EHRs Benefits and Drawbacks

Requirements:

As discussed in the lesson and assigned reading for this week, EHRs provide both benefits and drawbacks. Create a “Pros” versus “Cons” table and include at least 3 items for each list. Next to each item, provide a brief rationale as to why you selected to include it on the respective list.

Refer to the Stage 3 objectives for Meaningful Use located in this week’s lesson under the heading Meaningful Use and the HITECH Act. Select two objectives to research further. In your own words, provide a brief discussion as to how the objective may impact your role as an APN in clinical practice.

Adhere to the following guidelines regarding quality for the threaded discussions in Canvas:

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EHRs Benefits and Drawbacks

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Application of Course Knowledge: Demonstrate the ability to analyze, synthesize, and/or apply principles and concepts learned in the course lesson and outside readings.
Scholarliness and Scholarly Sources: Demonstrates achievement of scholarly inquiry for professional and academic decisions using valid, relevant, and reliable outside scholarly source to contribute to the discussion thread.
Writing Mechanics: Grammar, spelling, syntax, and punctuation are accurate. In-text and reference citations should be formatted using correct APA guidelines.
Direct Quotes: Good writing calls for the limited use of direct quotes. Direct quotes in discussions are to be limited to one short quotation (not to exceed 15 words). The quote must add substantively to the discussion. Points will be deducted under the grammar, syntax, APA category.

For each threaded discussion per week, the student will select no less than TWO scholarly sources to support the initial discussion post.

Scholarly Sources: Only scholarly sources are acceptable for citation and reference in this course. These include peer-reviewed publications, government reports, or sources written by a professional or scholar in the field. The textbooks and lessons are NOT considered to be outside scholarly sources. For the threaded discussions and reflection posts, reputable internet sources such as websites by government agencies (URL ends in .gov) and respected organizations (often ends in .org) can be counted as scholarly sources. The best outside scholarly source to use is a peer-reviewed nursing journal. You are encouraged to use the Chamberlain library and search one of the available databases for a peer-reviewed journal article. The following sources should not be used: Wikipedia, Wikis, or blogs. These websites are not considered scholarly as anyone can add to these. Please be aware that .com websites can vary in scholarship and quality. For example, the American Heart Association is a .com site with scholarship and quality. It is the responsibility of the student to determine the scholarship and quality of any .com site. Ask your instructor before using any site if you are unsure. Points will be deducted from the rubric if the site does not demonstrate scholarship or quality. Current outside scholarly sources must be published with the last 5 years. Instructor permission must be obtained BEFORE the assignment is due if using a source that is older than 5 years.

readings: McGonigle, D. & Mastrian, K. (2018). Nursing informatics and the foundation of knowledge (4th ed.). Jones and Bartlett.

Chapter 11 The Human-Technology Interface
Chapter 14 The Electronic Health Record and Clinical Informatics (pages 266-269)

McBride, S., & Tietze, M. (2018). Nursing Informatics for the Advanced Practice Nurse (2nd ed.). Springer Publishing.

Chapter 1 Introduction to Health Information Technology in a Policy and Regulatory Environment
Chapter 8 Systems Development Life Cycle for Achieving Meaningful Use
Chapter 11 Electronic Health Records and Health Information Exchanges Providing Value and Results for Patients, Providers, and Healthcare Systems

Articles

Gold, M., & McLaughlin, C. (2016).

Assessing HITECH implementation and lessons: 5 years later. (Links to an external site.)

Milbank Quarterly, 94(3), 654-687.

Hydari, M. Z., Telang, R., & Marella, W. M. (2015).

Electronic health records and patient safety (Links to an external site.)

. Communications of the ACM, 58(11), 30-32.

Payne, T. H. (2016).

The electronic health record as a catalyst for quality improvement in patient care (Links to an external site.)

. Heart, 102(22), 1782. doi: http://dx.doi.org.proxy.chamberlain.edu:8080/10.1136/heartjnl-2015-308724

Resnick, C. M., Meara, J. G., Peltzman, M., & Gilley, M. (2016).

Meaningful use: A program in transition. (Links to an external site.)

Bulletin of the American College of Surgeons, 101(3), 10-16.

Waldren, S. E., & Solis, E. (2016).

The Evolution of meaningful use: Today, stage 3, and beyond (Links to an external site.)

. Family Practice Management, 23(1), 17-22.

The electronic health record as a catalyst for quality
improvement in patient care
Thomas H Payne

Department of Medicine,
University of Washington,
Seattle, Washington, USA

Correspondence to
Dr Thomas H Payne, Medicine
IT Services, Box 359968, 325
Ninth Avenue, Seattle, WA
98105, USA; tpayne@u.
washington.edu

Received 4 April 2016
Revised 6 July 2016
Accepted 7 July 2016
Published Online First
8 August 2016

To cite: Payne TH. Heart
2016;102:1782–1787.

ABSTRACT
Electronic health records (EHRs) are now broadly used,
following decades of development and incentive
programmes for their use. EHRs have been shown
through use of reminders, electronic order sets and other
means to improve reliability of performance of many
basic tasks in acute, preventive and chronic care.
They assist with collecting, summarising and displaying
the large volumes of information in patient records and
support the implementation of guidelines and care
pathways. Broad use of EHRs has brought into focus
weaknesses of the current generation of EHRs: their user
interface, implementation difficulties, time required to
use them and others. Addressing these weaknesses
and adopting new technologies, including use of voice,
natural language processing and data analytic
techniques, is necessary for EHRs to achieve their full
potential: to gather information from routine care, to
learn from it and to be an integral component of efforts
to continuously improve and to transform care.

INTRODUCTION
Electronic health records (EHRs) have been
regarded as an integral component of healthcare
transformation1 and since large programmes in the
UK2 and the US American Recovery and
Reinvestment Act of 2009 financial incentives3

have become an important part of daily practice
for physicians in many countries. The rapid transi-
tion from paper to EHRs has resulted in substan-
tial change in practice, with mixed reception
among physicians.4

What evidence drove the vision that EHRs are
the key to healthcare transformation? Should this
vision be changed and if so in what ways? In this
paper we provide an overview for the rationale of
moving to EHRs and the ways they can be lever-
aged to improve the quality of care we deliver.
EHRs, sometimes referred to as electronic

medical records, are computing systems that replace
and expand functions previously provided by paper
medical records: to document care, review patient
data from the laboratory, imaging, clinical studies,
patient experience and other sources and to enter
and communicate orders. Beyond this, EHRs
permit communication within the patient care team
including the patient in ways paper could not and
permit us to study and manage care of populations,
to bill for care, potentially to learn from pooled
EHR data and other functions (table 1).
The term ‘system’ indicates that EHRs are

usually not single applications but rather multiple
applications and databases connected into a larger
and more complex whole. They often using web
portals and devices at the point of care,

connections to patient-monitoring devices and
sometimes remotely stored database management
systems. The earliest EHRs were referred to as
computer-based medical record systems6 and were
mostly the product of academic and research devel-
opment groups in the hospitals and clinics where
they were developed. Today most, with very few
exceptions,7 8 are commercial systems licenced
from vendors in a market stratified by their focus
on inpatient or outpatient care and dominated by a
small number of vendors.

PROBLEMS THAT EHRS CAN HELP SOLVE
The quality of medical care is multifaceted and
includes as its foundation the reliable performance
of many basic tasks. The detail involved in these
tasks is ‘work humans neither relish nor reliably
perform’,9 in part because of limitation of our
memory and attention, which computing systems
can help address.10 Among the earliest demonstra-
tions of the ability of EHRs to improve reliable per-
formance arose over 40 years ago from efforts to
manage positive strep throat cultures.11 In this
early study, reminders were sent to providers of
patients who did not have documented treatment
of positive cultures within 10 days. These remin-
ders reduced rates of untreated positive cultures
dramatically, but more importantly this effect
seemed not due entirely to education: when the
reminders were removed, rates of untreated cul-
tures returned to their previous level (figure 1).
This is because when facing the demands of busy
and sometimes chaotic clinic practice, computerised
reminders helped providers remember to follow
through with care they intended to provide;
without reminders, 1 in 10 patients were untreated
after 10 days.
This beneficial effect of EHRs has been observed

repeatedly in various domains such as instituting
antimicrobial prophylaxis in immunocompromised
patients,12 general preventative care13 and in care of
patients with chronic illness.14 The weight of evi-
dence suggests that such reminders work and
augment human tendency to forget details (figure 2).
EHRs also help store, manage and deliver infor-

mation in volumes that exceed human abilities and
permit multiple clinicians to simultaneously access
the same patient record from different locations. As
volumes of health information have risen geomet-
rically driven by imaging and genomic information
and vast collections of text and other data, the
storage, information summarisation and communi-
cation strengths of EHRs have led most to believe
that reverting to a paper record is now impractical.
Limitations in human cognition and the amount of
information we can simultaneously consider when

1782 Payne TH. Heart 2016;102:1782–1787. doi:10.1136/heartjnl-2015-308724

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http://crossmark.crossref.org/dialog/?doi=10.1136/heartjnl-2015-308724&domain=pdf&date_stamp=2016-08-08

http://www.bcs.com

http://heart.bmj.com

making decisions may present an upper limit beyond which
some external support will be needed (figure 3).15

The patient safety movement was energised by the Institute of
Medicine’s publication To Err is Human,16 in which EHR cap-
abilities were highlighted and recommended, such as the use of
computerised practitioner order entry (CPOE) with immediate
checks to avoid errors. In an early and seminal study, CPOE was
associated with 55% reduction in serious adverse medication
errors.17 Other studies have shown benefit in dosage adjustment
for patients in renal failure and in other domains.18 Embedding
patient care guidelines in order sets—collections of electronic
orders that simplify and speed ordering—has been demonstrated
to improve adherence with guidelines because it is much simpler
to do so and fits within ordering workflow,19 including highly
complex chemotherapy protocols. Guidelines embedded in
order sets can be easily updated and disseminated. Some medi-
cation errors occur at the time of medication administration at
the bedside; bar code medication administration has the poten-
tial to reduce these errors.20

As national healthcare systems turn to risk-sharing and quality
reimbursement models to manage the health of a population of
patients, patient care information management needs rise by
several orders of magnitude. Paper medical records are ill-suited
to this task: maintaining and continuous updating records of
millions of patients is only possible when health information is
in electronic form.

Nowhere are volumes of patient information higher and the
need for information management greater than in critically ill
patients. Volumes of patient history and observations, non-
invasive and invasive monitoring information, imaging, labora-
tory testing and other data are enormous in the most critically
ill patients. Health information technology, including EHRs but
extending beyond to include imaging systems, picture archiving
and communication systems, bedside devices and other forms, is

invaluable for decision-making.21–24 EHRs are interfaced to
these systems, providing a more unified view of the patient.

We now have early steps towards leveraging EHRs to better
measure and improve quality,25 though formidable challenges
remain.26 27 However, quality of patient care is more than
avoiding errors and attending to details. It includes making
the correct diagnosis.28 Aiding clinician diagnostic judgement
has been for many years viewed as a difficult task and in
some cases beyond capabilities of current computing systems
because of the broad range of facts to be considered.29 Early
experiments in leveraging that information for reasoning and
application of artificial intelligence did not reach broad
use,30 but there is renewed work in diagnostic decision
support using systems closely linked to data on patient symp-
toms, physical findings and test results gathered in the
EHRs.31 All this is dependent on capturing detailed elements
of history, examination and other findings in machine-
processable form.

Table 2 summarises key articles and reviews of EHR function-
ality for improving care and the evidence regarding its
effectiveness.

NEW EHR TECHNOLOGIES
Several trends will contribute to our ability to leverage rising
computing power and information volumes to address health-
care quality improvement.

Analytics
With the medical records in electronic form, there is a potential
to leverage enormous growth in computer processing power to
analyse patient information and to act on the results. Simple
reminders based on the above-mentioned algorithms are an
early form of potential predictive analytics extend these capabil-
ities to finding associations and correlations between data within
one patient’s record or across millions of records in a fashion
that has proved valuable for other large collections of data.46

Today the main barrier is capturing information from clinicians
without disrupting their workflow or requiring excessive time
and in capturing patient information dispersed across many
EHRs and other computing systems and devices. The data
within EHRs, particularly the large proportion in narrative text
and stored images, are used for human review but not for its
full potential. This will likely change because of continued
growth in other technologies.

Voice technologies
Voice recognition software is increasingly accurate and available
both in handheld devices and EHRs. It permits use of voice as
an alternative to keyboard and mouse for documenting care in
notes and reports, which appeals especially to the large percent-
age of physicians who are not expert typists.47 Using voice to
enter a note most often results in unstructured or narrative text,

Figure 1 Graph showing the effect
of reminders on the percentage of
patients with recorded treatment for
positive group A β-haemolytic strep
throat cultures.11

Table 1 Typical functionality of EHRs in use today5*

Results review (lab, path, imaging, notes) Quality metrics, dashboards

Documentation (direct entry, structured
unstructured, dictation, mixed)

Electronic communication
With team
With patients

Order management Patient monitoring review
Patient summary displays Patient support
Medication administration record Population health
Bar code medication administration External reference resources
Patient lists, schedule, rounding/handoff tools Administration and billing

*This list is an extension of the list from Institute of Medicine Committee on Data
Standards for Patient Safety, Key Capabilities of an Electronic Health Record System,
Letter Report, Washington DC: The National Academies Press, 2003.
EHR, electronic health record.

Payne TH. Heart 2016;102:1782–1787. doi:10.1136/heartjnl-2015-308724 1783

Review

rather than structured or coded information entered using a
mouse to select from dropdown lists or radio buttons. Narrative
text familiar to humans must be converted to a machine-

processable encoded form in a way that preserves meaning in
order to leverage computing technologies.

Natural language processing
Natural language processing is a subfield of artificial intelligence
and computational linguistics used for studying the problems
of automated generation and understanding of natural human
languages. It is used to capture meaning within text generated
by spoken voice or other narrative text and then in conjunction
with other methods to represent that meaning so that it can
be processed and interpreted by computing systems.48

Representing information and knowledge is in itself an complex
problem: simple listing of data as in a spreadsheet can be
enhanced by creating links between data elements, synonyms
and attributes of the data and of the linkages between them.49

Doing so can preserve the information contained within a
patient history, discharge summary or narrative procedure
report. The meaning of phrases indicating concept negation or
qualification, such as ‘denies chest pain’ and ‘probable aortic
stenosis’, is preserved.

