I have uploaded three documents for this Database Project. The last two uploaded documents are PowerPoint lecture and other information that maybe needed for the project. 

Reach out to me if any questions thanks 

Department of Health Informatics
Health Information Management Program
BINF 5520 Health Analytics
Creating A Diabetes Tracking Relational Database
Using Microsoft Access

Fundamentals of Creating A
Clinical Tracking Database
Working With Database “Objects”
Tables
Forms
Queries
Reports
Creating a Database to Track Patients With Diabetes
Review of Database Fundamentals
Questions and Answers

How This Presentation Is Organized
Step Number Will Always Be At Top
Command Orientation in Red on Left Side
Screen Shot In Middle
Arrows will focus your attention.

The Four Objects of Microsoft Access
TABLES: The “Containers” That Hold The Data. We must DESIGN these tables before we can do anything, because they hold the data !
FORMS: The Forms allow us to display information to users easily.
QUERIES: The Queries allow us to select data based on specific criteria.
REPORTS: The Reports allow us to output data, either via printer or via a file, such as files that are in a PDF or XLS format.

The Four Objects of Microsoft Access
TABLES
QUERIES
REPORTS
FORMS
DATABASE

The Five Steps of Creating A Relational Database
1. Create the Tables
2. Define The Database Relationship(s)
3. Create The MPI and Encounter Forms
4. Combine the MPI and Encounter Forms Into One Form
5. Start Using The Database !

1. Create the Tables
Master Patient Index (MPI)
Field Name Field Type Field Length
PtId AutoNumber Numeric
PtLast ShortText 30
PtFirst ShortText 30
PtDOB Date MM/DD/YYYY
MRNumber ShortText 12
PtSex ShortText 1
PtRace ShortText 1
And other fields….
Encounters
Field Name Field Type Field Length
EncounterID AutoNumber Numeric
PtId Number Numeric
DateOfService Date MMDDYYYY
Provider ShortText 30
A1C Numeric Decimal,0
BP-Systolic Numeric Decimal,0
BP-Diastolic Numeric Decimal,0
Cholesterol Numeric Decimal,0
Retinopathy Yes/No Yes/No
Neuropathy Yes/No Yes/No
And other fields….

2. Define The Database Relationship(s)
Master Patient Index (MPI)
Field Name Field Type Field Length
PtId AutoNumber Numeric
PtLast ShortText 30
PtFirst ShortText 30
PtDOB Date MM/DD/YYYY
MRNumber ShortText 12
PtSex ShortText 1
PtRace ShortText 1
And other fields….
Encounters
Field Name Field Type Field Length
EncounterID AutoNumber Numeric
PtId Number Numeric
DateOfService Date MMDDYYYY
Provider ShortText 30
A1C Numeric Decimal,0
BP-Systolic Numeric Decimal,0
BP-Diastolic Numeric Decimal,0
Cholesterol Numeric Decimal,0
Retinopathy Yes/No Yes/No
Neuropathy Yes/No Yes/No
And other fields….

3. Create The MPI and Encounter Forms

4. Graft the MPI and Encounter Forms Together

5. Start Using The Database !

Step 1

Step 2
Create / Table Design

Step 3
Create / Table Design

Step 4
Create / Table Design

Step 5
Create / Table Design

Step 6
Create / Table Design

Step 7
Create / Table Design

Step 8
Home

Step 9
Create / Table Design

Step 10
Create / Table Design

Step 11
Create / Table Design

Step 12
Create / Table Design

Step 13
Create / Table Design

Step 14
Create / Table Design

Step 15
Home

Step 16
Database Tools / Relationships

Step 17
Database Tools / Relationships

Step 18
Database Tools / Relationships

Step 19
Database Tools / Relationships

Step 20
Database Tools / Relationships

Step 21
Database Tools / Relationships

Step 22
Database Tools / Relationships

Step 23
Database Tools / Relationships

Step 24
Create / Table Design

Step 25
Create / Table Design

Step 26
Create / Table Design

Step 27
Database Tools / Relationships

Step 28
Home

Step 29
Home

Step 30
Home / Right Click on Encounter / Left Click on Design View

Step 31
Move to Provider Field and go to Tab at Bottom called Lookup

Step 32
In Tab at Bottom called Lookup, Select Combo Box

Step 33
In Row Source option, select lkpProvider Table developed earlier.

Step 34
We now save the table by selecting Yes.

Step 35
We will now see the two tables and the relationship between the tables.

Step 36
Design / Relationships / Save / Yes

Step 37
We now see all three tables: MPI, Encounter, and lkpProvider

Step 38

Create / Form Wizard

Step 39

Create / Form Wizard

Step 40

Create / Form Wizard

Step 41

Create / Form Wizard

Step 42

Create / Form Wizard

Step 43

Home / Right Click on Form MPI, Left Click on Design View

Step 44

Highlight the four fields at the bottom left side of the screen and move to upper right.

Step 45

Highlight the four fields at the bottom left side of the screen and move to upper right.

Step 46

Highlight the four fields at the bottom left side of the screen and move to upper right.

Step 47

Close the Form MPI and Left Click Yes to save the changes to the design of the form.

Step 48

Highlight the four fields at the bottom left side of the screen and move to upper right.

Step 49

Create, Form Wizard, Left Click on Form Encounter, Right Click on Design View

Step 50

Click the double right arrows (>>) to move from Available to Selected and click Next.

Step 51

Click Next to display all fields for this form.

Step 52

Indicate that the form should be organized in a Tabular layout and click Next.

Step 53

Name the form Encounter and click Finish.

Step 54

The form will organize horizontally. You may need to adjust the width of fields to enhance the readability of the form.

Step 55

Close the form and click Yes to save the changes to the design of the form Encounter.

Step 56

On the left side of the screen, left click on the Form MPI and right click on Design View.

Step 57

You will see the large area under the MPI fields. This is where we will move the Encounter form so that we can simultaneously see the Patient and all associated encounters.

