networks (WBANs)
wireless speech recognition
wisdom
as cardinal virtue
clinical
in decision-making process
definition of
knowledge and
transparent
working
word processing
workarounds
work domain analysis
worker competencies analysis
workflow
definition of
paper-based
and technology
transitioning to future state
variation in
workflow analysis
case study
future directions
and informatics practice
optimization
purpose
value added vs. non–value added activities/step
workflow redesign
team building for
work process
workshops
workspace security discipline
World Health Organization (WHO)
Alliance for Patient Safety
CARE Telematics Project
World Views on Evidence-Based Nursing
World Wide Web
worms
written documents
X
XML. See Extensible Markup Language
Y
Yahoo! Messenger (Y!M)
yottabyte (YB)
Youth Risk Behavior Surveillance System (YRBSS)
Z
Z1
zettabyte (ZB)
Zoho
Cover
Halftitle Page
The Pedagogy
Title Page
Copyright
Special Acknowledgments
Contents
Preface
Acknowledgments
Authors’ Note
Contributors
Section I: Building Blocks of Nursing Informatics
1 Nursing Science and the Foundation of Knowledge
2 Introduction to Information, Information Science, and Information Systems
3 Computer Science and the Foundation of Knowledge Model
4 Introduction to Cognitive Science and Cognitive Informatics
5 Ethical Applications of Informatics
Section II: Perspectives on Nursing Informatics
6 Overview of Nursing Informatics
7 Informatics Roles and the Knowledge Work of Nursing
8 Information and Knowledge Needs of Nurses in the 21st Century
9 Legislative Aspects of Nursing Informatics: HITECH and HIPAA
Section III: Nursing Informatics Administrative Applications: Precare and Care Support
10 Systems Development Life Cycle: Nursing Informatics and Organizational Decision Making
11 Administrative Information Systems
12 The Human-Technology Interface
13 Electronic Security
14 Nursing Informatics: Improving Workflow and Meaningful Use
Section IV: Nursing Informatics Practice Applications: Care Delivery
15 The Electronic Health Record and Clinical Informatics
16 Informatics Tools to Promote Patient Safety and Clinical Outcomes
17 Supporting Consumer Information and Education Needs
18 Using Informatics to Promote Community/Population
19 Telenursing and Remote Access Telehealth
Section V: Education Applications of Nursing Informatics
20 Nursing Informatics and Nursing Education
21 Simulation in Nursing Informatics Education
22 Games, Simulations, and Virtual Worlds for Educators
Section VI: Nursing Informatics: Research Applications
23 Research: Data Collection, Processing, and Analysis
24 Data Mining as a Research Tool
25 Translational Research: Generating Evidence for Practice
26 Bioinformatics, Biomedical Informatics, and Computational Biology
Section VII: Imagining the Future of Nursing Informatics
27 The Art of Caring in Technology-Laden Environments
28 Emerging Technologies and the Generation of Knowledge
29 Nursing Informatics and the Foundation of Knowledge
Abbreviations
Glossary
Index
What Will You Get?
We provide professional writing services to help you score straight A’s by submitting custom written assignments that mirror your guidelines.
Premium Quality
Get result-oriented writing and never worry about grades anymore. We follow the highest quality standards to make sure that you get perfect assignments.
Experienced Writers
Our writers have experience in dealing with papers of every educational level. You can surely rely on the expertise of our qualified professionals.
On-Time Delivery
Your deadline is our threshold for success and we take it very seriously. We make sure you receive your papers before your predefined time.
24/7 Customer Support
Someone from our customer support team is always here to respond to your questions. So, hit us up if you have got any ambiguity or concern.
Complete Confidentiality
Sit back and relax while we help you out with writing your papers. We have an ultimate policy for keeping your personal and order-related details a secret.
Authentic Sources
We assure you that your document will be thoroughly checked for plagiarism and grammatical errors as we use highly authentic and licit sources.
Moneyback Guarantee
Still reluctant about placing an order? Our 100% Moneyback Guarantee backs you up on rare occasions where you aren’t satisfied with the writing.
Order Tracking
You don’t have to wait for an update for hours; you can track the progress of your order any time you want. We share the status after each step.
From brainstorming your paper's outline to perfecting its grammar, we perform every step carefully to make your paper worthy of A grade.
Preferred Writer
Hire your preferred writer anytime. Simply specify if you want your preferred expert to write your paper and we’ll make that happen.
Grammar Check Report
Get an elaborate and authentic grammar check report with your work to have the grammar goodness sealed in your document.
One Page Summary
You can purchase this feature if you want our writers to sum up your paper in the form of a concise and well-articulated summary.
Plagiarism Report
You don’t have to worry about plagiarism anymore. Get a plagiarism report to certify the uniqueness of your work.
Free Features $66FREE
Most Qualified Writer
$10FREE
Plagiarism Scan Report
$10FREE
Unlimited Revisions
$08FREE
Paper Formatting
$05FREE
Cover Page
$05FREE
Referencing & Bibliography
$10FREE
Dedicated User Area
$08FREE
24/7 Order Tracking
$05FREE
Periodic Email Alerts
$05FREE
Our Services
Join us for the best experience while seeking writing assistance in your college life. A good grade is all you need to boost up your academic excellence and we are all about it.
On-time Delivery
24/7 Order Tracking
Access to Authentic Sources
Academic Writing
We create perfect papers according to the guidelines.
Professional Editing
We seamlessly edit out errors from your papers.
Thorough Proofreading
We thoroughly read your final draft to identify errors.
Thorough Proofreading
We thoroughly read your final draft to identify errors.
Delegate Your Challenging Writing Tasks to Experienced Professionals
Work with ultimate peace of mind because we ensure that your academic work is our responsibility and your grades are a top concern for us!
We Analyze Your Problem and Offer Customized Writing
We understand your guidelines first before delivering any writing service. You can discuss your writing needs and we will have them evaluated by our dedicated team.
Clear elicitation of your requirements.
Customized writing as per your needs.
We Mirror Your Guidelines to Deliver Quality Services
We write your papers in a standardized way. We complete your work in such a way that it turns out to be a perfect description of your guidelines.
Proactive analysis of your writing.
Active communication to understand requirements.
We Handle Your Writing Tasks to Ensure Excellent Grades
We promise you excellent grades and academic excellence that you always longed for. Our writers stay in touch with you via email.
Thorough research and analysis for every order.
Deliverance of reliable writing service to improve your grades.