Figure 2 Median absolute improvements in adherence to processes of care between intervention and control groups in each study are shown.
Each study is represented by the median and IQR for its reported outcomes; studies with single data points reported only one eligible outcome.

Figure 3 Schematic representation depicting the increase in number
of facts per clinical decision with new sources of biological data.15 SNP,
single nucleotide polymorphism.

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EHR architecture
Change to EHR architecture is also underway. Despite myriad
shortcomings of commercial EHRs, their millions of lines of
computer code embody the results of decades of work by vendor
teams and customer innovations and feedback. Can core EHR
systems be leveraged by other developers, who are not connected
with the EHR vendor except through use of shared open stan-
dards, or by the public at large? One promising way to do this is
by developing standards such as the Fast Health Interoperability
Resource, a draft standard from the Health Level 7 standards
organisation,50 which permits other developers to create applica-
tions that build on, extend and improve EHRs.51

Linking EHRs with other databases
Health data within EHRs may be linked with national mortality
databases, medical registries, drug prescription files and environ-
mental exposure databases to provide insights not possible
separately.52

Patient involvement
Much of EHR development and investment has been devoted
to the small percentage of life spent in an acute care facility or
clinic, but until recently without substantial support for where
people live. Personal health records, patient portals and greater

patient involvement in their record are all growing rapidly.53

Patient contribution of self-monitoring, vital signs and outcome
measurements can provide a fuller, more accurate health record.

ADDRESSING EHR WEAKNESSES
This listing of real and potential EHR capabilities is not materi-
ally different from those described a quarter century ago.54 What
is clearer today are EHRs’ weaknesses, highlighted recently not
only by broad EHR adoption by technology-avid pioneers and
developers, but by the majority of physicians, nurses and other
health professionals. Most clinicians require hours of training to
use them safely, many feel EHRs usability lags behind technology
in other sectors of society55 and that EHRs require too much
time to use56 and contribute to professional dissatisfaction.57

CPOE also has the potential to introduce errors and requires
extra time for physicians.58 Both the public and physicians have
raised concerns about privacy of EHR data and limitations of
anonymisation of data.59 Broad EHR adoption has been very dif-
ficult and expensive in the USA and the UK.60 61

Difficulties with EHR documentation include time require-
ments, risk that the patient story is lost or, on the other hand,
that narrative notes may not contain data needed to improve
care quality.62 Possible solutions include capturing high-value
data that patients can often enter as well or better than

Table 2 EHR capabilities for improving care and their impact. Key articles and reviews

Key findings References

Decision support
General Evidence suggests that some CDSSs can improve physician performance and use of CDS and computerised

provider order entry.

32 33

Many CDSSs improve practitioner performance and healthcare process measures across diverse settings. The
effects on patient outcomes remain understudied and, when studied, they are inconsistent. Evidence for
clinical, economic, workload and efficiency outcomes remains sparse.

34 35

Recommendations for improving decision support 36

Reminders
General Computer reminders produce care improvements, though less than generally expected from the

implementation of computerised order entry and electronic medical record systems.

Preventive care Reminders improve timeliness and completeness of preventive care interventions. 12 13

Chronic illness Process benefits are easier to achieve than outcomes benefits, especially for chronic diseases. 37

CPOE
Adverse drug events risk Risk of serious adverse drug events is reduced by 55%. 17

Adverse drug event events and outcomes CPOE with CDS can improve patient safety and can lower medication-related costs. Few studies measured the
effects of CPOE and CDS on rates of adverse drug events and none of the studies were randomized
controlled trials.

18 38

Appropriate imaging ordering Computerised CDS integrated with the EHR can improve appropriate use of diagnostic radiology by a
moderate amount and can decrease the use by a small amount.

Ordering of appropriate anti-infective A computerised anti-infective management programme can improve outcomes and reduce costs. 23

Effect on anti-infective time of delivery Implementation of an electronic order-management system improved the timeliness of antibiotic
administration to critical-care patients.

Complying with patient care guidelines EHR order sets can increase compliance with care guidelines. 19

Diagnostic accuracy
Differential diagnosis Experimental diagnostic systems performed as well as clinicians in some domains. 41

Diagnostic decision support Diagnostic HIT research is still in its early stages with few demonstrations of measurable clinical impact. 42

Therapeutic recommendations Early systems provide advice on lymphoma treatment similar to the treatment provided in a university
oncology clinic.

30 43

Use in the ICU EHRs have not been shown to have substantial effect on ICU mortality, length of stay or cost. 21

Record visualisation and summarisation Application of data visualisation techniques to EHRs is currently limited by data complexity and
incompleteness, but there is growing research to leverage ‘big data’ techniques to patient data.

Population and public health EHRs can expand the role of current surveillance efforts and can help bridge the gap between public health
practice and clinical medicine.

CDS, clinical decision support; CDSS, clinical decision support system; CPOE, computerised practitioner order entry; EHR, electronic health record; HIT, health information technology;
ICU, intensive care unit.

Payne TH. Heart 2016;102:1782–1787. doi:10.1136/heartjnl-2015-308724 1785

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providers and appropriate mixture of narrative and encoded
data.63 Addressing documentation problems may require
changes to regulation and reimbursement models (eg, EHR
vendors support current evaluation and management rules in
the USA), broadening documentation requirements for reim-
bursement from the entire healthcare team including the patient
and not just the physicians.64

Particularly relevant to this discussion is that alerts and order
checks are not well accepted.65 66 Most alerts for drug-drug
interactions—one of the most common alerts clinicians experi-
ence—are based on simple logic that does not consider labora-
tory results, age or provider response to prior similar alerts.
Improving decision support requires underlying rules that reflect
patient and provider characteristics and use of more detailed
and complete patient data. Table 3 summarises recent reports
that propose EHR improvements.

VISION
The transition from paper to electronic records has largely
occurred in many countries. We now need a more efficient,
comfortable clinician-user-EHR interaction with EHR features
that augment human strengths so that the EHR captures the full
history of health, illness and impact of treatments and also sub-
stantially helps us improve the care we deliver. With such an
EHR we can potentially learn immensely from countless visits,
hospitalisations, procedures and even more from the everyday
experience of people in health and disease, as captured in the
EHR. We can apply what we learn to decision support that is
‘smarter than the doctor’, to automated diagnostic assistance
and to data analytics that offer insights not previously possible.

Unifying models for improving care include the concept of a
learning healthcare system70 where information gathered in the
EHR in the process of care loops back to improve health and
care delivery with little delay. The learning healthcare system
calls for feedback and analysis of enormous volumes of infor-
mation to complement the view provided by today’s controlled
clinical trials. Imagine capitalising on and supporting what
physicians do best without unduly detracting from time spent
with patients, yet continuously analysing the records to
measure and inexorably improve care. This is the potential of
EHRs: to serve as an integral part of our efforts to improve
and to transform care.

Competing interests None declared.

Provenance and peer review Commissioned; internally peer reviewed.

REFERENCES
1 Institute of Medicine. Crossing the quality chasm: a new health system for the 21st

century. Washington DC: National Academy Press, 2001.
2 National Audit Office. The National Programme for IT in the NHS: progress since

2006. NAO, 2008. http://www.nao.org.uk/publications/0708/the_national_
programme_for_it.aspx (accessed 8 Jun 2016).

3 Blumenthal D. Launching HITECH. N Engl J Med 2010;362:382–5.
4 Byyny RL. The tragedy of the electronic health record. Pharos Alpha Omega Alpha

Honor Med Soc 2015;78:2–5.
5 Payne TH. Snapshot at mid-stride: current state of EHRs and their use by clinicians.

In: Weaver C, Ball M, Kim G, eds. Healthcare information management systems. 4th
edn. New York: Springer, 2015.

6 Barnett GO. The application of computer-based medical-record systems in
ambulatory practice. N Engl J Med 1984;310:1643–50.

7 Halamka J. The Era of Epic. http://geekdoctor.blogspot.com.offcampus.lib.
washington.edu/2013/07/the-era-of-epic.html (accessed 27 Feb 2016).

8 Monegain B. Epic scores EHR contract from Vanderbilt University Medical Center,
beats Cerner. 21 December 2015. http://www.healthcareitnews.com/news/
epic-scores-ehr-contract-vanderbilt-university-medical-center-beats-out-cerner
(accessed 27 Feb 2016).

9 McDonald CJ, Tierney WM. Computer-stored medical records. Their future role in
medical practice. JAMA 1988;259:3433–40.

10 McDonald CJ. Protocol-based computer reminders, the quality of care and the
non-perfectability of man. N Engl J Med 1976;295:1351–5.

11 Barnett GO, Winickoff R, Dorsey JL, et al. Quality assurance through automated
monitoring and concurrent feedback using a computer-based medical information
system. Med Care 1978;6:962–70.

12 Safran C, Rind DM, Davis RB, et al. Guidelines for management of HIV infection
with computer-based patient’s record. Lancet 1995;346:341–6.

13 McDonald CJ, Hui SL, Smith DM, et al. Reminders to physicians from an
introspective computer medical record. A two-year randomized trial. Annals Intern
Med 1984;100:130–8.

14 Shojania KG, Jennings A, Mayhew A, et al. Effect of point-of-care computer
reminders on physician behaviour: a systematic review. CMAJ 2010;182:
E216–25.

15 Stead WW. Beyond expert-based practice. IOM (Institute of Medicine).
Evidence-based medicine and the changing nature of health care: 2007 IOM annual
meeting summary. Introduction and Overview, p. 19). Washington DC: The National
Academies Press, 2008.

16 Kohn L, Corrigan J, Donaldson M, eds, Committee on Quality of Health Care in
America, Institute of Medicine. To err is human: Building a safer health system.
Washington DC: National Academy Press, 1999.

17 Bates DW, Leape LL, Cullen DJ, et al. Effect of computerized physician order entry
and a team intervention on prevention of serious medication errors. JAMA
1998;280:1311–16.

18 Kuperman GJ, Bobb A, Payne TH, et al. Medication-related clinical decision support
in computerized provider order entry systems: a review. J Am Med Inform Assoc
2007;14:29–40.

19 McGreevey JD III. Order sets in electronic health records: principles of good practice.
Chest 2013;143:228–35.

20 Young J, Slebodnik M, Sands L. Bar code technology and medication administration
error. J Patient Saf 2010;6:115–20.

21 Thompson G, O’Horo JC, Pickering BW, et al. Impact of the electronic medical
record on mortality, length of stay, and cost in the hospital and ICU: a systematic
review and metaanalysis. Crit Care Med 2015;43:1276–82.

22 Gains IM, Georgiou A, Westbrook JI. The impact of PACS on clinician work practices
in the intensive care unit: a systematic review of the literature. J Am Med Inform
Assoc 2012;19:506–13.

23 Evans RS, Pestotnik SL, Classen DC, et al. A computer-assisted management
program for antibiotics and other anti-infective agents. N Engl J Med
1998;338:232–8.

24 Scheepers-Hoeksa AJ, Groulsa RJ, Neef C, et al. Physicians’ responses to clinical
decision support on an intensive care unit—comparison of four different alerting
methods. Artif Intell Med 2013;59:33–8.

25 Chan KS, Fowles JB, Weiner JP. Review: electronic health records and the reliability
and validity of quality measures: a review of the literature. Med Care Res Rev
2010;67:503–27.

26 Rubbo B, Fitzpatrick NK, Denaxas S, et al. Use of electronic health records to
ascertain, validate and phenotype acute myocardial infarction: a systematic review
and recommendations. Int J Cardiol 2015;187:705–11.

27 Amster A, Jentzsch J, Pasupuleti H, et al. Completeness, accuracy, and
computability of National Quality Forum-specified eMeasures. J Am Med Inform
Assoc 2015;22:409–16.

28 Schiff GD, Bates DW. Can electronic clinical documentation help prevent diagnostic
errors? N Engl J Med 2010;362:1066–9.

29 Blois MS. Clinical judgment and computers. N Engl J Med 1980;303:192–7.
30 Shortliffe EH, Buchanan BG. Artificial intelligence. New Engl J Med 1980;

302:1482.

Table 3 Recommendations for improving EHRs and their
implementation

Topic Year(s) References

Improving key functionality
Usability 2013, 2016 55 67

Documentation 2013, 2015 62 63 68

Drug-drug interaction alerts 2016 69

Decision support 2003 36

General EHR improvements
EHR 2020 2015 64

Addressing difficulties with implementation
NHS secondary care 2011 61

EHR, Electronic health record; NHS, National Health Service.

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http://www.nao.org.uk/publications/0708/the_national_programme_for_it.aspx

http://dx.doi.org/10.1056/NEJMp0912825

http://dx.doi.org/10.1056/NEJM198406213102506

http://geekdoctor.blogspot.com.offcampus.lib.washington.edu/2013/07/the-era-of-epic.html

http://www.healthcareitnews.com/news/epic-scores-ehr-contract-vanderbilt-university-medical-center-beats-out-cerner

http://dx.doi.org/10.1056/NEJM197612092952405

http://dx.doi.org/10.1097/00005650-197811000-00007

http://dx.doi.org/10.1016/S0140-6736(95)92226-1

http://dx.doi.org/10.1503/cmaj.090578

http://dx.doi.org/10.1001/jama.280.15.1311

http://dx.doi.org/10.1197/jamia.M2170

http://dx.doi.org/10.1378/chest.12-0949

http://dx.doi.org/10.1097/PTS.0b013e3181de35f7

http://dx.doi.org/10.1097/CCM.0000000000000948

http://dx.doi.org/10.1136/amiajnl-2011-000422

http://dx.doi.org/10.1056/NEJM199801223380406

http://dx.doi.org/10.1016/j.artmed.2013.05.002

http://dx.doi.org/10.1177/1077558709359007

http://dx.doi.org/10.1016/j.ijcard.2015.03.075

http://dx.doi.org/10.1136/amiajnl-2014-002865

http://dx.doi.org/10.1056/NEJMp0911734

http://dx.doi.org/10.1056/NEJM198007243030405

31 Ramnarayan P, Kapoor RR, Coren M, et al. Measuring the impact of diagnostic
decision support on the quality of clinical decision making: development of a
reliable and valid composite score. J Am Med Inform Assoc 2003;10:
563–72.

32 Johnston ME, Langton KB, Haynes RB, et al. Effects of computer-based clinical
decision support systems on clinician performance and patient outcome: a critical
appraisal of research. Ann Intern Med 1994;120:135–42.