Step 58

We then left click on the Form Encounter and we position it under the PtFirst field in the MPI form.

Step 59

We then close Form MPI and we click Yes to save all changes to the design of this form.

Step 60

We can now double click on the MPI form and we will see how the two forms have been joined together.

Step 61

The screen below shows you the results of a database that has been populated. Note that the PtId in the MPI is the same as the PtId in the Encounter.

DesignConsiderations for HIM Related Databases

1. Database design considerations for HIM Professionals are complex and vary widely when considering such factors as database purpose, setting, objectives, targeted audience, and output requirements. Using your past experience in the HIM industry as a guide, please select two topics (a primary and a secondary) that are of professional interest to you that will serve as the basis for developing a database during this semester. Your selection(s) must contain data elements that represent the entire continuum of the patient experience: administrative, clinical, and financial data elements. The instructor will distribute a Database Design Template/Model that will assist you with this process. You can modify the Template/Model to fit your topic.

2. When considering design issues for HIM related databases, we must remain mindful of the interrelationships between data elements. In order for an HIM-related database to be useable, linkages must exist between the tables contained within each database. Within your database design, which fields do you plan to use as the key data elements which will enable the various tables in your database to interact with each other?

3. When considering issues of database design, we are ultimately concerned with the clinical and regulatory needs of the intended audience. Please specifically identify the intended audience for the database that you intend to design in partial fulfillment of the requirements of this course.

The Sample Diabetes Database (in red) on the subsequent page is provided as a guide for this exercise, although your own database design may follow any standard database design convention.

Sample Database Design: Diabetes

Master Patient Index

Field Name Data Type Field Size
PtIdNo AutoNumber Integer
PtLast Text 30
PtFirst Text 30
MRNO Number Double
Gender Text 1
Race Text 10
DOB Date/Time mm/dd/yy;@

Encounter

Field Name Data Type Field Size
EncounterId AutoNumber Integer
PtIdNo Number Integer
MRNO Number Double
DOS Date/Time mm/dd/yy;@
Height Number Double
Weight Number Double
Physician Text 35
Date Onset Date/Time mm/dd/yy;@
Insulin Dependent Text 1
A1CScore Number Double
A1CRating Text 10,@
DietCompliance Text 10,@
Neuropathy Text 10,@
Retinopathy Text 10,@
BMI Number Double
BMIRating Text 10,@

Insurance

Field Name Data Type Field Size
InsuranceId AutoNumber Integer
PtIdNo Number Integer
InsPlanName Text 30
InsPlanNo Text 30

Sample Database Design 1: _________________________

Encounter

Field Name Data Type Field Size
EncounterId AutoNumber Integer
PtIdNo Number Integer

Master Patient Index

Field Name Data Type Field Size
PtIdNo AutoNumber Integer
PtLast Text 30
PtFirst Text 30
MRNO Number Double
Gender Text 1
Race Text 10
DOB Date/Time mm/dd/yy;@

Insurance

Field Name Data Type Field Size
InsuranceId AutoNumber Integer
PtIdNo Number Integer
InsPlanName Text 30
InsPlanNo Text 30

Sample Database Design 2: _____________________________________

Encounter

Field Name Data Type Field Size
EncounterId AutoNumber Integer
PtIdNo Number Integer

Master Patient Index

Field Name Data Type Field Size
PtIdNo AutoNumber Integer
PtLast Text 30
PtFirst Text 30
MRNO Number Double
Gender Text 1
Race Text 10
DOB Date/Time mm/dd/yy;@

Insurance

Field Name Data Type Field Size
InsuranceId AutoNumber Integer
PtIdNo Number Integer
InsPlanName Text 30
InsPlanNo Text 30

EncounterId		PtId	DateOfService	Provider	A1C	BP-Systolic	BP-Diastolic	Cholesterol	Retinopathy	Neuropathy

PtId

PtLast	PtFirst	PtDOB	MRNumber	PtSex	PtRace

PARAMETERS __PtId Value;
SELECT DISTINCTROW *
FROM Encounters AS MPI
WHERE ([__PtId] = PtId);
SELECT DISTINCTROW *
FROM MPI;

Sponsored

by

Center for Cancer Research

National Cancer Institute

Clinical Data Management

Introduction
• Clinical data management (CDM) consists of various

activities

involving the handling of data or information
that is outlined in the protocol to be
collected/analyzed. CDM is a multidisciplinary activity.

• This module will provide an overview of clinical

data

management and introduce the CCR’s clinical
research database. By the end of this module, the
participant will be able to:
• Discuss what constitutes data management activities in clinical

research.

• Describe regulations and guidelines related to data management
practices.

• Describe what a case report form is and how it is developed.

• Discuss the traditional data capture process.
• Describe how protocols are developed in Cancer Central

Clinical

Database (C3D).

Clinical Data Management

• A multi-disciplinary activity that includes:
• Research nurses
• Clinical data managers

• Investigators
• Support personnel

• Biostatisticians

• Database

programmers

• Various activities involving the handling of
information outlined in protocol

Clinical Data Management

Activities

• Data acquisition/collection

• Data abstraction/extraction

• Data processing/coding

• Data analysis

• Data transmission

• Data storage

• Data privacy

• Data QA

Guidelines and Regulations…

• Good Clinical Practice (GCP):
• Trial management; data handling, record

keeping (2.10, 5.5.3 a-d)

• Subject and data confidentiality (2.11; 5.5.3

g)

• Safety reporting (4.11)

• Quality control (4.9.1; 4.9.3; 5.1.3)

• Records and reporting (5.21; 5.22)

• Monitoring (5.5.4)

…Guidelines and Regulations

• 21 CFR Part 11

• Applies to all data (residing at the institutional site and

the sponsor’s site) created in an electronic record that

will be submitted to the FDA

• Scope includes:
• validation of databases

• audit trail for corrections in database

• accounting for legacy systems/databases

• copies of records

• record retention

Case

Report

Forms

What is a Case Report Form

(CRF)?…
• Data-reporting document used in a clinical

study

• Collects study data in a standardized

format:

• According to the protocol

• Complying with regulatory requirements

• Allowing for efficient analysis

…What is a Case Report Form

(CRF)?

• Allows for efficient and complete data

collection, processing, analysis and

reporting

• Facilitates the exchange of data across

projects and organizations especially

through standardization

• Types: Paper, electronic/web interface

• Accompanied by a completion/instruction

manual

CRF Relationship to Protocol

• Protocol determines what data should be

collected on the CRF

• All data must be collected on the CRF if

specified in the protocol

• Data that will not be analyzed should not

appear on the CRF

General Considerations for CRF

Development…

• Collect data with all users in mind

• Collect data required by the regulatory

agencies

• Collect data outlined in the protocol

• Be clear and concise with your data

questions

…General Considerations for

CRF Development

• Avoid duplication

• Request minimal free text responses

• Collect data in a fashion that:
• allows for the most efficient computerization

• similar data to be collected across studies

Elements of a CRF

• The term CRF indicates a single page

• A series of CRF pages makes up a CRF Book

• One CRF book is completed for each subject
enrolled in a study

• Three major parts:
• Header
• Safety related modules
• Efficacy related modules

Header Information

• Key identifying Information

• MUST HAVES
• Study Number

• Site/Center Number

• Subject identification number

Safety Modules

• Keep safety analysis requirements of the protocol
in mind

• Follow the general guidelines for CRF development

• Safety Modules include:
• Demographic information
• Adverse Events
• Medical History/Cancer history (e.g., diagnosis, staging)
• Physical Exam, including Vital Signs
• Concomitant/Concurrent Medications/Measures
• Deaths
• Drop outs/off-study reasons
• Eligibility confirmation

Efficacy Modules

• Considered to be “unique” modules and can be
more difficult to develop

• Protocol dictates the elements required in efficacy
modules

• Define
• Key efficacy endpoints of trial (primary and secondary_
• Additional test to measure efficacy (e.g.: QOL)
• How lesions will be measured (longest diameter, bi-

dimensional, volumetric)
• CR, PR, SD, PD
• Required diagnostics

• Include appropriate baseline measurements
• Repeat same battery of tests

Standard CRFs

• Allows rapid data

exchange

• Removes the need for mapping during data

exchange

• Allows for consistent reporting across protocols,

across projects

• Promotes monitoring and investigator staff

efficiency

• Allows merging of data between

studies

• Provides increased efficiency in processing and

analysis of clinical data

CRF Development Process…

• Begins as soon in the study development
process as possible

• Responsibility for CRF design can vary
between clinical research organizations (e.g.:
CRA, data manager, Research Nurse,
Database Development, Dictionary Coding,
Standards)

• Include all efficacy and safety parameters
specified in the protocol using standard
libraries

…CRF Development

Process

• Collect ONLY data required by the protocol

• Work with protocol visit schedule

• Interdisciplinary review is necessary
• Note:

• each organization has its own process for
review/

sign-off

• Should include relevant members of the project
team involved in conduct, analysis and reporting of
the trial

Properly Designed CRF

• Allows components or ALL of the CRF

pages to be reused across studies

• Saves time

• Saves money

Poorly Designed CRF

• Poorly designed CRFs will result in data

deficiencies including:

• Data not collected as per protocol

• Collecting unnecessary data (i.e.: data not

required to be collected per protocol)

• Impeding data entry process

• Database requiring modifications throughout

study

Electronic

CRFs

• The use of Remote Data

Capture (RDC) is increasing

• In general, the concepts for the design of

electronic CRFs/RDC screens are the

same as covered for paper

• No need to print and distribute paper

CRF Completion

CRF Completion…

• According to GCP Section 4.9.1, the investigator

should ensure the accuracy, completeness,

legibility, and timeliness of the data reported on

the CRFs and in all required reports. This

includes ensuring:
• all sections have been completed, including the

header with identifying items

• all alterations have been properly made

• all adverse events are fully recorded and that for all

serious adverse events, any specific documentation

has been completed

…CRF Completion

• Data is taken from the source documents

(e.g.: medical record) and entered onto the

CRFs by study personnel. This is referred

to as data abstraction.

• Only designated members of the research

staff should be allowed to record and/or

correct data in the CRFs

• Typically this responsibility resides with the

Data Manager/ Research Nurse

Tips: CRF Completion…

1. CRF completion/instruction manual should be

observed to ensure the accuracy,

completeness, legibility, and timeliness of the

data reported to the sponsor

2. Make sure appropriate protocol, investigator

and subject identifying information is included

in the Header (for RDC, may be pre-populated)

3. Ensure data is entered in the correct location or

data field

…Tips: CRF Completion…
4. Use the appropriate units of measurement

(UOM), and be consistent

5. Check to see that data is consistent across
data fields and across CRFs
• E.g.:

• Make sure visit dates match dates on the
laboratory or other procedure reports;

• Make sure the birth date matches the
subject’s age;

6. Use only the abbreviations authorized per

completion/instruction manual

7. Double check your spelling

…Tips: CRF Completion
8. Watch for transcription errors

• E.g.: sodium level should be “135” and entered as

“153”

9. Do not allow entries to run outside the

indicated data field; this important data might

be missed during data processing

10. Use “comments” section to elaborate on any

information, but keep to a minimum

Timeliness of CRF Completion

• Ideally CRFs should be completed as

soon after the subject’s visit as possible

• Ensures that information can be retrieved

or followed-up on while the visit is still

fresh in the healthcare provider’s mind,

and while the subject and/or the

information is still easily accessible

REMEMBER….