33 Jones SS, Rudin RS, Perry T, et al. Health information technology: an updated
systematic review with a focus on meaningful use. Ann Intern Med
2014;160:48–54.

34 Garg AX, Adhikari NK, McDonald H, et al. Effects of computerized clinical decision
support systems on practitioner performance and patient outcomes: a systematic
review. JAMA 2005;293:1223–38.

35 Bright TJ, Wong A, Dhurjati R, et al. Effect of clinical decision-support systems: a
systematic review. Ann Intern Med 2012;157:29–43.

36 Bates DW, Kuperman GJ, Wang S, et al. Ten commandments for effective clinical
decision support: making the practice of evidence-based medicine a reality. J Am
Med Inform Assoc 2003;10:523–30.

37 Einbinder JS, Bates DW. Leveraging information technology to improve quality and
safety. Yearb Med Inform 2007:22–9.

38 Wolfstadt JI, Gurwitz JH, Field TS, et al. The effect of computerized physician order
entry with clinical decision support on the rates of adverse drug events: a systematic
review. J Gen Intern Med 2008;23:451–8.

39 Goldzweig CL, Orshansky G, Paige NM, et al. Electronic health record-based
interventions for improving appropriate diagnostic imaging: a systematic review and
meta-analysis. Ann Intern Med 2015;162:557–65.

40 Cartmill RS, Walker JM, Blosky MA, et al. Impact of electronic order management
on the timeliness of antibiotic administration in critical care patients. Int J Med
Inform 2012;81:782–91.

41 Miller RA, Pople HE Jr, Myers JD. Internist-1, an experimental computer-based
diagnostic consultant for general internal medicine. N Engl J Med
1982;307:468–76.

42 El-Kareh R, Hasan O, Schiff GD. Use of health information technology to reduce
diagnostic errors. BMJ Qual Saf 2013;22(Suppl 2):ii40–51.

43 Hickam DH, Shortliffe EH, Bischoff MB, et al. The treatment advice of a
computer-based cancer chemotherapy protocol advisor. Ann Intern Med 1985;103
(Pt 1):928–36.

44 West VL, Borland D, Hammond WE. Innovative information visualization of
electronic health record data: a systematic review. J Am Med Inform Assoc
2015;22:330–9.

45 Birkhead GS, Klompas M, Shah NR. Uses of electronic health records for public
health surveillance to advance public health. Annu Rev Public Health
2015;36:345–59.

46 Parikh RB, Kakad M, Bates DW. Integrating predictive analytics Into high-value care:
The Dawn of precision delivery. JAMA 2016;315:651–2.

47 Kalava A, Ravindranath S, Bronshteyn I, et al. Typing skills of physicians in training.
J Grad Med Educ 2014;6:155–7.

48 Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an
introduction. J Am Med Inform Assoc 2011;18:544–51.

49 Rubin DL, Lewis SE, Mungall CJ, et al. National Center for Biomedical Ontology:
advancing biomedicine through structured organization of scientific knowledge.
OMICS 2006;10:185–98.

50 HL7. Introducing HL7 FHIR. http://www.hl7.org/implement/standards/fhir/summary.
html (accessed 11 Jan 2015).

51 Mandl KD, Mandel JC, Murphy SN, et al. The SMART Platform: early experience
enabling substitutable applications for electronic health records. J Am Med Inform
Assoc 2012;19:597–603.

52 Wei L, Lang CC, Sullivan FM, et al. Impact on mortality following first acute
myocardial infarction of distance between home and hospital: cohort study. Heart
2008;94:1141–6.

53 Walker J, Darer JD, Elmore JG, et al. The road toward fully transparent medical
records. N Engl J Med 2014;370:6–8.

54 Dick RS, Steen EB, Detmer DE, eds, Committee on Improving the Patient Record;
Institute of Medicine. The computer-based patient record: an essential technology
for health care, Revised Edition (1997). Washington DC: National Academy Press,
1991.

55 Middleton B, Bloomrosen M, Dente MA, et al. Enhancing patient safety and quality
of care by improving the usability of electronic health record systems:
recommendations from AMIA. J Am Med Inform Assoc 2013;20:e2–8.

56 McDonald CJ, Callaghan FM, Weissman A, et al. Use of internist’s free time by
ambulatory care Electronic Medical Record systems. JAMA Intern Med
2014;174:1860–3.

57 Friedberg MW, Peggy G, Chen PG, et al. Factors affecting physician professional
satisfaction and their implications for patient care, health systems, and health
policy. Santa Monica: RAND Corporation, 2013.

58 Ash JS, Sittig DF, Poon EG, et al. The extent and importance of unintended
consequences related to computerized provider order entry. J Am Med Inform Assoc
2007;14:415–23.

59 Papoutsi C, Reed JE, Marston C, et al. Patient and public views about the security
and privacy of Electronic Health Records (EHRs) in the UK: results from a mixed
methods study. BMC Med Inform Decis Mak 2015;15:86.

60 Rosenbaum L. Transitional Chaos or Enduring Harm? The EHR and the Disruption
of Medicine. N Engl J Med 2015;373:1585–8.

61 Sheikh A, Cornford T, Barber N, et al. Implementation and adoption of nationwide
electronic health records in secondary care in England: final qualitative results
from prospective national evaluation in “early adopter” hospitals. BMJ 2011;343:
d6054.

62 Morrison Z, Fernando B, Kalra D, et al. National evaluation of the benefits and risks
of greater structuring and coding of the electronic health record: exploratory
qualitative investigation. J Am Med Inform Assoc 2014;21:492–500.

63 Cusack CM, Hripcsak G, Bloomrosen M, et al. The future state of clinical data
capture and documentation: a report from AMIA’s 2011 Policy Meeting. J Am Med
Inform Assoc 2013;20:134–40.

64 Payne TH, Corley S, Cullen TA, et al. Report of the AMIA EHR-2020 Task Force on
the Status and Future Direction of EHRs. J Am Med Inform Assoc
2015;22:1102–10.

65 Nanji KC, Slight SP, Seger DL, et al. Overrides of medication-related clinical decision
support alerts in outpatients. J Am Med Inform Assoc 2014;21:487–91.

66 Lin CP, Payne TH, Nichol WP, et al. Evaluating clinical decision support systems:
Monitoring CPOE order check override rates in the Department of Veterans
Affairs’ Computerized Patient Record System. J Am Med Inform Assoc
2008;15:620–6.

67 American Medical Association. Improving Care: Priorities to Improve Electronic
Health Record Usability. http://www.ama-assn.org/ama/pub/news/news/2014/
2014-09-16-solutions-to-ehr-systems.page (accessed 15 Mar 2016).

68 Kuhn T, Basch P, Barr M, et al. Medical Informatics Committee of the American
College of Physicians. Clinical documentation in the 21st century: executive
summary of a policy position paper from the American College of Physicians. Ann
Intern Med 2015;162:301–3.

69 Payne TH, Hines LE, Chan RC, et al. Recommendations to improve the usability of
drug-drug interaction clinical decision support alerts. J Am Med Inform Assoc
2015;22:1243–50.

70 Friedman CP, Wong AK, Blumenthal D. Achieving a nationwide learning health
system. Sci Transl Med 2010;2:57cm29.

Payne TH. Heart 2016;102:1782–1787. doi:10.1136/heartjnl-2015-308724 1787

Review

http://dx.doi.org/10.1197/jamia.M1338

http://dx.doi.org/10.7326/0003-4819-120-2-199401150-00007

http://dx.doi.org/10.7326/M13-1531

http://dx.doi.org/10.1001/jama.293.10.1223

http://dx.doi.org/10.7326/0003-4819-157-1-201207030-00450

http://dx.doi.org/10.1197/jamia.M1370

http://dx.doi.org/10.1007/s11606-008-0504-5

http://dx.doi.org/10.7326/M14-2600

http://dx.doi.org/10.1016/j.ijmedinf.2012.07.011

http://dx.doi.org/10.1056/NEJM198208193070803

http://dx.doi.org/10.1136/bmjqs-2013-001884

http://dx.doi.org/10.1136/amiajnl-2014-002955

http://dx.doi.org/10.1001/jama.2015.19417

http://dx.doi.org/10.4300/JGME-D-13-00164.1

http://dx.doi.org/10.1136/amiajnl-2011-000464

http://dx.doi.org/10.1089/omi.2006.10.185

http://www.hl7.org/implement/standards/fhir/summary.html

http://dx.doi.org/10.1136/amiajnl-2011-000622

http://dx.doi.org/10.1136/hrt.2007.123612

http://dx.doi.org/10.1056/NEJMp1310132

http://dx.doi.org/10.1136/amiajnl-2012-001458

http://dx.doi.org/10.1001/jamainternmed.2014.4506

http://dx.doi.org/10.1197/jamia.M2373

http://dx.doi.org/10.1186/s12911-015-0202-2

http://dx.doi.org/10.1056/NEJMp1509961

http://dx.doi.org/10.1136/bmj.d6054

http://dx.doi.org/10.1136/amiajnl-2013-001666

http://dx.doi.org/10.1136/amiajnl-2012-001093

http://dx.doi.org/10.1093/jamia/ocv066

http://dx.doi.org/10.1136/amiajnl-2013-001813

http://dx.doi.org/10.1197/jamia.M2453

http://www.ama-assn.org/ama/pub/news/news/2014/2014-09-16-solutions-to-ehr-systems.page

http://dx.doi.org/10.7326/M14-2128

http://dx.doi.org/10.1093/jamia/ocv011

http://dx.doi.org/10.1126/scitranslmed.3001456

heartjnl-102-1782_8014

The electronic health record as a catalyst for quality improvement in patient care
Abstract
Introduction
Problems that EHRs can help solve
New EHR technologies
Analytics
Voice technologies
Natural language processing
EHR architecture
Linking EHRs with other databases
Patient involvement
Addressing EHR weaknesses
Vision
References

Original Investigatio

Assessing HITECH Implementation
and Lessons: 5 Years Later

M A R S H A G O L D a n d C AT H E R I N E McL A U G H L I N

Mathematica Policy Research

Policy Points

� The expansive goals of the Health Information Technology for Eco-
nomic and Clinical Health (HITECH) Act required the simultaneous
development of a complex and interdependent infrastructure and a wide
range of relationships, generating points of vulnerability.

� While federal legislation can be a powerful stimulus for change, its
effectiveness also depends on its ability to accommodate state and local
policies and private health care markets.

� Ambitious goals require support over a long time horizon, which can be
challenging to maintain. The future of health information technolog

(health IT) support nationally is likely to depend on the ability of
the technology to satisfy its users that its functionalities address the
interests policymakers and other stakeholders have in using technology
to promote better care, improved outcomes, and reduced costs.

Context: The Health Information Technology for Economic and Clinical Health
(HITECH) Act set ambitious goals for developing electronic health information
as one tool to reform health care delivery and improve health outcomes. With
HITECH’s grant funding now mostly exhausted but statutory authority for
standards remaining, this article looks back at HITECH’s experience in the
first 5 years to assess its implementation, remaining challenges, and lessons
learned.

Methods: This review derives from a global assessment of the HITECH Ac

Earlier, we examined the logic of HITECH and identified interdependencies
critical to its ultimate success. In this article, we build on that framework to
review what has and has not been accomplished in building the infrastructure
authorized by HITECH since it was enacted. The review incorporates quan-
titative and qualitative evidence of progress from the global assessment and

The Milbank Quarterly, Vol. 94, No. 3, 2016 (pp.

654

-687)
c© 2016 Milbank Memorial Fund. Published by Wiley Periodicals Inc.

654

Assessing HITECH Implementation and Lessons 655

from the evaluations funded by the Office of the National Coordinator for
Health Information Technology (ONC) of individual programs authorized by
the HITECH Act.

Findings: Our review of the evidence provides a mixed picture. Despite
HITECH’s challenging demands, its complex programs were implemented,
and important changes sought by the act are now in place. Electronic health
records (EHRs) now exist in some form in most professional practices and
hospitals eligible for HITECH incentive payments, more information is being
shared electronically, and the focus of attention has shifted from adoption of
EHRs toward more fundamental issues associated with using health informa-
tion technology (health IT) to improve health care delivery and outcomes.

In some areas, HITECH’s achievements to date have fallen short of the hopes
of its proponents as it has proven challenging to move meaningful use beyond
the initial low bar set by Meaningful Use Stage 1. EHR products vary in their
ability to support more advanced functionalities, such as patient engagemen

and population-based care management. Many barriers to interoperability per-
sist, limiting electronic communication across a diverse set of largely private
providers and care settings.

Conclusions: Achieving the expansive goals of HITECH required the simulta-
neous development of a complex and interdependent infrastructure and a wide
range of relationships, some better positioned to move forward than others. To
date, it has proven easier to get providers to adopt EHRs, perhaps in response to
financial incentives to do so, than to develop a robust infrastructure that allows
the information in EHRs to be used effectively and shared not only within clin-
ical practices but also across providers. Effective exchange of data is necessary
to drive the kinds of delivery and payment reforms sought nationwide.

Keywords: health information technology, health care delivery, federal health
policy, health reform.

T
he Health Information Technology for Economic
and Clinical Health (HITECH) Act, enacted as part of the
American Recovery and Reinvestment Act of 2009 (ARRA),

has been a major health policy initiative seeking to promote the use of
electronic health information as one tool to reform the delivery of health
care and improve health outcomes. Early in HITECH’s implementa-
tion, we analyzed factors that would drive initial efforts to adopt elec-
tronic health records (EHRs) and expand health information exchange
(HIE) to support meaningful use (MU).1 In that analysis, we exam-
ined the logic behind HITECH’s requirements and programs in light

656 M. Gold and C. McLaughlin

of the factors driving EHR uptake (affordability, product availability,
practice integration, and provider attitudes) and HIE (data harmoniza-
tion, privacy and security, organizational interfaces, access to technology,
provider participation, and patient support). Our analysis showed how
the ambitious scope of HITECH’s objectives would require rapid and
simultaneous progress on multiple fronts, with critical interdependen-
cies complicating execution. Such interdependencies were particularly
challenging because key decisions important to the ultimate success
of the HITECH Act would be made by private providers and diverse
state and local public and private organizations, over which the federal
government had only limited authority.