• Data cannot be entered onto a CRF if it is not in

the medical record or for some documents, in

the research record

• If the individual completing the CRF, finds

missing or discrepant source data he/she

should:
• Notify the research nurse or health care provider who

then will provide the data

• If applicable, contact outside source (i.e.: outside lab

or doctor’s office)

Common Errors …

• Logical
• date of the second visit is earlier than the first

visit

• Inaccurate information
• source document says one thing, the CRF

says another

• Omissions
• AE is recorded on the CRF but not on the

source document

• Transcription errors
• date errors, 11-2-59 instead of 2-11-59

…Common Errors

• Abbreviations

• unless an approved list of abbreviations is

distributed and utilized, data entry personnel

often misinterpret abbreviations

• Spelling errors

• Illegible entries/”write-overs”

• Writing in margins

Correcting Paper CRF

Entries…
• If corrections are necessary, make the change

as follows:

• Draw one horizontal line through the error;

• Insert the correct data;

• Initial and date the change;

• DO NOT ERASE, SCRIBBLE OUT, OR USE
CORRECTION FLUID OR ANY OTHER MEANS
WHICH COULD OBSCURE THE ORIGINAL
ENTRY

• These procedures ensure a complete “audit
trail” exists for all entries.

01/JAN/2005 05C1234 NIC 12345678

03/JAN/1925 80

x

x

1. Complete each form in black or blue pen to ensure good photocopies.

2. All dates are to be expressed in day/month/year (dy/mth/yr) format. To

avoid ambiguity,months are to be recorded using a three letter

abbreviation (i.e., Jan, Feb, Mar., etc.). Years are to be recorded as four

digits (i.e. 1998).

NCI EN

9/8/05

…Correcting Paper CRF Entries

Electronic Data Collection

Process

• Web-based interface

• Sponsor or site dependent

• Ensures data integrity:
• Controls the ability to delete or alter

previously entered data

• Provides an audit trail for data changes

• Protects the database from being tampered

with

• Ensures data preservation (e.g. automatic

back ups)

Process of Data Transfer to

Sponsor

Traditional (Paper)

Electronic

Traditional Data Transfer…

• CRF Books developed by sponsor and supplied
to the site for completion along with
completion/instruction manual

• Paper CRFs are either 2 or 3 part NCR (No
Carbon Required paper)

• Use a black or blue ballpoint pen for permanency –
and PRESS HARD

• At the time of a monitoring visit, CRFs are
reviewed for adherence to completion
guidelines and verified against source
documents by the Monitor

…Traditional Data Transfer …

• During the monitoring visit, site staff make
required corrections to CRFs

• Verified/corrected CRFs are submitted to

the sponsor, leaving a legible copy of the
CRF at the site

• e.g.: CRA may hand carry completed
CRFs to the sponsor;

• If data is not retrieved at the time of the

monitoring visit, sponsor may want the
CRFs submitted via mail or facsimile

…Traditional Data Transfer

• Sponsor enters the CRF data into a centralized
database (generally done by 2 separate
individuals, called double data entry) and
reviews the data for errors

• If inconsistencies are found, the sponsor
generates data queries (forms may vary slightly
from sponsor to sponsor) and sends to the site

• Site staff investigates these queries and
responds to them either directly on the data
query form or on the CRF. The data correction
is then re-submitted to the sponsor for entry into
their database.

Data Transfer:

Electronic CRF (eCRF)
• Site records data from source documents to the

electronic database or the web interface

• Data periodically electronically transmitted to
Sponsor/CRO or automatically resides in Sponsor
database

• Real-time review of data performed by in house
CRAs

• Less frequent CRA visits

• Electronic queries generated and sent to site

• Database lock

Cancer Central Clinical Database

(C3D)…

• C3D is an integrated clinical trial

information system for the CCR

• System is secure, compliant with

regulatory requirements (21 CRF Part 11 )

• System is friendly and flexible for user

…Cancer Central Clinical

Database (C3D)

• Designed to allow integration with the NCI

extramural divisions and the NIH Clinical Center

CRIS (Clinical Research Information System).

• Currently this is being done with labs drawn at the

Clinical Center.

• Oversight is done by the Control and

Configuration Management Group (CCMG)

whose membership has clinical and IT expertise

C3D Overview…

• Based on commercial software produced by the

Oracle Corporation called Oracle Clinical (OC)

• Allows for Remote Data Capture (RDC) so that

local and remote personnel enter and manage

clinical data over a LAN, intranet, telephone line,

or the Internet

• Data can be electronically transferred to

Sponsors (responsibility of DM IT team)

…C3D Overview

• A template set of master CRFs have been
created to collect the data required by
CCR protocols

• Templates are reused and each study will
only use the eCRFs that are appropriate
and required for that study

• Confidentiality statement signed at time of
training

J-Review

• J-Review is a software product that allows us to

get data out of C3D into a variety of reports

• Numerous template reports have been

developed including:

• Adverse event summary

• Demographics

• Drug administration

• Also allows for customized reports

C3D eCRFs

Resources

• C3D Data Entry

• Manual for the Completion of the
NCI/CCR/C3D Case Report Forms

• Access to J-review is granted once
training occurs.

https://ccrod.cancer.gov/confluence/display/CCRClinicalIT3/Login

https://ccrod.cancer.gov/confluence/display/CCRClinicalIT3/Training+and+Education

https://octrials-rpt.nci.nih.gov/jreviewwww/sample_default.htm

https://octrials-rpt.nci.nih.gov/jreviewwww/sample_default.htm

https://octrials-rpt.nci.nih.gov/jreviewwww/sample_default.htm

https://octrials-rpt.nci.nih.gov/jreviewwww/sample_default.htm

C3D Protocol Build Process…

• OCD determines if a protocol will be

built in C3D

• Currently the following are built:
• All CTEP-sponsored, non-cooperative

group

trials

• All industry-sponsored trials with company

agreement (if not, sponsor will then

provided paper crfs)

• All internal/non-sponsored interventional

trials

… C3D Protocol Build Process…

…
• Clinical Analyst (CA)

receives protocol from

IRB

• CA identifies standard

eCRFs to be used

• CA develops the eCRF

book and identifies if

new eCRFs are needed

• CA meets with research

team to confirm eCRF

book

CR Doc

Forms & Rules

Testing
Protocol

Receiving

Clinical Analyst

Forms & Rules

Building

Initiation

Meeting

Control & Configurations Management Group (CCMG)

$

$ $

Requirement

Specification

Clinical

Programmers

Clinical

Programmers

TeamClinical Analyst

Research

Team

(PI, RN, DM)

Clinical Analyst

Activation

Meeting
Team

Protocol

Protocol

Reqs

Protocol
Reqs

Sign-off

Protocol
Reqs
Sign-off

Build Doc

Protocol
Reqs
Sign-off
Build Doc

QC Doc

Protocol
Reqs
Sign-off
Build Doc
QC Doc
Protocol
Reqs
Sign-off
Build Doc
QC Doc

Rep Doc

Signoff

Team
Protocol
Reqs
Sign-off
Build Doc
QC Doc
Rep Doc

Signoff

Change

Request

Report

Building
Signoff

… C3D Protocol Build Process…

…
• Clinical Programmers

(CP) build protocol

(eCRFs) in C3D

• Research team tests

the build/enters data

• Modifications made

as needed

• Protocol activated in

C3D by CA/CP

• eCRFS available for

data entry

CR Doc
Forms & Rules
Testing
Protocol
Receiving
Clinical Analyst
Forms & Rules
Building
Initiation
Meeting
Control & Configurations Management Group (CCMG)

$

$ $
Requirement
Specification
Clinical
Programmers
Clinical

Programmers
TeamClinical AnalystClinical Analyst

Modification

Activation
Clinical Analyst

Clinical Programmers

Team

Protocol Protocol

Reqs
Protocol
Reqs
Sign-off
Protocol
Reqs
Sign-off
Build Doc
Protocol
Reqs
Sign-off
Build Doc
QC Doc
Protocol
Reqs
Sign-off
Build Doc
QC Doc
Protocol
Reqs
Sign-off
Build Doc
QC Doc
Rep Doc
Signoff
Team
Protocol
Reqs
Sign-off
Build Doc
QC Doc
Rep Doc
Signoff
Change
Request
Report
Building
Signoff

… C3D Protocol Build Process…

…
• If a protocol

amendment requires

changes in C3D (e.g.

eligibility criteria),

CA/CP will develop

new eCRF

• Team will review,

sign-off

• CA/CP will activate

new eCRF Book

CR Doc

Forms & Rules
Testing
Protocol

Amendment

Clinical Analyst

New Forms & Rules

Building

Activation of New Forms

Control & Configurations Management Group (CCMG)

$

$ $

Update

Requirement
Specification
Clinical

Programmers
Clinical

Programmers
TeamClinical Analyst
Activation
Meeting
Team
Protocol Protocol
Reqs
Protocol
Reqs
Sign-off
Protocol
Reqs
Sign-off
Build Doc
Protocol
Reqs
Sign-off
Build Doc
QC Doc
Protocol
Reqs
Sign-off
Build Doc
QC Doc
Protocol
Reqs
Sign-off
Build Doc
QC Doc
Rep Doc
Signoff
Team
Protocol
Reqs
Sign-off
Build Doc
QC Doc
Rep Doc
Signoff
Change
Request
Report
Building
Signoff

Training

• There is specific training required for use

of C3D and I-review.

• See Training Sessions for date, time and

location.

https://ccrod.cancer.gov/confluence/display/CCRClinicalIT3/Training+and+Education

Industry Sponsored Queries

• Sponsor generates questions/queries:

• During/end of a monitoring visit

• After data sent to sponsor and

reviewed/entered in sponsor’s database

• Site corrects CRF:

• During/between monitoring visit

• May need to also sign-off on query form itself

CTEP Sponsored CTMS

Clarification

• These are paper queries generated for
CTEP-sponsored, CTMS-monitored trials

• Sent every Monday by Theradex (contractor
for CTEP)

CTEP Sponsored

CDS

Rejection/Notification

• These are electronic data queries for CTEP-

sponsored, CDS-monitored clinical trials

• CDS submitter receives notice
• For studies in C3D, the notification will be sent to the

CCR IT Programmer who transfers the data to CDU

• CCR staff corrects data in the database and

resubmits

• Process occurs until data is loaded correctly in

CDS

Missing Data at Time of Transfer

• Missing data elements

• Source Document (SD) not supporting CRF

• CRF not supporting SD

• Referred to as:

• Discrepancies

• Queries

• Clarifications

• Identified by:

• Sponsor

• Database

Sponsor Queries

• Sponsor generates:

• During/End of a monitoring visit

• After data sent to sponsor and

reviewed/entered in database

• Site corrects CRF:
• During/between monitoring visit

• May need to sign-off on query

Database Discrepancies

• Failure of entered data to pass a validation

check as applied by a database

• Univariate discrepancy – single data

element errors (e.g., not using provided

pick-list, missing data in a field)

• Multivariate discrepancy – multiple data

element errors (e.g., male patient with +

beta HCG)

Quality Control

According to GCP Section 5.1.3 quality

control should be applied to each stage of

data handling to ensure that all data are

reliable and have been processed correctly.

Assessing the QC/QA Process

• Are staff checking their own work?

• Are staff relying on others to check their work?

• Does the organization have a QA plan for

monitoring protocol adherence and data

collection?

• Are there SOPs related to data management?

• How soon after a visit is a CRF completed?

• Is all data, as defined in the protocol, captured

from the source document to the CRF?