HITECH’s funding is now mostly disbursed, but federal authority to
establish standards and other requirements for health information tech-
nology (health IT) remains, and collaborative engagement with states
and the private sector continues. This article, developed as part of

larger project funded by the Office of the National Coordinator for
Health Information Technology (ONC) to provide a global assessment
of the HITECH Act, looks back at the experience of HITECH to exam-
ine what was accomplished in terms of implementing the HITECH Act,
remaining challenges, and lessons learned. As part of our evaluation, we
monitored the progress of key activities and looked in more depth at
selected aspects of implementation.2 This article draws on that and the
other work ONC commissioned to evaluate HITECH and its specific
programs, many of which have received little attention in current publi-
cations. The article provides a high-level synthesis of what research and
evaluation reveals about the implementation of HITECH and its vari-
ous programs, including progress on key indicators that ONC presents
on its “dashboard” and other findings that stem from research by our
colleagues and ourselves.

The analysis is not without its limitations. First, the scope and
time line mandated by HITECH—and the guidance and resources
available for this study—meant that benchmarks to measure progress
were limited in some areas. Second, while we summarize what is known
about HITECH’s ability to achieve its ultimate goals, we can provide
only limited insight on this topic within the time frame available for
study. Third, the article emphasizes findings that provide a national
perspective, generally based on federal data sources and studies. While
this perspective is not comprehensive, we believe it includes the major
national information sources on this topic. Many of these reports and

Assessing HITECH Implementation and Lessons 657

informational pieces are not well documented in the available peer
literature, and thus this synthesis provides a valuable lens through
which other studies of health IT can be assessed.

The Logic of HITECH

Figure 1 summarizes the core logic of the HITECH Act, including
the policies and programs built into HITECH to achieve its goals, key
features of the environment in which it is being implemented, and the
critical interdependencies that will influence the ultimate success of the
HITECH Act. As is well recognized,3 HITECH provided incentives
and support for providers to adopt EHRs and allow their information to
be exchanged electronically to promote the MU of health information
and improve health care outcomes. Even though HITECH preceded the
Patient Protection and Affordable Care Act of 2010 (ACA) it anticipated
the ACA and sought to develop digitized health information regarded as
important in reforming health care delivery.4,5 As reflected in Figure 1,
HITECH’s programs and policies sought to address key drivers behind
the adoption of EHRs (such as affordability and availability) and HIE
(such as data harmonization and organizational interfaces).

Only partially addressed by HITECH is the impact on implemen-
tation of the considerable variation in state policy environments and
private and local market forces across the country that have influenced
the historical experience with health IT, the form of delivery, and the
practical realities of implementing change.

Meaningful Use as Central Concept

As emphasized by the National Coordinator for Health IT when
HITECH was adopted,3 HITECH’s central concept is to encourage MU
of EHRs to enhance individual and population health outcomes and
research. MU requirements were envisioned as evolving over 3 stages,
becoming increasingly sophisticated over time as capacity grew.7 The
intent was to move beyond meeting formal requirements that could be
“checked off” toward effectively changing the way care is delivered and
how patients and providers interact—what some refer to as “meaningful,
meaningful use.”8 HITECH’s components provide building blocks for
this transition.

658 M. Gold and C. McLaughlin

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Assessing HITECH Implementation and Lessons 659

Critical Building Blocks

Adoption of EHRs. To encourage providers to adopt EHR systems,
HITECH authorized up to $27 billion in Medicare and federal Medicaid
payments over 10 years to eligible providers who adopted EHR systems
that were certified as meeting federal standards and who attested to
meeting MU requirements. (Eligible hospitals received payments from
both Medicare and Medicaid; eligible professionals had to choose if they
qualified for both.) The HITECH Act also funded a Regional Exten-
sion Center (REC) program on a short-term basis to assist high-priority
providers in making the transition if they needed help to adopt and use
EHRs. (RECs focused on providers in small practices and those serv-
ing disadvantaged or rural populations, anticipating that such support
could be important to reduce disparities resulting from uneven access to
market-based support.) Short-term workforce programs were funded to
meet the anticipated increased demand for qualified professionals with
skills in health IT.

Although privacy and security protections were already in place
through the Health Insurance Portability and Accountability Act of
1996 (HIPAA), HITECH recognized the importance of these issues,
with an emphasis on funding education and enforcement of HIPAA and
identifying where additional protections might be required.

Exchange of Health Information. HITECH also sought to improve
data harmonization and to enable features that would allow data to be
electronically exchanged at diverse geographical levels. To complement
national policy, the HITECH Act provided short-term support for a state
health information exchange (state HIE) program to work on exchange
with stakeholders in states and local areas. Under HITECH, the US
Department of Health and Human Services (HHS) also continued to
work on national efforts to develop standards and mechanisms to promote
exchange and protect the security and privacy of health information.

Supporting Meaningful Use of Health IT. Recognizing variations in
health IT experience and maturity, the legislation authorized funding
for a Beacon program,9 initially envisioned as supporting more advanced
communities to illustrate the value of MU of health IT to improve health
outcomes. The Strategic Health IT Advanced Research Project (SHARP)
program was intended to spur applied research in select areas, including
the security of health IT, patient-centered cognitive support, network
architecture, and the use of EHRs. The subsequent enactment of the

660 M. Gold and C. McLaughlin

ACA provided increased support for changes in health care delivery and
payment that would complement MU requirements and enhance the
value of electronic health information. Over time, recognition grew that
patient engagement was also a critical aspect of change and the MU of
information.6,1

Time Frame and Resources. HITECH called for concurrent implemen-
tation on multiple fronts on a tight time line, in which government
resources were front-loaded. Implicit in the HITECH Act were assump-
tions that MU would transition through all 3 of its stages by 2016,
with no additional Medicare financial incentive payments authorized
after that year and penalties imposed starting in 2015 for those who did
not begin to attest by 2014. Medicaid’s initial payment requirements
for eligible professionals were less demanding than Medicare’s require-
ments on the front end; Medicaid payments were also higher than those
of Medicare and are slated to continue until 2020. Grant-supported
programs involved substantially fewer resources than anticipated MU
payments ($27 billion). Three-quarters of the funds went to the REC
and state HIE programs (Figure 2).

Funds for all grant-supported programs have largely been spent
(Figure 3). Funding was deliberately front-loaded, consistent with the
broader intent of ARRA (through which HITECH was authorized) to
provide a short-term stimulus for the economy. This front-loading was
especially true for the workforce programs, for which almost two-thirds
of the spending occurred in the first year (see Figure 3).

Implementation Experience

Moving From Meaningful Use Stage 1 to
Stage 2 Slower Than Projected

As of September 2015, more than 307,000 Medicare-eligible profession-
als (physicians, dentists, podiatrists, optometrists, and chiropractors) and
nearly 4,500 Medicare-eligible hospitals met at least MU Stage 1 re-
quirements (Figure 4). This means that they have an EHR that meets
certification requirements and that they have attested to use of that
EHR that meets Stage 1 criteria.11 Because of the different rules under
which they operate, some Medicaid-eligible professionals (physicians,
nurse practitioners, certified nurse-midwives, dentists, and physician
assistants) received initial payments upon acquisition of an EHR alone

Assessing HITECH Implementation and Lessons 661

Figure 2. HITECH Grant Program Funding (in millions)a

$721
42%

$564
33%

$118
7%

$250
15%

$60
4%

REC Program State HIE Program
Workforce Programs Beacon Communities Program
SHARP Program

aDerived from ONC grants management reports.

(adopt, implement, upgrade [AIU]) and have not yet gone on to meet
the Stage 1 attestation requirements.12,13

By December 2014, 94% of eligible hospitals had been paid for
demonstrating MU Stage 1 in the Medicare incentives program.15 ONC
estimates that 73% of eligible health professionals had been paid by
2014.16 Physicians that adopted EHRs following HITECH (2011-2013)
are particularly likely to cite incentive payments or financial penalties
as major influences on their decisions.17

Originally, Medicare providers attesting to MU Stage 1 in 2011, at the
program’s start, were to begin Stage 2 attestation in 2013. Deadlines
were extended, however, and Stage 2 attestations, especially among
eligible professionals, did not begin to grow until 2015. As a result,
Stage 3 has been delayed from its initial start date of 2015; proposed
Stage 3 requirements were sent out for comment in summer 2015.18

The final rules, released for public comment in October 2015, allow
providers to attest to Stage 3 for the first time on a voluntary basis in

662 M. Gold and C. McLaughlin
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Assessing HITECH Implementation and Lessons 663

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664 M. Gold and C. McLaughlin

2017.19 Stage 3 is viewed as a streamlined set of functionalities that
can be met more flexibly but are also more advanced. As currently
envisioned, Stage 3 requirements will apply to most eligible providers
in 2018, regardless of their previous status.

The delays in moving from MU Stage 1 to later stages of adop-
tion are important, as many advanced functionalities, such as electronic
HIE and patient engagement, were addressed only in limited form in
Stage 1 in the interest of jump-starting the program. Due to these de-
lays, fewer providers will be offered the carrot of financial payments
to make the transition as the program shifts from payments to penal-
ties, potentially influencing the provider support that is essential to
the success of HITECH. Providers’ acceptance of future stages of MU
may depend on how well the MU requirements reinforce their par-
ticular needs and how the dominant forms of delivery and payment
evolve.

Broad-Based EHR Adoption

EHR Adoption Nationwide. In 2014, nearly all reporting hospitals
(97%) possessed certified EHR technology, with three-quarters (75.5%)
having at least a basic system that included a defined set of functions
in at least 1 unit in the hospital. This change represents a substantial
increase from the adoption rates of 15.6% in 2010 and 9.4% in 2008.20

Among office-based physician practices in 2014, 83% had an EHR sys-
tem, with half (51%) having a basic system with specific functionalities
important to MU—more than double the share that had such a sys-
tem in 2009 (21.8%) before HITECH was passed.21 One study found
that while the percentage of physicians adopting any EHR system had
increased by more than it would have without MU incentives, the dif-
ference was not statistically significant.22 But EHR adoption rates are
higher among hospitals eligible than those ineligible for incentives.23

Researchers comparing trends in EHR adoption rates for the acute care
hospitals eligible for MU incentives to those of ineligible hospitals (such
as specialty and longer-stay hospitals) attribute the much stronger gains
in EHR use to HITECH and its MU incentives.24 Other researchers,
estimating the increase in payments from Medicaid and Medicare that
each hospital anticipated receiving from the MU payments in its first
year of eligibility, found that these incentives led to an additional 10%
of hospitals adopting an electronic medical record in 2011.25 This same

Assessing HITECH Implementation and Lessons 665

study estimated that the 2011 level of adoption would have been reached
in 2013 without the incentives.

EHR adoption is broad but to some extent still uneven, with lower
rates of adoption in small rural hospitals and in small specialty and
physician-owned practices.26 Adoption rates also tend to be low for
many providers excluded from MU incentives, such as those involved in
long-term care and behavioral health.27 Such gaps create challenges for
care management.

The REC Program and the “Digital Divide.” The REC program was
structured to give program grantees strong incentives to encourage high-
priority providers to attest to MU since, after an initial small payment,
a grantee’s future funding was dependent on the grantee’s demonstrated
success in signing up professionals, having them adopt EHRs, and attain-
ing sufficient quality for MU payments. ONC’s program data show that
the REC program met its goal of getting 100,000 providers to MU, with
a focus on high-priority providers.28 ONC’s external evaluation of the
REC program by the American Institutes for Research (AIR)29 found
that participating with an REC was positively associated with EHR
adoption and receipt of MU payments among primary care physicians
whose practices were small or served a large underserved population—a
finding of the Government Accountability Office as well.30 While the
form of payment used with RECs could incentivize grantees to sign up
those providers readier to attest to MU (so-called low-hanging fruit), the
REC evaluation showed that providers using the REC program included
both early and late adopters and also were more likely to be those without
ready access to other sources of support, such as hospital systems. This
finding is consistent with the REC program’s targeting those providers
that fall through the cracks, as HITECH intended.

Critical ingredients behind REC success are less well understood;
providers participating with RECs reported they were no less likely
to experience challenges than nonparticipants, and the AIR evaluation
was unable to distinguish among the multiple potential factors that
may have contributed to this finding. With federal REC funding end-
ing, the sustainability of RECs depended on their ability to survive
in the marketplace (or on their finding other sources of support). The
AIR evaluation found that, whereas some RECs are likely to achieve
sustainability with revenue from users, most will face challenges in do-
ing so, particularly as they lose staff when their grant funds decline.
From a public policy perspective, the extent of loss will depend on how

666 M. Gold and C. McLaughlin

effective the REC has been. If effective RECs are not sustainable in the
absence of a subsidy, it could create impediments to continued progress
for high-priority providers with no alternative sources of support.

Availability of Certified EHR Products and Practice Integration.
HITECH succeeded in spurring a large and active vendor market to
offer EHRs, especially for office-based practices in which such products
previously were rare. Our analysis of the ambulatory vendor market in
2012 showed that eligible office-based professionals attesting to MU
from 2011 to 2012 used products from 353 different vendors, although
16 firms accounted for 75% of the market.31 A common measure of
market competition (the Herfindahl-Hirschman Index32) showed the
ambulatory market to be highly competitive, particularly for practices
with 50 or fewer professionals. The vendors and external analysts we
interviewed concurred that Stage 1 requirements set a relatively low bar
for market entry.

Stage 1 certification requirements initially were effective in generat-
ing extensive product choice, but they may have contributed to longer-
term problems as vendors did not necessarily have to create products
that anticipated requirements in later stages and for the more rigorous
testing to be employed in the permanent certificate program set up
in Stage 2.31 The literature shows that planning and vendor selection,
workflow and software design, training and user support, and optimiza-
tion and modification are all critical to the successful implementation of
EHRs.33 Although both vendors and RECs say they have worked with
providers on these issues,29,31 providers’ dissatisfaction with the ability
of products to integrate with their workflow has contributed to their
opposition to moving forward with future stages of MU.34

Workforce Implications. Concerned that workforce limitations would
serve as barriers to progress in EHR adoption, especially for small and
rural physician practices and critical access hospitals,35 HITECH in-
cluded front-end funding for programs to support staff training in the
relevant skills required by the HITECH Act.

ONC’s external evaluation of these programs (done by NORC at
the University of Chicago),36 found that, while variations existed across
markets, the university and community college programs generally were
well received by students and resulted in a higher share of graduates
employed both overall and in the health IT field, as a result of the
training they received. These programs also developed curriculum and
credentialing tools that may prove valuable over time. The evaluation

Assessing HITECH Implementation and Lessons 667

also found, however, that the programs were not as well connected to
the employer community as they might have been.