Terminology

• Quality Control

• Quality Assurance

• Quality Improvement

Quality Control (QC)

• Ongoing and concurrent review of subject data

• Typically 100%

• Checking your own work and work of others

• Verify that data collected and abstracted:
• Correctly entered onto CRF

• Able to be found in source document

• Follows regulations and guidelines

• Individual team member level

Quality Assurance (QA)
• Planned, systematic check done at the branch or

organizational level

• Verifies:
• Trial is performed as per the approved plan

• Data generated is accurate

• Identifies problems and trends:
• Retrospective and involves sampling of subjects and

data

• Pulls all the pieces together to gain a picture
(measurement) of compliance

• Ensures staff is compliant with internal and external
regulations/guidelines

QA Activities

• Internal monitoring/audits

• Compile all data components and gain a
measurement of compliance

• Clarification monitoring

• Assess for trends

• Review clarifications responses before they are
submitted to sponsor

• Measure data inconsistencies and trends using
a sampling of the data prior to audits/monitoring
visits

• Summarize QA findings and report to
management

• Identify learning needs

QA Activities for CCR

• The following are examples of QA activities

for the CCR:
• Office of the Clinical Director (OCD)

• Internal monitoring/audits

• Conduct audits per upon request, for PI sponsored

studies
• Clarification monitoring

• Data Management Contractor
• Develop QA tools

• Summarize QA findings and report to management,

education and training

• Identify needs

Quality Improvement (QI)

• Result of QC and QA

• Developing a plan includes:
• Identifying root causes of problems

• Intervening to reduce or eliminate these problems

• Taking steps to correct the process(es)

• Identifying trends and areas for improvement

• Identifying solutions:
• Assess work flow and time management activities

• Develop tools for source documentation

• Assess training needs

• Involve appropriate staff in resolution

• Implementing new/updated solution

QI Activities for CCR

• Team Level:

• Based on QC activities: identifying trends

• Based on audit/monitoring visit results

• OCD CCR Level:

• Based on audit/monitoring visit results

• Guide in implementing processes for making

corrective changes

Responsibilities

• Research Team responsibilities

• Research Nurse responsibilities

• Data Manager responsibilities

Research Team
• Ensure that all source data is documented in the

Medical Record/Research Chart with accuracy,
completeness, and consistency

• Ensure the overall quality of the research data is
verifiable and acceptable for sponsor
submissions, publications, etc.

• Review data discrepancy/clarification resolutions
for accuracy, consistency and timely response

Research Nurse….

• Provide accurate and complete source
documentation

• Develop, implement, and maintain a team QC
plan:
• Establish a schedule of QC activities

• Quality check source documentation, data
abstraction, CRFs completion

• Quality check of database
• Verify function in database

• Develop team quality improvement plan, as
needed

….Research Nurse

• Lead Team QC meeting:

• Provide administrative updates

• Provide patient updates

• Perform QC on data/resolve issues

• Review query/clarification:

• Assign to Data Manager(s), if appropriate, to

investigate and resolve or resolve yourself

• Review and sign off:

• Follow sponsor SOP

Data Manager….

• Abstract data onto CRFs according to what is

found in the source documents (Medical Record

or Research Chart) and CRF Instruction Manual

• Abstract data in a timely fashion, this includes

entry into database

• Code Adverse Events accurately utilizing the

appropriate version of CTCAE, as per protocol

….Data Manager

• Apply quality control checks at each stage

of data handling

• Ensure that data elements abstracted are

complete and accurate

• Contact Research Nurse for missing source data

• Resolve discrepant data – ongoing

• Utilize database report tools to assist with QC

activities

Guiding Principles

• Source documents need to be accurate and
complete

• Data abstraction should occur in real time

• QC/QI is the responsibility of every research
team member

• QC/QI should be completed on all protocol data
for all protocols

• QC/QI should be proactive and ongoing

• Each team member should know and
understand the roles and responsibility of each
team member

Resources

• Guidelines for Good Clinical Practice.

International Conference on Harmonisation

(ICH).
• http://www.ich.org

• FDA, Title 21 CFR Part 11
• http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcf

r/CFRSearch.cfm?CFRPart=11

http://www.ich.org/

http://www.ich.org/

http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?CFRPart=11

http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?CFRPart=11

Evaluation

Please complete the evaluation form and

fax to Elizabeth Ness at 301-496-9020.

For questions, please

contact Elizabeth Ness

301-451-2179

nesse@mail.nih.gov

https://ccrod.cancer.gov/confluence/download/attachments/71041052/CDM_Evaluation

How I do i

t

: A Practical Database Management System to Assist
Clinical Research Teams with Data Collecting, Organization, and
Reporting

Howard Lee, B.S.1, Julius Chapiro, M.D.1, Rüdiger Schernthaner, M.D.1, Rafael Duran, M.D.
1, Zhijun Wang, M.D., Ph.D1, Boris Gorodetski, B.S.1, Jean-François Geschwind, M.D.1, and
MingDe Lin, Ph.D2

Howard Lee: howard.lee1722@gmail.com; Julius Chapiro: j.chapiro@googlemail.com; Rüdiger Schernthaner:
rschern1@jhmi.edu; Rafael Duran: rduran4@jhmi.edu; Zhijun Wang: wangzj301hospital@163.com; Boris Gorodetski:
boris.gorodetski@charite.de; Jean-François Geschwind: jfg@jhmi.edu; MingDe Lin: ming.lin@philips.com
1Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and
Interventional Radiology, The Johns Hopkins Hospital, Sheikh Zayed Tower, Ste 7203, 1800
Orleans St, Baltimore, MD, USA 21287

2U/S Imaging and Interventions (UII), Philips Research North America, 345 Scarborough Road,
Briarcliff Manor, New York 10510

Introduction

With the growing amount of clinical research studies in the field of interventional oncology,

selective patient data is becoming more difficult to store and organize effectively. Existing

hospital EMR (electronic medical record) systems store patient data in the form of reports

and data tables. Our institution’s EMR system placed our researchers in a position where

time consuming methods are needed to search for suitable patients for clinical studies.