Apart from direct workforce program effects, HITECH may have
stimulated the growth of labor force opportunities in the private sector;
the Bureau of Labor Statistics reported that health IT jobs grew by
50,000 as a result of HITECH, with a 20% growth projected from
2008 to 2018.37

Health Information Exchange Growing
but Interoperability Still Limited

Electronic Exchange by Hospitals. Between 2010 and 2014, the per-
centage of nonfederal acute care hospitals that exchanged health in-
formation with any outside ambulatory provider or hospital increased
from 44% to 76%.38 In 2014, 69% of hospitals exchanged laboratory
results, 65% radiology reports, 64% summary of care records, and 55%
medication histories. In 2008, the comparable figures were 35%, 37%,
25%, and 21%. Although geographical variation exists, the majority of
hospitals in most states report some exchange with outside providers.
The mechanisms used for such exchange are not defined and are likely
to vary across hospitals.

Despite this progress, there are still barriers to exchange that limit its
breadth. While the indicator used above is the same one ONC tracks on
Health IT Dashboard for HITECH Progress, it is possible for a provider
to satisfy the measure and still have relatively limited external exchange
of information. In 2013, we interviewed staff at 6 large and diverse
hospital systems with relatively high rates of exchange.39 We found that
all of these systems had worked on achieving some form of exchange;
some had made this a priority years ago. Although they supported
broad-based exchange, they saw many barriers to achieving it, including
limited ability to exchange information with affiliated providers, the
diversity of EHRs used, and uneven EHR uptake among some important
provider groups. In their own efforts, they prioritized exchange involving
providers within their health system or with outside providers with
whom they share patients because the business case was stronger. Most
supported more broad-based community exchange, but they saw such
exchange as complementing their work rather than replacing it and
perceived barriers to the evolution of such broad-based community-wide
exchange.

668 M. Gold and C. McLaughlin

Electronic Exchange by Office-Based Physicians. Exchange is more lim-
ited among office-based practices, although, like hospitals, they give pri-
ority to exchange within their own organizations. Whereas 39% of office-
based physicians in 2013 reported any electronic exchange with other
ambulatory providers or hospitals, rates were substantially higher within
physicians’ own organizations than outside of them, and physicians in
larger practices were much more likely to exchange information.40 Al-
though the proportion of physicians reporting electronic exchange with
other providers increased to 42% in 2014, only 16% of physicians were
sharing at least 1 of 5 kinds of information with an outside provider.41

External Exchange with Clinical Laboratories. Stage 1 limited require-
ments for electronic exchange of laboratory test data to a “menu” measure
because it was unclear whether laboratories could support that function-
ality. However, an ONC survey of clinical laboratories in 2012 found
that 67% had the capability to send structured test results to the order-
ing physician, and 80% used that functionality, although performance
varied across states.42 About 58% of test results that hospitals and inter-
mediaries processed for Medicare patients from clinical laboratories were
sent electronically. The 2 main barriers reported by surveyed laborato-
ries were the high subscription rates for exchange services (19%) and the
lack of harmonization among industry-accepted standards (17%); about
9% said their primary challenge was that EHR systems could not accept
their data. Stage 2 requires incorporating laboratory results into EHRs
in a structured format, though it does not require that the results be
received electronically.

Federal Efforts Supporting Exchange. Perhaps recognizing the tight
time frame and limited program resources of HITECH, as well as re-
search showing mixed results from earlier efforts to develop state or
locally based exchange at those levels, HHS’s initial requirements for
exchange in Stage 1 were limited, though they set out markers to show
areas of future intention. (Stage 2 added requirements for electronic ex-
change of summary of care records, laboratory test exchange [for hospitals
only], consumer engagement, and additional public health reporting, for
example.)

HHS developed the Direct Project, a set of technical standards and
services to “push” information from one health care entity to another in a
trusted network.43 Direct provides a national standards-based, encrypted
method for sending electronic health information at a relatively low
cost. It is a simple way for providers with no other means to exchange

Assessing HITECH Implementation and Lessons 669

information on a one-way basis. Direct was piloted in 2 states, starting
in February 2011,44 and ONC issued guidelines for its broader use in
more than 40 statewide HIEs a few months later.45 Vendors seeking
certification for Stage 2 are required to demonstrate that their product
utilizes the Direct protocol.

HHS also developed the Nationwide Health Information Network
(NwHIN, now eHealth Exchange), a set of standards, policies, and
procedures that provides health systems with more advanced HIE func-
tionality, in particular the ability to send and receive electronic health
information with outside providers. In 2012, the exchange was transi-
tioned to a nonprofit public and private partner, operating as Healthe-
way, now called the Sequoia Project, which runs the eHealth Exchange.
A total of 110 systems and related organizations now participate in the
eHealth Exchange.46

State HIE Program. Early on, ONC decided that both the relatively
disappointing historical experience with state and regional exchange and
the limited funds available to achieve progress over a short period of time
meant that it was important to focus state efforts on immediate needs
and local priorities.1 States were asked to develop state- and locality-
appropriate plans and priorities to use program resources to (1) create or
foster exchange, (2) fill gaps in exchange capacity, and (3) encourage flow
across exchanges. The flexibility afforded states, as well as the differences
in their starting places and strategies, makes it challenging to assess the
success as it is not clear what metrics should be employed nationally in
the long term.

In its evaluation for ONC, NORC found that HIE increased on a
variety of metrics over the period of the program, though there was
considerable variation across states based on state context and program
factors. The former (context) involves state-specific demographic and
market factors and the latter (program) involves decisions states make
about exchange (such as governance structure, technical and consent
models, and supportive legislation). State contextual factors were more
important than decisions states made about the state HIE program itself
in explaining the variation in state progress on HIE in the early years.47

Between 2011 and 2013, smaller states and states with more experi-
ence with EHR adoption before HITECH progressed more than other
states. Over time, market factors (such as concentration of managed care
and hospital competition) also influenced exchange levels. But program
factors also influenced success, often in synergy with context. Across

670 M. Gold and C. McLaughlin

programs, states that used opt-out versus opt-in methods of consent to
share information and those that drew down state HIE funds faster had
higher levels of exchange in later years.

From state HIE grantee reports to ONC on factors affecting grantees’
progress, NORC found that they emphasized the value of diverse stake-
holders for building trust relationships with aligned goals and a shared
sense of ownership. Grantees said that payment reform did play, and
would continue to play, a role in HIE expansion. They also said that
making progress was more resource intensive than anticipated, standards
and requirements were not clear, and there were issues with develop-
ers. However, they viewed interoperability as critical to improving care
through health IT. Grantees saw HITECH as a catalyst for action with
a onetime infusion of funds. They remained concerned about financial
sustainability of HIE efforts as the program drew to a close.

Ongoing Federal Efforts at Standardization to Support Exchange. Al-
though HITECH provided resources for federal leadership on standards
and interoperability and the state HIE program to address issues of
exchange, the legislation was silent on how standardization was to be
achieved and exchange promoted in a health care system that was largely
private and jointly regulated at the federal and state levels.1 Recognizing
that interoperability to support exchange remains a challenge, HHS and
its advisory groups continue to work on identifying ways to enhance it.
ONC’s federal advisory committees—the Health IT Policy Committee
and the Health IT Standards Committee—both have been working for
some time on these issues, which increasingly are recognized as critical to
HITECH’s ultimate success. In its Federal Health IT Strategic Plan for
2015–2020,48 HHS outlines broad-based health IT goals to (1) advance
person-centered and self-management health; (2) transform health care
delivery and community health; (3) foster research, scientific knowledge,
and innovation; and (4) enhance the nation’s health IT infrastructure.
In its Shared Nationwide Interoperability Roadmap,49 ONC calls for
collaboration across stakeholders to advance nationwide interoperabil-
ity that blends work to achieve success in the near term with progress
on reaching the longer-term vision. ONC has proposed a time line for
high-level critical actions needed to move this effort forward between
2015 and 2024. Early feedback on that plan suggests a focus on actions
that are of highest priority and that will have the most impact.50

Limitations of Vendor Products. Emerging evidence also shows that
some vendors use information-blocking techniques that enhance their

Assessing HITECH Implementation and Lessons 671

market position by making it more difficult for providers using
their products to share information with those using other vendors’
products.51 Flexible certification requirements helped spur innovation
in the vendor market; however, if some products prove less competitive,
a market shakeout could cause disruption, particularly since vendors say
product migration is not easy to execute.31

Recent analysis shows that most hospitals and eligible professionals
could accommodate the upgrades in MU standards effective in 2014 by
either upgrading their current product or obtaining additional products
or modules from their current vendor.52 Whether such products can fully
support the more advanced data aggregation and patient engagement
functionalities and information flows demanded by advanced payment
and delivery models53 remains to be seen.

Although technical challenges exist, one can argue that the fact that
no single party is responsible for interoperability in the United States
is a major obstacle to making it happen. Some experts, as reflected
in a JASON report,54 have called for HHS to promulgate a common
architecture for interoperability, based on public application program
interfaces (APIs) and standards. However, achieving consensus on those
standards is difficult when our economy reserves considerable autonomy
for private sector organizations and divides public authority between
federal and state governments.

Meaningful Use in the Marketplace Context

The Beacon Program. HITECH’s ability to use the Beacon program
to generate early evidence on its value in more advanced communities
was impeded because some grantees were not able to make progress as
quickly as anticipated. The Beacon program involved grants to 17 diverse
geographically based collaboratives nationwide to improve health care
quality and outcomes while lowering overall costs of care. Grant funds
were used to support building and strengthening health IT and clinical
transformation efforts in communities believed to be more advanced.

NORC’s independent evaluation of the Beacon program55 found that,
even though they were above average, the communities selected varied
substantially in their rates of EHR adoption and ability to exchange
information. They also varied in their history. For example, 10 Beacon
communities had a base on which to build, whereas 7 of them were
developing exchange infrastructure from the ground up. NORC also

672 M. Gold and C. McLaughlin

found that even more advanced communities experienced challenges
with interoperability involving early EHRs. These contextual barriers
delayed the implementation of clinical transformation efforts, making it
difficult to identify short-term effects of health IT on clinical outcomes.55

Consistent with our framework, grantees whose efforts were aligned with
existing payment and service delivery initiatives were more likely to be
sustainable after grant funding ended.

Payment and Delivery Reform. As reflected in the Figure 1 frame-
work, the implementation of HITECH has been influenced by a variety
of federal and state government policies. For example, the Center for
Medicare & Medicaid Innovation (the Innovation Center) of the Cen-
ters for Medicare & Medicaid Services (CMS), authorized in the ACA,
has been actively engaged in supporting providers, states, and others to
develop and test innovative models of health care delivery, models that
often rely on having an effective IT system in place. At year-end 2014,
the Innovation Center estimated that these demonstrations would soon
serve 2.5 million beneficiaries and include care furnished by more than
60,000 providers.56

Many providers now assume that future requirements will include
movement toward more value-based payment. In early 2015, HHS’s
secretary announced specific goals for shifting Medicare from volume-
to value-based payments, with a target of 50% of traditional Medicare
payments in alternative models by 2018.57 The proposed Stage 3 re-
quirements reinforce an interest in aligning MU performance metrics
with those required for these value-based payments.18 The Medicare Ac-
cess and CHIP Reauthorization Act of 2015 mandates such integration
within a new Merit-Based Incentive Payment System and new param-
eters for alternative payment models overseen by Medicare and other
payers.58

Aligning Goals With Available Functionalities. The movement toward
value-based payment and delivery reform can create additional incen-
tives for providers to adopt and make MU of health IT. However, our
interviews with diverse health system leaders and federal officials also
highlighted the challenges in using digital tools to support reform.53

As these leaders saw it, delivery reform requires changes in processes of
care, with each modification necessitating a specific information flow
to support it. Although there are strong links between health IT and
payment and delivery reform, interviewees said that health IT should be
viewed as much more than an EHR to support the functionalities needed

Assessing HITECH Implementation and Lessons 673

by reform initiatives—and the current EHRs and exchange infrastruc-
ture fall short. Whereas in an ideal world, there would be time to create
an infrastructure before having to employ it to support delivery reform,
those with whom we spoke said that current providers are being asked
to do both at once. Interviewees affiliated with experienced systems said
it took years to evolve their health IT to where it is now; without this
experience as a base for their care delivery reforms, they would be forced
to rely on workarounds that would later have to be replaced. That is
where most providers are now.

A 2014 survey of accountable care organizations (ACOs) confirmed
the need for better alignment; ACOs reported adopting and using health
IT with various capabilities but finding significant gaps that limited
their ability to use it to monitor cost and quality in a timely way.59 Rec-
ognizing these limitations, many key stakeholders now think it could
be important to focus health IT on achieving high-priority objectives,
or “use cases,” that can go further in meeting the emerging needs of
providers within value-based payment models. To think through that
goal, ONC’s Health IT Policy Committee has formed the Advanced
Health Models and Meaningful Use Workgroup and made recommen-
dations to ONC about how to structure priorities that take into account
both technical issues and strategic policy needs through a process that
engages diverse stakeholders.43 Whether an increased focus on specific
use cases will generate buy-in across the provider, vendor, and con-
sumer communities, and ultimately improve interoperability, remains to
be seen.

Consumer Engagement. As reflected in Figure 1, consumer engage-
ment is increasingly regarded as important to care transformation. Im-
proved access to personal health information can enable consumers to
consult with providers and take actions to improve their health that
are consistent with their needs and values. Goals of a patient-centered
health care system include an increase in self-management and preven-
tion, support for seamless interaction with the health care system, and
shared management of health care.60,61 Analysts believe that in order for
such goals to be achieved, there needs to be a shift in consumers’ and
providers’ attitudes toward less hierarchical, more collaborative partner-
ships between patients and providers, enabled by health IT.6,62

Although some progress was made in the early years of HITECH,
the most substantial policies in this area were either adopted in Stage
2 or proposed for Stage 3.10 Stage 1 requirements were limited to

674 M. Gold and C. McLaughlin

capabilities relating to visit summaries and educational resources. In
2013, office-based physicians made limited use of exchange to pro-
mote patient engagement; for example, whereas about two-thirds of
physicians had the ability to provide patients with electronic visit sum-
maries or patient-specific educational resources, only half had the capa-
bility to exchange information electronically with patients.40 Likewise,
4 in 10 physicians could enable patients to view online, download, or
transmit their health information; far fewer made routine use of these
capabilities.40 Requirements for download functionalities were added to
Stage 1 in 2014 concurrently with their introduction in Stage 2.