Researchers had to manually read through the reports and data tables to filter patients and

gather data. For most studies, spreadsheet programs such as Microsoft Excel® (Microsoft,

Washington, USA) are often used as a data repository similar to a database to record and

organize patient data for research. Once the spreadsheet is populated, it is manually filtered

by set study parameters and then pushed to statistical analysis software for further analysis.

For statistical analysis, columns containing text are translated into binary values (1 or 0) to

be in a format acceptable by statistical analysis software. For example, each tumor entity is

assigned a new column. Patient histological reports are read manually to assign a 1 or 0 to

each tumor entity column, 1 for positive, 0 for negative. Under a tumor entity column,

researchers would write a 1 for all patients with the tumor and a 0 for all patients without the

tumor.

© 2014 AUR. All rights reserved.

Correspondence to: Jean-François Geschwind, jfg@jhmi.edu.

Publisher’s Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our
customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of
the resulting proof before it is published in its final citable form. Please note that during the production process errors may be
discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

HHS Public Access
Author manuscrip

t

Acad Radiol. Author manuscript; available in PMC 2016 April 01.

Published in final edited form as:
Acad Radiol. 2015 April ; 22(4): 527–533. doi:10.1016/j.acra.2014.12.002.

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This method of data storage has limitations in the organization and the quality of the data.

Data input and analysis without a database run a higher risk of incorrect data entry, patient

exclusion, and a higher risk of introducing duplicates. Furthermore, data selection and

calculation is time consuming. An alternative could be the clinical research database that

Meineke et. al. proposed (1). However, it is too unspecific for interventional oncology

research and would need additional optimization, for example, the capability to

automatically calculate various variables such as tumor staging systems and to record

information about multiple treatment sessions.

The purpose of this study was to provide an improved workflow efficient tool through the

use of a clinical research database management system (DBMS) optimized for interventional

oncology clinical research.

Materials and Methods

This was a single-institution prospective study. The study was compliant with the Health

Insurance Portability and Accountability Act (HIPAA) and was waived by the Institutional

Review Board.

Database and Query Interface Design

The presented database management system has two distinct parts, the database server and

client interface, illustrated in Figure 1. The database is run by software (MySQL, Oracle

Corporation, California, USA and phpMyAdmin, The phpMyAdmin Project, California,

USA) on a central computer server within the department (2, 3). Authorized users were

granted access to this password protected and encrypted secured server (HIPAA compliant).

Multiple users concurrently add, edit, and query data remotely through a customized

graphical user interface (GUI) utilizing Microsoft Access® (Microsoft, Washington, USA).

Any data changes are immediately logged for others to see. The database performed

automatic calculations using queries, user-defined search criteria. Queries were saved, rerun,

and exported to spreadsheets. Queries aid in data analysis and increase study productivity

(4). They are powerful tools for filtering and sorting datasets. Figure 2 illustrates the query

interface and an example of request from the

database.

Graphical User Interface Design and Utility

In our research environment, the database GUI was created to facilitate patient data input.

This was done by using custom user-friendly interface forms that contain textboxes and

labels including demographic data, treatment information (e.g. conventional transarterial

chemoembolization (TACE)), tumors types, dates and types of radiological exams, etc. The

GUI is used to view patient data and allows users to add/edit data (Figure 3). The database

interface is not limited to one form. It can have multiple forms, shown as tabs, to assist

grouping various medical data. Figure 4 shows an example of multiple tabs for groups of

related data.

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Automatic Calculations

Automatic calculations may be run between values, such as dates. For example, the database

may calculate the time between baseline imaging, follow-up imaging, treatment dates, pre-

and post-treatment dates, date of diagnosis, and patient’s date of death in relation to a

particular treatment or event (e.g. randomization), essential for survival studies. Using these

queries, the database can also calculate the median overall survival automatically. The

database does also automatically calculate clinical scores such as Child-Pugh score and

Barcelona Clinic Liver Cancer (BCLC) stage as shown in Figure 5 (5). For our purposes, the

Child Pugh score and BCLC were calculated using baseline data before a patient’s first

embolization as is typically done for staging. The illustrated calculators can be revised as

needed. Once patient blood data is available, queries are run to produce a list of all patients

with Child-Pugh scores. Researchers can then quickly retrieve them.

Statistical Output

Another powerful feature of the database is its ability to provide a first tier of statistical

information. Using this GUI, the user defines the search criteria and runs queries to obtain

immediate statistical information about a particular set of parameters. With this feature, the

database can quickly output an accurate summary of patient data such as, for example, how

many patients have colorectal carcinoma and undergo conventional TACE.

Questionnaire Assessment

A questionnaire (15 questions) was designed and distributed to 21 board-certified

interventional radiologists who conduct clinical research at our academic hospital that

include Phase I, II, and III clinical trials, and retrospective studies. The questionnaire

determined how data is controlled in retrospective studies and the likelihood to use the

database. The questionnaire is shown in Table 1. The purpose of the questionnaire was to 1)

illustrate the general scope of where researchers were having problems within Excel and

data organization, such as wasted effort working with duplicate patients and unintentional

failure to include available patients, and 2) to gauge how receptive they would be to a

database system. Using this information, the database system was constructed. There were

weekly progress updates with the clinical research team to ensure that the original goals set

out to address the deficiencies of Excel were being resolved.

Results

Questionnaire Results

All 21 interventional radiologists completed the questionnaire. Self evaluation results are

shown in Figure 6. In data collection and analysis, over 50% (11/21) spent most of the time

searching, filtering, and/or categorizing data. However, about 50% (10/21) spent little to no

time calculating the data. 67% of respondents (14/21) realized at some point that there were

erroneously included patients who should have been excluded and there were patients who

were erroneously not included. Over 85% (18/21) were very receptive to using software that

produces group summaries such as totals of each tumor type with minimal effort, calculates

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clinical staging and score systems automatically, and also allows remote access for multiple

users to add/edit data in a central server with data modification logs.

Query Interface Output

In Figure 7, the query of male patients, over 40 years old, with HCC is run. Figure 8 shows a

query result of patients with TACE and Child-Pugh score A calculated by the database.