HITECH also served as an impetus for amending the Clinical Labo-
ratory Improvement Amendments of 1988 (CLIA) to increase patients’
direct access to test results from laboratories. In a 2012 survey designed
to provide baseline data, ONC found that about 30% of clinical laborato-
ries allowed patients direct access to clinical tests, with 17% permitting
electronic access.63 The most common mechanisms of direct access were
through a physician’s EHR, a laboratory web portal, and/or transmis-
sion to a personal health record. A significantly higher percentage of
independent laboratories than hospital-based laboratories allowed such
access.

We recently conducted an environmental scan of the status of con-
sumer engagement and HITECH.10 The available evidence suggests that
consumers respond positively when given electronic access to personal
health information. However, serious technical limitations constrain the
value of the information available to them, particularly if they use mul-
tiple providers. The strength of provider support for consumer-oriented
functionalities is unclear.

Implications of Geographic Variation. A common theme emerging from
both our global assessment and the individual HITECH program eval-
uations is that state and market variation matters and makes a very
important difference in experience with HITECH. EHR adoption var-
ied considerably across markets before HITECH, and this variation
continued afterward, even as adoption rates grew.47,64 Case studies in
8 markets, conducted over a period of time as part of the global as-
sessment, showed that local community and state attributes influenced
both initial and subsequent levels of EHR adoption, HIE, and MU
achievement.64 Despite selection criteria that excluded markets with
the most and least advanced EHR adoption pre-HITECH, the 8 mar-
kets studied still were very diverse in their health IT experience and

Assessing HITECH Implementation and Lessons 675

state health IT policies. The markets also varied in important local
characteristics like purchaser and provider market structure, health IT
infrastructure, and health reform implementation activity, all of which
influenced providers’ incentives to make more advanced use of health
IT. The results suggest that tremendous knowledge and skill are needed
to tailor HITECH programs to specific state and community contexts,
leveraging local assets and adapting to changing conditions over time.

The program evaluations documented similar kinds of state and mar-
ket variation. For example, the state HIE evaluation47 found that concen-
tration of managed care and hospital competition were important factors
in changes in HIE from 2011 to 2013. That evaluation also found cross-
pollination between state HIE programs and payment reform initiatives.
The Beacon evaluation found that providers in competitive markets were
reluctant to share data, with hospitals viewing their data as a competitive
asset.55

Characteristics of state policies and markets tend to persist over time
and are not changed readily by national policy. Thus, variation across
states, communities, and markets is likely to mean that provider behavior
and adoption of MU will continue to evolve unevenly nationwide.

Ultimate Effect on Outcomes Still Unknown

Because HITECH’s implementation proceeded more slowly than origi-
nally expected, it is too early to assess its ultimate effects on the outcomes
it sought, but some early evidence is emerging.

Impact on Individual Care. Most nationwide studies of the role of
health IT in improving quality of care, whether in terms of processes of
care or outcomes for patients, have focused on health IT use by hospitals
and have used data from before the passage of HITECH.65-68 The
evidence of an association between HIE and various measures of care is
lacking, mixed, and mostly dated.69 An analysis that is part of the global
assessment expanded on this prior evidence by examining data from
2010 to 2013, testing for correlations between the growth in health
IT before and after the enactment of HITECH and hospital admissions
and readmissions for Medicare beneficiaries with at least 1 of 4 chronic
conditions—chronic obstructive pulmonary disease (COPD), congestive
heart failure (CHF), diabetes, and ischemic heart disease (IHD).70 The
analysis found that increases in hospital referral region–level health
IT penetration among physicians in ambulatory care settings were

676 M. Gold and C. McLaughlin

correlated with decreases in ambulatory care sensitive condition (ACSC)
admissions for these Medicare patients. When the authors examined
each of the 4 chronic conditions separately, they found that CHF
patients showed the largest magnitude decrease in ACSC admissions.

Impact on Population Health. In addition to its effects on population
health through cumulative effects on individuals, HITECH sought to
enhance public health by supporting the transfer of digital data on indi-
viduals from providers to public health agencies so that the latter could
use those data on a population basis to identify outbreaks, assess trends
in population health, and promote healthy choices for individuals.71

In Stage 1 of MU, reporting of immunization information, electronic
laboratory results, and syndromic surveillance were included among the
menu options eligible hospitals and professionals could use to demon-
strate MU. In Stage 2, all 3 of these public health measures were moved
to the core for eligible hospitals, and eligible professionals are required
to report on at least the first two. The third remains a menu option,
joined by new options for reporting to cancer and other specialized reg-
istries. By February 2014, 40% of eligible providers were submitting
immunization data to public health registries; many fewer (6%) were
submitting syndromic surveillance data.71 Among eligible hospitals,
54% submitted immunization data to public health agencies, 20% syn-
dromic surveillance data, and 15% laboratory results. Another analysis
showed that more Stage 2 hospitals reported on all applicable public
health measures without exclusion than did Stage 1 hospitals.72

Although HIEs in some states helped facilitate exchange with public
health agencies,47,71 many state agencies have found it challenging to
design interoperable systems that can support and accept the extensive
volume of data potentially reported by providers.

Support for Research. The challenges in developing broad-based HIE
contrast with HITECH’s original goal of leveraging EHRs and HIE
to develop comprehensive population-based data to support clinical
and health services research, though it remains part of the long-term
vision of a nationwide learning health system that is part of the Shared
Nationwide Interoperability Roadmap. However, such analysis may
be feasible in more limited form sooner in locales served by public
exchange models or for populations included in private exchanges
that span subgroups of providers who collaborate on a voluntary basis.
Some support for building population-based clinical data is also being
provided by a program funded by the ACA—the Patient-Centered

Assessing HITECH Implementation and Lessons 677

Outcomes Research Institute (PCORI)—which views such capacity as
critical to the infrastructure needed to support its mission.73

Although the SHARP program funded by HITECH cannot point to
large-scale change, its evaluation74 found that the HITECH Act had
led to some industry collaborations and pilot strategies. The program
also served as a potentially good learning source for future public efforts
to fund similar programs that seek to develop new products and op-
erationally useful knowledge in furthering HITECH. Such knowledge
includes how to address issues of market relevance and different ways to
sponsor projects and set expectations.

Conclusions

Despite HITECH’s complexity and tight time lines, HHS did imple-
ment key features of the HITECH Act, including MU payments and
EHR certification requirements, which generally began on schedule, al-
beit with some state Medicaid delays. For the most part, grant programs
also performed as they were charged to do. Although most of the latter’s
performance required a short-term infusion of funds, it is likely that
some of the capacity funded will be sustained selectively in some form.

Some trade-offs and policy choices likely allowed successful imple-
mentation consistent with HITECH’s time frame. In particular, Stage
1 requirements set a relatively low bar in the interest of retaining
provider support and encouraging take-up of EHRs, presumably un-
der the assumption that these factors were critical to making future
progress. Vendors seeking to meet Stage 1 certification requirements did
not necessarily know what would be expected of them in future stages.

In the rollout of the ONC grant programs, some grants went to new,
not necessarily experienced organizations; also, the need to move con-
currently on several fronts complicated effective planning. For example,
the REC evaluation reported that 34% of REC grants went to new
organizations.29 Time lines for workforce programs were so tight that
training programs and student recruitment had to take place simulta-
neously with the development of curriculum and certificate programs,
with little time to consult with those firms and other organizations tar-
geted to employ trainees on personnel needs and desired qualifications.
Meanwhile, the Beacon program found that its ability to garner support
for future investments in health IT was limited because sites were not
necessarily as far along as the legislation had envisioned and could not

678 M. Gold and C. McLaughlin

progress rapidly enough to generate compelling evidence on the value
of health IT in the planned time frame.

How to generate interoperability in a mixed public/private economy
and shared federal/state governing system was an issue at the start of
HITECH and arguably remains the central one today.1 In the absence
of clear authority and consensus, the initial years of HITECH focused
on short-term priorities and experimentation with diverse models for
engaging private providers, states, and health systems. Although mech-
anisms such as the Direct Project were developed to meet short-term
exchange needs, this emphasis on the immediate future delayed hard
decisions about how best to approach standards within a federalized
governing model that relies on the private sector to provide the dom-
inant share of health services, decisions now addressed in the Shared
Nationwide Interoperability Roadmap.49

Lessons for the Future

Although it will be many years before the final lessons from HITECH
become clear, there is much to be learned from the 5 years of experience
with HITECH to guide future work on health IT as well as other policy
initiatives of broad scope.

First, achieving the expansive goals of HITECH required the simul-
taneous development of a complex and interdependent infrastructure
and a wide range of relationships. HITECH programs supported the
digitization and exchange of personal health information that was (1)
accessible across a variety of settings, (2) integrated with workflows, and
(3) interpretable by providers, patients, and other potential users with a
legitimate reason to access the data.

The HITECH experience suggests that the strength of this interde-
pendent system was challenged by the gaps in the available infrastructure
to support exchange across EHRs. Although there is good evidence that,
under HITECH, EHR adoption grew rapidly, along with specific kinds
of one-on-one exchange, the lack of more robust mechanisms for captur-
ing data in different ways and easily sharing those data across a variety
of relevant providers was a critical weakness. This lack of exchange
mechanisms also detracted from the ability of providers, consumers, and
public health agencies to make MU of information spread across diverse
settings.

Assessing HITECH Implementation and Lessons 679

Second, while federal legislation can be a powerful stimulus for
change, its effectiveness in context depends on its ability to accom-
modate private health care markets, as well as diversity in state and local
policies. HITECH greatly expanded the availability and use of health
IT products, but the speed and scope of progress also was limited by
contextual factors that will need to be addressed in the future if health
IT is to contribute to supporting health delivery reform as proponents
of HITECH hoped. In a market economy, government and industry do
not necessarily have the same goals for exchange of information. For
example, consolidated large hospital systems may agree with the pub-
lic interest aspects of broad-based exchange, but their business case for
exchange is much stronger with providers internal to the system than
with those outside of it. Similarly, some vendors appear to view open
exchange as a threat to their business. Yet these large players may be
both best positioned and needed to generate support for robust exchange
models. Further, in a country as large and diverse as the United States,
state and local government policies and conditions vary. For example,
privacy laws, particularly those dealing with sensitive data, differ across
states, creating state-specific barriers to interoperability. Some states
have been more willing than others to use their own funds to invest
in health IT. Finally, health care markets have different characteristics,
and prior experience with health IT varies across the nation so that
some communities have further to go and more barriers to success than
others.

Third, ambitious goals require a long time horizon. Both the realities
of elections and terms of office and HITECH’s connection to ARRA
resulted in legislation with a short-term focus on results, but the reality
is that change on the scale HITECH envisioned takes time.

Given the current health policy agenda, the drive for delivery and
payment reform will be an increasingly important lever for change.
Thus, the future of health IT support nationally is likely to depend on the
ability of the technology to satisfy its users that health IT functionalities
address the interests policymakers and other stakeholders have in using
technology to promote better care, improved outcomes, and reduced
costs. While private sector collaboration is critical, the ability to meet
such expectations is likely to be enhanced by ongoing public investments
in those areas most essential to assuring that health IT supports broad-
based national goals in health care.

680 M. Gold and C. McLaughlin

References

1. Gold MR, McLaughlin CG, Devers KJ, Berenson RA, Bovbjerg
RR. Obtaining providers’ ‘buy-in’ and establishing effective means
of information exchange will be critical to HITECH’s success.
Health Aff. 2012;31(3):514-526.

2. Health IT Dashboard IV. Part D. Global evaluation of HITECH
implementation. http://dashboard.healthit.gov/evaluations/
library.php#HITECH-Global-Evaluations. Accessed October 27,
2015.

3. Blumenthal D. Launching HITECH. N Engl J Med. 2010;362
(5):382-385.

4. Buntin M, Jain SH, Blumenthal D. Health information technol-
ogy: laying the groundwork for national health reform. Health Aff
(Millwood). 2010;219(6):1214-1219.

5. Institute of Medicine. Best care at lower cost: the path to
continuously learning health care in America. http://iom.
nationalacademies.org/Reports/2012/Best-Care-at-Lower-Cost-
The-Path-to-Continuously-Learning-Health-Care-in-America.
aspx. September 6, 2012. Accessed October 27, 2015.

6. Ricciardi L, Mostashari F, Murphy J, Daniel J, Siminerio E. A
national action plan to support consumer engagement via e-health.
Health Aff. 2013;32(2):376-384.

7. Office of the National Coordinator for Health Information Tech-
nology (ONC). Federal Health Information Technology Strate-
gic Plan, 2011-2015. https://www.healthit.gov/sites/default/
files/utility/final-federal-health-it-strategic-plan-0911 . 2014.
Accessed October 27, 2015.

8. Mathematica Policy Research, Urban Institute and Insight Policy
Research. Global Assessment of the HITECH Program Technical
Expert Panel Meeting Summary Notes. Submitted to ONC on
October 22, 2010. (Available from authors on request.)

9. Rein A; HealthIT.gov. The Beacon Community Program:
three pillars of pursuit. Beacon Policy Brief 1.0. http://www.
healthit.gov/sites/default/files/pdf/beacon-brief-061912 . June
3, 2012. Accessed June 18, 2016.

10. Gold M, Hossain M, Mangum A. Consumer engagement in health
IT: distinguishing rhetoric from reality. eGEMS. 2015;3(1). http://
repository.edm-forum.org/cgi/viewcontent.cgi?article=1190&
context=egems. Accessed December 14, 2015.

11. Blumenthal D, Tavenner M. The “meaningful use” regulation for
electronic health records. N Engl J Med. 2010;363:501-504.

Assessing HITECH Implementation and Lessons 681

12. Centers for Medicare and Medicaid Services (CMS). Elec-
tronic health records (EHR) incentive programs. https://
www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncen
tivePrograms/index.html?redirect=/EHRIncentivePrograms/01_
Overview.asp#BOOKMARK2. Updated July 14, 2016. Accessed
June 18, 2016.

13. ONC. Data analytics update: Health IT Policy Committee
meeting. http://healthit.gov/FACAS/sites/faca/files/HITPC_Data_
Analytics_Update_2015-04-07_FINAL . April 7, 2015. Ac-
cessed June 18, 2016.

14. CMS. September 2015 EHR incentive program summary report.
https://www.cms.gov/Regulations-and-Guidance/Legislation/
EHRIncentivePrograms/Downloads/September2015_Summary
Report . Accessed October 15, 2015.

15. ONC. Non-federal acute care hospital electronic health record
adoption. http://dashboard.healthit.gov/quickstats/pages/FIG-
Hospital-EHR-Adoption.php. May 2016. Accessed June 18,
2016.

16. ONC. Office-based health care professional participation in the
CMS EHR incentive programs. http://dashboardhealthit.gov/
quickstats/pages/FIG-Health-Care-Professionals-EHR-Incentive-
Programs.php. December 2014. Accessed June 18, 2016.

17. Heisey-Grove D, Patel V; ONC. Physician motivations for
adoption of electronic health records. ONC Data Brief,
No. 21. https://www.healthit.gov/sites/default/files/oncdatabrief-
physician-ehr-adoption-motivators-2014 . December 2014.
Accessed June 18, 2016.

18. Medicare and Medicaid programs; electronic health record in-
centive program—stage 3. Proposed rule. Federal Regist. 2015;80
(60). http://www.cms.gov/Regulations-and-Guidance/
Legislation/EHRIncentivePrograms/Downloads/Stage3_Rule .
Accessed June 18, 2016.

19. US Department of Health and Human Services. HHS
issues rules to advance electronic health records with
added simplicity and flexibility. http://www.hhs.gov/about/news/
2015/10/06/hhs-issues-rules-advance-electronic-health-records-
added-simplicity-and-flexibility.html. October 6, 2015. Accessed
October 28, 2015.

20. Charles D, Gabriel M, Searcy T; ONC. Adoption of electronic
health record systems among U.S. non-federal acute care hospitals:
2008-2014. ONC Data Brief, No. 23. https://www.
healthit.gov/sites/default/files/data-brief/2014HospitalAdoption
DataBrief . April 2015. Accessed June 18, 2016.

682 M. Gold and C. McLaughlin

21. Heisey-Grove D, Patel V; ONC. Any, certified, and basic:
quantifying physician EHR adoption through 2014. ONC
Data Brief, No. 28. https://www.healthit.gov/sites/default/files/
briefs/oncdatabrief28_certified_vs_basic . September 2015.
Accessed June 18, 2016.

22. Mennemeyer ST, Menachemi N, Rahurkar S, Ford EW.
Impact of the HITECH Act on physicians’ adoption of
electronic health records. J Am Med Inform Assoc. 2016;23(2):375-
379.

23. Walker D, Mora A, Demosthenidy MM, Menachemi N, Diana ML.
Meaningful use of EHRs among hospitals ineligible for incentives
lags behind that of other hospitals, 2009-13. Health Aff (Millwood).
2016;35(3):495-501.

24. Adler-Milstein J, Jha A. The impact of HITECH on hos-
pital EHR adoption. Paper presented at: AcademyHealth’s
2015 Annual Research Meeting; June 16, 2015; Minneapolis,
MN. https://academyhealth.confex.com/academyhealth/2015arm/
meetingapp.cgi/Paper/3746. Accessed June 18, 2016.

25. Dranove D, Garthwaite C, Li B, Ody C. Investment subsidies and
the adoption of electronic medical records in hospitals. J Health
Econ. 2015;44:309-319.

26. Adler-Milstein J, DesRoches CM, Kralovec P, et al. Electronic
health record adoption in U.S. hospitals: progress continues but
challenges persist. Health Aff (Millwood). 2015;34(12):2174-2180.

27. Dougherty M, Williams M, Millenson M, Harvell J; US Depart-
ment of Health and Human Services. EHR payment incentives
for providers ineligible for payment incentives and other funding
study. http://aspe.hhs.gov/daltcp/reports/2013/EHRPI . June
2013. Accessed October 28, 2015.

28. ONC. Regional Extension Centers (REC) priority primary care
providers meaningful use milestone. http://dashboard.healthit.
gov/quickstats/pages/FIG-REC-Program-Priority-Primary-Care-
Provider-Meaningful-Use-Milestone.php. February 2015.
Accessed October 28, 2015.

29. American Institutes for Research. Evaluation of the Regional
Extension Center program: final report. https://www.healthit.
gov/sites/default/files/Evaluation_of_the_Regional_Extension_
Center_Program_Final_Report_4_4_16 . April 2016.
Accessed May 5, 2016.

30. Government Accountability Office. Electronic health records:
number and characteristics of providers awarded Medicare in-
centive payments for 2011-2012. http://www.gao.gov/assets/
660/658534 . October 2013. Accessed October 28, 2015.

Assessing HITECH Implementation and Lessons 683

31. Gold M, Hossain M, Charles DR, Furukawa MF. Evolving vendor
market for HITECH-certified ambulatory EHR products. Am J
Manag Care. 2013;19(10 Spec No):SP353-361.

32. US Department of Justice. Herfindahl-Hirschman Index. https://
www.justice.gov/atr/herfindahl-hirschman-index. Updated July
29, 2015. Accessed June 18, 2016.

33. Blavin F, Ramos C, Shah A, Devers K; US Department
of Health and Human Services. Final report: lessons from
the literature on electronic health record implementation.
http://www.healthit.gov/sites/default/files/hit_lessons_learned_
lit_review_final_08-01-2013 . August 2013. Accessed
October 28, 2015.

34. American Medical Association. AMA provides blueprint
to improve the meaningful use program. http://www.ama-
assn.org/ama/pub/news/news/2014/2014-10-14-ama-blueprint-
improve-Meaningful-use.page. October 14, 2014. Accessed June
18, 2016.

35. Gabriel MH, Jones EB, Samy L, King J. Progress and challenges:
implementation and use of health information technology among
critical-access hospitals. Health Aff. 2014;33(7):1262-1270.

36. NORC at the University of Chicago. Final report: evaluation
of the Information Technology Professionals in Health Care
(“Workforce”) Program—summative report. http://www.healthit.
gov/sites/default/files/workforceevaluationsummativereport .
March 2014. Accessed October 28, 2015.

37. Mathematica Policy Research. Monitoring national implemen-
tation of HITECH: status and key activity quarterly sum-
mary. https://www.healthit.gov/sites/default/files/qmr_oct-
dec2011 . October-December 2011. Accessed October 28,
2015.

38. Swain M, Charles D, Patel V, Searcy T; ONC. Health information
exchange among U.S. non-federal acute care hospitals: 2008-2014.
ONC Data Brief, No. 24. https://www.healthit.gov/sites/
default/files/data-brief/ONC_DataBrief24_HIE_Final . April
2015. Accessed June 19, 2016.

39. Gold M, Hossain M; Mathematica Policy Research. How early
adopting hospital systems view the business case for health infor-
mation exchange. Working Paper #34. http://www.mathematica-
mpr.com/our-publications-and-findings/publications/how-early-
adopting-hospital-systems-view-the-business-case-for-health-
information-exchange. October 2014. Accessed October 28,
2015.

40. Furukawa MF, King J, Patel V, Hsaio C, Adler-Milstein J, Jha AK.
Despite substantial progress in EHR adoption, health information

684 M. Gold and C. McLaughlin

exchange and patient engagement remain low in office settings.
Health Aff. 2014;33(9):1672-1679.

41. Heisey-Grove D, Patel V, Searcy T; ONC. Physician electronic
exchange of patient health information, 2014. ONC Data
Brief, No. 31. http://dashboard.healthit.gov/evaluations/data-
briefs/physician-electronic-exchange-patient-health-information.
php. October 2015. Accessed October 28, 2015.

42. Swain M, Patel V; ONC. Health information exchange among
clinical laboratories. ONC Data Brief, No. 14. https://www.
healthit.gov/sites/default/files/onc-data-brief-14-
testresultexchange_databrief . February 2014. Accessed
October 28, 2015.

43. HealthIT.gov. Direct Project. http://www.healthit.gov/policy-
researchers-implementers/direct-project. Updated July 15, 2014.
Accessed June 19, 2016.

44. Mathematica Policy Research. Monitoring national implemen-
tation of HITECH: status and key activity quarterly sum-
mary. https://www.healthit.gov/sites/default/files/mni_hitech_
january-march_2011 . January-March 2011. Accessed October
28, 2015.

45. Terry K. ONC releases guidelines for direct clinical messaging.
InformationWeek. July 24, 2012. http://www.informationweek.
com/regulations/onc-releases-guidelines-for-direct-clinical-
messaging/d/d-id/1105476. Accessed June 19, 2016.

46. Sequoia Project. eHealth Exchange participants. http://
healthewayinc.org/ehealth-exchange/participants/. Accessed Oc-
tober 28, 2015.

47. NORC at the University of Chicago. Final report: evalu-
ation of the State HIE Cooperative Agreement Program.
https://www.healthit.gov/sites/default/files/reports/finalsummati-
vereportmarch_2016 . March 2016. Accessed March 20, 2016.

48. ONC. Federal Health IT Strategic Plan, 2015-2020. https://
www.healthit.gov/sites/default/files/9-5-federalhealthitstrat-
planfinal_0 . n.d. Accessed October 28, 2015.

49. ONC. Connecting health and care for the nation: a Shared
Nationwide Interoperability Roadmap. https://www.healthit.
gov/sites/default/files/hie-interoperability/nationwide-
interoperability-roadmap-final-version-1.0 . n.d. Accessed
October 28, 2015.

50. Health IT Policy Committee. Letter on Shared Nationwide Inter-
operability Roadmap. http://www.healthit.gov/sites/faca/files/
hitpc_roadmap_transmittal_letter_2015 . April 29, 2015. Ac-
cessed October 28, 2015.

Assessing HITECH Implementation and Lessons 685

51. ONC. Report to Congress: report on health information
blocking. https://www.healthit.gov/sites/default/files/reports/info_
blocking_040915 . April 2015. Accessed October 28, 2015.

52. Health IT Policy Committee. Data update. http://www.
healthit.gov/FACAS/sites/faca/files/HITPC_Data_Update_
Presentation_Final_2015-09-09 . September 9, 2015.
Accessed October 28, 2015.

53. Gold M. Employing health information technology in the real
world to transform delivery. Am J Manag Care. 2013;19(10 Spec
No):SP377-381.

54. JASON; Agency for Healthcare Research and Quality. A
robust health data infrastructure. http://healthit.gov/sites/
default/files/ptp13-700hhs_white . April 2014. Accessed Oc-
tober 28, 2015.

55. NORC at the University of Chicago. Final report: evaluation
of the Beacon Community Cooperative Agreement Pro-
gram. https://www.healthit.gov/sites/default/files/norc_beacon_
evaluation_final_report_final . November 2015. Accessed Jan-
uary 2, 2015

56. CMS. The Center for Medicare and Medicaid Innovation:
Report to Congress. https://innovation.cms.gov/Files/reports/
RTC-12-2014 . December 2014. Accessed June 22, 2016.

57. Burwell SM. Setting value-based payment goals—HHS efforts
to improve U.S. Health care. N Engl J Med. 2015;372(10):897–
899.

58. Medicare Access and CHIP Reauthorization Act of 2015,
Pub L No. 114-10 (2015). https://www.congress.gov/bill/114th-
congress/house-bill/2. Accessed June 19, 2016.

59. Adler-Milstein J, Jha A. The use of health IT to support ac-
countable care organizations. Paper presented at: AcademyHealth’s
2015 Annual Research Meeting; June 14, 2015; Minneapolis,
MN. https://academyhealth.confex.com/academyhealth/2015arm/
meetingapp.cgi/Paper/4308. Accessed June 18, 2016.

60. HealthIT.gov. Consumer Workgroup. https://www.healthit.gov/
FACAS/health-it-policy-committee/hitpc-workgroups/consumer-
workgroup. Updated March 10, 2015. Accessed October 29,
2015.

61. Charles D, Gabriel M, Henry J; ONC. Electronic capa-
bilities for patient engagement among U.S. non-federal
acute care hospitals: 2012-2014. ONC Data Brief, No. 29.
http://dashboard.healthit.gov/evaluations/data-briefs/hospitals-
patient-engagement-electronic-capabilities.php. October 2015.
Accessed June 19, 2016.

686 M. Gold and C. McLaughlin

62. Daniel J, Deering MJ, Murray M; ONC. Issue brief: using health IT
to put the person at the center of their health and
care by 2020. https://www.healthit.gov/sites/default/files/person_
at_thecenterissuebrief . January 10, 2014. Accessed June 19,
2016.

63. Swain M, Patel V; ONC. Patient access to test re-
sults among clinical laboratories. ONC Data Brief, No.
13. https://www.healthit.gov/sites/default/files/onc-data-brief-13-
labsurveydatabrief . February 2014. Accessed June 19, 2016.

64. Devers KJ, Blavin F, Cafarella-Lallemand N, Shah A, Napoles
A. State and local market barriers and spur to meaningful use of
electronic health records: insights from local communities in Ap-
pendix I of The Global Assessment of HITECH—Final Report by
McLaughlin C, Gold M, Lammers E, Hossain M, Barna M, Devers
K and others. Submitted to the Office of the National Coordinator
for Health Information Technology, October 30, 2015. In press.

65. Agha L. The effects of health information technology on the costs
and quality of medical care. J Health Econ. 2014;34:19-30.

66. McCullough JS, Casey M, Moscovice I, Prasad S. The effect of
health information technology on quality in U.S. hospitals. Health
Aff. 2010;29(4):647-654.

67. McCullough JS, Parente S, Town R. Health Information Technology
and Patient Outcomes: The Role of Organizational and Informational
Complementarities. Cambridge, MA: National Bureau of Economic
Research; 2013.

68. Miller AR, Tucker CE. Can health care information technology
save babies? J Polit Econ. 2011;119(2):289-324.

69. Rahurkar S, Vest JR, Menachemi N. Despite the spread of health
information exchange, there is little evidence of its impact on cost,
use, and quality of care. Health Aff. 2015;34(3):477-483.

70. Lammers E, McLaughlin C, Barna M. Physician EHR adoption and
potentially preventable hospital admissions among Medicare ben-
eficiaries: panel data evidence from 2010-2013. Paper presented
at the 2016 AcademyHealth Annual Research Meeting; June 28,
2016; Boston, MA.

71. Wu L; ONC. Issue brief: health IT for public health reporting and
information systems. https://www.healthit.gov/sites/default/
files/phissuebrief04-24-14 . April 29, 2014. Accessed June
19, 2016.

72. Heisey-Grove D, Chaput D, Daniel J; ONC. Hospital reporting on
meaningful use public health measures in 2014. ONC Data Brief,
No. 22. https://www.healthit.gov/sites/default/files/databrief22_
hospitalreporting . March 2015. Accessed June 19, 2016.

Assessing HITECH Implementation and Lessons 687

73. Patient-Centered Outcomes Research Institute. PCORI funding
announcement: improving infrastructure for conducting patient-
centered outcomes research. The National Patient-Centered Clini-
cal Research Network (PCORnet): clinical data research networks
(CDRNs)—Phase II. http://www.pcori.org/sites/default/files/
PCORI-PFA-CDRN . December 22, 2014. Accessed June 19,
2016.

74. NORC at the University of Chicago. Final report: assessing
the SHARP experience. http://www.healthit.gov/sites/default/
files/sharp_final_report . July 2014. Accessed June 19, 2016.

Funding/Support: This article was developed under Contract
HHSP23320095642WC/HHSP23337009T from the Office of the National
Coordinator for Health Information Technology (ONC) to Mathematica Policy
Research. The opinions expressed in this article reflect only those of the authors
and not necessarily those of the organizations for whom the authors work or the
funding agency.

Conflict of Interest Disclosures: The authors completed and submitted the ICMJE
Form for Disclosure of Potential Conflicts of Interest. No disclosures were
reported.

Acknowledgments: Mynti Hossain and Eric Lammers at Mathematica and Kelly
Devers and Fred Blavin at the Urban Institute were key staff on the overall
project, which was directed by Catherine McLaughlin. We benefited from the
hard work of ONC evaluators at NORC at the University of Chicago and at the
American Institutes for Research. At ONC, we benefited from the guidance of
numerous project officers over the course of the work, including Dustin Charles,
Meghan Gabriel, Michael Furukawa, Matthew Swain, and Yael Harris.

Address correspondence to: Marsha Gold, Mathematica Policy Research, 1100
1st St NE, 12th Fl, Washington, DC 20002 (email: MarshaRGold@
gmail.com).

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Economic and
Business Dimensions
Electronic Health
Records and
Patient Safety
Examining the effects of electronic health records
on the safety of patients in medical facilities.

U
. S . H E A L T H C A R E H A S made
huge investments in health
information technologies
(IT). The U.S. Health Infor-
mation Technology for Eco-

nomic and Clinical Health (HITECH)
Act of 2009 earmarked more than
$20 billion to foster electronic health
records (EHRs) at U.S. hospitals and
other medical facilities, and facilities
have spent billions of their own to digi-
tize patient records and clinical work-
flows. What benefits have accrued?
Have EHRs lowered the cost and im-
proved the quality of healthcare? In
particular, what has been the effect of
EHRs on patient safety?

There is some evidence that EHRs
reduce costs over the long term and
under the right conditions.2,a But evi-
dence is scant on the effect of EHRs
on patient safety. An Institute of Medi-
cine (IOM) 2012 study, Health IT and

a EMR detractors have asserted EMR implemen-
tations also impose high cost to hospitals and
physicians—both direct financial costs of imple-
mentation and maintenance and for the medi-
cal providers, the indirect costs of increased doc-
umentation imposed by EMRs. For a polemical
example of such an argument, see Charles Krau-
thammer, “Why Doctors Quit.” The Washington
Post (May 28, 2015); http://wapo.st/1FdVEX4.

DOI:10.1145/2822515 Muhammad Zia Hydari, Rahul Telang, and William M. Marella

I
M

A
G

E
F

R
O

M
S

H
U

T
T

E
R

S
T

O
C

K
.C

O
M

http://dx.doi.org/10.1145/2822515

N O V E M B E R 2 0 1 5 | V O L . 5 8 | N O . 1 1 | C O M M U N I C AT I O N S O F T H E A C M 31

viewpoints

V
viewpoints

Patient Safety, concluded, “current
literature is inconclusive about the
overall impact of health IT on patient
safety.” This lack of evidence prompt-
ed an econometric study of patient
safety at Pennsylvania (PA) hospitals.
Patient safety improved for Pennsyl-
vania hospitals that adopted EHRs: a
27% decline in overall patient safety
events and a 30% decline in medica-
tion errors.b

What Is Patient Safety?
Patient safety can be described as “free-
dom, as far as possible, from harm, or
risk of harm, caused by medical man-
agement (as opposed to harm caused
by the natural course of the patient’s
original illness or condition).”4 A pa-
tient safety event (PSE) occurs if a pa-
tient is harmed or unnecessarily placed
at risk of harm. Every year PSEs affect
hundreds of thousands of patients in
the U.S. and cost billions of dollars.

Medical errors and the harm they
cause have been seen as unavoidable
side effects of modern medicine or the
result of incompetence. Lucian Leape
says, “many errors are preventable and
many are evidence of system flaws not
character flaws.”7 The patient safety
movement was brought to the medical
mainstream by a report of the Institute
of Medicine To Err is Human,5 with the
goal to eliminate preventable patient
harm through improved systems and
find solutions to previously “unpre-
ventable” errors.13

It is difficult to distinguish between
known and acceptable risks and risks
due to medical mismanagement. Con-
cerns for patient privacy add to this
problem. Finally, medical providers see
PSEs on their watch as stains on their
professional competence and may be
hesitant to report them. Measuring
PSEs is difficult. A popular approach is
to use algorithms from the Agency for
Healthcare and Research and Quality
(AHRQ) to infer patient safety indica-
tors (PSIs) from billing data. But these
are viewed skeptically by the medical
community. Peter Pronovost, a promi-
nent Johns Hopkins physician, called
them, “truly, near worthless,” during a
U.S. Senate hearing on patient safety.12

b The study is described in detail in a longer
paper available on SSRN: http://ssrn.com/ab-
stract=2503702

AHRQ algorithms for PSIs can generate
false positives if medical conditions
deemed markers for PSIs were pres-
ent on admission, if there were coding
errors, or if the coding lacks specific-
ity.9 Coding is notoriously inconsistent
across hospitals, or even within hospi-
tals. Global trigger tools (GTTs), a set of
defined rules applied in retrospect to
identify trigger events that help trained
analysts identify PSEs, are also viewed
with skepticism. Their large random
sample makes them a superior mea-
sure of PSEs, but they are difficult to
use due to high costs in their manual
review process. Of course, PSEs can be
measured through voluntary or man-
datory reporting, but this has its own
problems related to underreporting.

The medical community has experi-
mented with many interventions to re-
duce patient safety events: harm reduc-
tion initiatives, training programs, and
checklist use programs (to reduce sur-
gical errors). The latter provides a good
example. The World Health Organiza-
tion (WHO) asserts, “in medicine, as
in aviation, checklists can help ensure
consistency and completeness in car-
rying out complex tasks,” has encour-
aged surgical checklists, and has been
working on other checklists for medical
care.14 However, the impact of these in-
terventions is not always clear. A study
published in the New England of Journal
of Medicine (NEJM) found no signifi-
cant reductions in PSEs in a random
sample of North Carolina hospitals,
even though these hospitals had im-
plemented patient safety initiatives.6
Another NEJM published study found
no relationship between reduced op-
erative mortality and surgical safety
checklists, although prior studies had
found beneficial effects.11 Showing

It is difficult
to distinguish
between known and
acceptable risks and
risks due to medical
mismanagement.

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beneficial impact of CPOE on four post-
operative PSIs that may be amenable
to decision support.3 These two studies
have results directionally similar to ours.
EHR adoption and concomitant work
flow changes can lead to patient harm
but those problems do not challenge
the potential value of EHRs in reduc-
ing PSEs. EHRs should not contribute
to medical errors, but on average, EHRs
seem to be improving patient safety.

c Our hospital EHR adoption data was sourced
from the Healthcare Information and Man-
agement Systems Society (HIMSS). We define
advanced EMRs as CPOE or PD, following
Dranove et al.2

References
1. Bates, D.W. and Gawande, A.A. Improving safety

with information technology. New England Journal of
Medicine 348, 25 (2003), 2526–2534.

2. Dranove, D., Forman, C., Goldfarb, A. and Greenstein, S.
The trillion dollar conundrum: Complementarities and
health information technology. American Economic
Journal: Economic Policy (2014); http://bit.ly/1itnxao.

3. Freedman, S., Lin, H., and Prince, J. Information
technology and patient health: An expanded analysis
of outcomes, populations, and mechanisms. (June 3,
2014) SSRN Scholarly Paper, http://bit.ly/1OViedS.

4. Great Britain House of Commons Health Committee.
Patient Safety: Sixth Report of Session 2008-09, Vol. 1:
Report, Together with Formal Minutes. The Stationery
Office, 2009; http://bit.ly/1OtSY09.

5. Institute of Medicine. To Err Is Human: Building a
Safer Health System. 1 edition. National Academies
Press, Washington, D.C., 2000.

6. Landrigan, C.P. et al. Temporal trends in rates of
patient harm resulting from medical care. New
England Journal of Medicine 363, 22 (Nov. 25, 2010),
2124–2134; doi:10.1056/NEJMsa1004404.

7. Leape, L.L. Error in medicine. JAMA 272, 23 (Dec. 21, 1994),
1851–1857; doi:10.1001/jama.1994.03520230061039.

8. Parente, S.T. and McCullough. J.S. Health information
technology and patient safety: Evidence from panel
data. Health Affairs 28, 2 (Mar. 1, 2009), 357–360;
doi:10.1377/hlthaff.28.2.357.

9. Rosen et al. Validating the patient safety indicators
in the veterans health administration. Medical
Care 50, 1 (Jan. 2012), 74–85; doi: 10.1097/
MLR.0b013e3182293edf.

10. Spear, S.J. and Schmidhofer, M. Ambiguity and
workarounds as contributors to medical error. Annals
of Internal Medicine 142, 8 (Aug. 2005), 627–630.

11. Urbach, D.R. et al. Introduction of surgical safety
checklists in Ontario, Canada. New England Journal
of Medicine 370, 11 (Mar. 13, 2014), 1029–1038;
doi:10.1056/NEJMsa1308261.

12. U.S. Senate Subcommittee on Primary Health and
Aging. More than 1,000 preventable deaths a day is
too many: The need to improve patient safety; http://1.
usa.gov/1JXulDM.

13. Wachter, R. Understanding Patient Safety. Second
Edition. McGraw-Hill Professional, New York, 2012.

14. World Health Organization. The checklist effect; http://
bit.ly/1KcLVas.

Muhammad Zia Hydari (zia@cmu.edu) is Assistant
Professor of Business Administration at the University of
Pittsburgh, PA.

Rahul Telang (rtelang@andrew.cmu.edu) is a professor
of Information Systems and the Ph.D. program chair at
Heinz College, Carnegie Mellon University, Pittsburgh, PA.

William M. Marella (wmarella@ecri.org) is the MBA
program director at Pennsylvania Patient Safety Authority,
Harrisburg, PA.

This research was supported by the Singapore National
Research Foundation under its International Research
Centre @ Singapore Funding Initiative and administered
by the IDM Programme Office.

clear benefits to patient safety from in-
terventions has been difficult.

EHRs and Patient Safety
Our econometric study of EHRs and pa-
tient safety attempted to get around the
measurement difficulties by using data
from a mandatory patient safety report-
ing system. The Pennsylvania legisla-
ture mandated all PA hospitals to re-
port patient safety events to the Patient
Safety Authority, a PA state agency cre-
ated by the “Medical Care Availability
and Reduction of Error (MCARE) Act.”
MCARE encourages more accurate re-
porting by guaranteeing that PSE data
is confidential, reducing provider and
hospital concerns about malpractice
lawsuits or loss of reputation. Providers
and hospitals are less concerned about
malpractice lawsuits or loss of reputa-
tion. Such confidentiality might seem
unfair to consumers, but providers and
hospitals are unlikely to disclose PSE
otherwise and the data creates opportu-
nities for learning and quality improve-
ment that can benefit consumers and
that otherwise might not occur.

Electronic health records are health-
care applications that digitize patient
records and clinical workflows. EHRs
might consist of a Clinical Data Reposito-
ry (CDR) that stores patient data, a Clini-
cal Decision Support System (CDSS) that
assists providers by providing reference
information and suggestions for care,
a Computerized Provider Order Entry
(CPOE) that enable providers to electron-
ically place orders, and a Physician Docu-
mentation (PD) system that consolidates
clinical notes across hospital depart-
ments. Errors in modern medicine occur
because of work complexity, knowledge
intensiveness, and the variety and volatil-
ity of circumstances.10 Tools that reduce
these problems might reduce medi-
cal errors. Well-designed EHRs might
improve patient safety by improving
communications, making knowledge
accessible, providing decision support,
requiring key pieces of information for
correct treatment, assisting with calcula-
tions, performing real-time checks, and
assisting with monitoring.1

To give an example, an elderly pa-
tient with multiple chronic diseases
and taking several medications might
benefit from a well-designed EHR (for
example, with a reconciled medication
list for the patient) when treated for new

medical conditions. The EHR can pro-
vide a physician with real-time access to
the patient’s data, notes from previous
encounters, drug-drug interaction alerts
and drug-disease interaction alerts.
These might influence the physician
when ordering new medications, or the
hospital pharmacist to infer the correct
route of administration when follow-
ing physician notes to fill the prescrip-
tion. Hospitals with well-designed EHRs
might improve safety for such patients.

One can imagine an ideal empiri-
cal test for whether EHRs affect PSEs.
It would involve a randomized control
trial (RCT) in which a randomly chosen
subset of hospitals would adopt and use
EHRs while those in a control group
would not, so systematic differences in
PSEs might be attributed to adopting or
not adopting EHRs. Because such an ex-
periment is completely unfeasible and
too costly to perform, we adopted the
next best approach using observational
data. Given that adoption of EHRs in
U.S. hospitals occurred over time, we
exploited within-hospital variation and
controlled for time trends to estimate
the effect of EHR on PSEs. This differ-
ence-in-difference research approach
has been successfully employed to esti-
mate the impact of EHRs on PSEs.

In our study, approximately 30% of
the PA hospitals had adopted advanced
EHRs in 2005, with adoption grow-
ing to approximately 70% by 2012.c We
found hospitals that adopted EHRs ex-
perienced a 27% decline in overall PSEs,
with a 30% decline in medication PSEs.
We think these results are significant,
and they have been seen in other stud-
ies. Parente and McCullogh found a
small beneficial effect of EHRs on three
specific PSIs (infections, postoperative
hemorrhage, and postoperative pulmo-
nary embolism).8 Freedman et al. found

The medical
community has
experimented with
many interventions
to reduce patient
safety events.

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