Figure 9 illustrates an interval of time between two events as a query that can be calculated

automatically (e.g. time elapsed between two embolization procedures). The output of the

queries as described above is shown in a structured and concise list, which can be exported

for further research study specific analysis.

Discussion

The main finding of this study is that there is a need for a much more time efficient and

accurate way to store, retrieve, and analyze patient data for clinical research studies. The

database management system presented here fulfills these needs. This was achieved through

the use of automatic calculations, interface forms, queries, etc. With a personalized

interface, data access, entry, organization, queries, calculations, and export processes are

seamlessly performed to assist clinical research with data and statistical analysis.

Furthermore, the database is a unified repository of clinical research information and a

shared resource among the clinical research team. This allows for a multi-user level

experience where there can be simultaneous access to the data and where the efforts of each

individual in adding/appending new information can be used by the entire team.

With the presented database put into use, the effort for clinical studies can truly focus on

conducting various statistical analysis and data interpretation rather than preparing data for

analysis (6). All retrospective data can be merged into this database, enabling a centrally

maintained and shared resource. Our clinical research team now has access to a customized

database of patients with a large number of clinical parameters, allowing a vast combination

of queries to form or support study hypotheses. The user defined GUI-connected interface is

invaluable for anyone collecting data as it facilitates data entry and minimizes data entry

errors.

In previous data collection and analysis, converting spreadsheet data to binary/numeric

format was time consuming and impractical. The database presented in this study relieves

the inconvenience of manually searching, organizing, and calculating data. Processing

calculations, especially more complex calculations such as clinical staging scores, can now

be done automatically. Prior to implementing the presented database system, a typical Excel

spreadsheet for the clinical studies at our institution would have over 100 columns. These

columns included patient demographics, repeat treatment dates and types (new columns per

TACE session), and repeated pre-/post-imaging dates and types (new columns per multi-

modular scan). Tracking medical data is frequently difficult due to the large amount of

columns in the spreadsheet. Compared to a typical Excel spreadsheet with many columns,

browsing and adding prospective data through the database interface presented here is more

organized and practical with ten defined tabs for data groups, ranging from a patient’s basic

information to treatments to survival status. In addition, the database interface lists all

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repeating treatments and imaging per patient as rows instead of columns, facilitating

comparisons between multiple treatments of a patient. Combining the database’s ability to

calculate statistical analysis with automatic calculation queries, reports can be generated

with virtually any parameter. This is not only helpful in radiology, but also beneficial for

other studies and hospital information systems.

The database management system in this study has some limitations. A database system may

not be suitable for all kinds of research teams. There are several factors that may illustrate

the need of a database. In a previous report on data collection, applicable examples and

guidelines were addressed to determine whether or not implementing a database is feasible

in the current environment (7). Depending on the environment and context, a database may

not be implemented right away as it needs additional testing. Furthermore, the database will

need a dedicated server to host the database along with the data. In order to use the database

interface, training is required. Someone who specializes in databases, such as a database

administrator, needs to teach researchers and other potential users how to use the database

interface and query interface for filtering patients and obtaining statistics. This is especially

needed in more advanced queries and in developing additional GUIs. It should be noted that

Microsoft Access is being used in this work as a “front-end” interface that communicates

with the SQL database to query (filter) data, and for input/appending to existing data. Other

software such as FileMaker Pro (FileMaker, Santa Clara, CA) and REDCap would serve a

similar function (8). The need for the SQL database is so that multiple users can access the

stored data at the same time, increased level of security, stability, and performance, and

serving as a unified repository of clinical research information that can be shared by the

research team (9, 10). Also, the database administrator has to not only construct a database

on a server with input from clinicians and other end users, but in addition would need to

maintain the database (11, 12). Typical maintenance includes routine backups, altering

database structure and interface for new data types, and updating database and client

software. A server can be hosted on a PC or online, both of which all parties involved can

access in the same network locally or remotely. Furthermore, databases can be enabled to

communicate with other databases. While the initial setup and learning curve is high, the

database allows for fluid data entry in an organized fashion, querying results including

calculations, and storing data while supporting simultaneous user access. With the variety of

research teams and departments, ideally each suitable team should have their own database.

This is not necessarily only for interventional oncology but also for any specific area of

research, for example, studies with patients undergoing ablation, percutaneous abscess

drainage (PAD), etc. These databases can be connected for interdisciplinary research to

provide a broader scope of data and facilitate data search (13).

Conclusion

The current database implementation and interface allows a much faster and more detailed

retrospective analysis of patient cohorts. In addition, it facilitates data management and a

standardized information output for ongoing prospective clinical trials. The database

management system with an interface is a work efficient and robust tool that provides a

significant edge over manual retrieval of patient records by filtering data and assisting

statistical analysis in a study-relevant fashion.

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Acknowledgments

Funding and support has been provided by NIH/NCI R01 CA160771, P30 CA006973, and Philips Research North
America, Briarcliff Manor, NY, USA.

References

1. Meineke FA, Staubert S, Lobe M, Winter A. A comprehensive clinical research database based on
CDISC ODM and i2b2. Studies in health technology and informatics. 2014; 205:1115–9. [PubMed:
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2. Stobart, S.; Vassileiou, M. MySQL Database and PHPMyAdmin Installation PHP and MySQL
Manual. Springer; London: 2004. p. 461-73.

3. Kuenz, D. Book Manage data for free with MySQL. City: Element K Journals; 2001. Manage data
for free with MySQL; p. 7-10.

4. Coronel, CMS.; Rob, P. Database systems: design, implementation, and management. 9. Boston,
Massachusetts: Cengage Learning; 2009.

5. Llovet JM, Di Bisceglie AM, Bruix J, et al. Design and endpoints of clinical trials in hepatocellular
carcinoma. Journal of the National Cancer Institute. 2008; 100(10):698–711. [PubMed: 18477802]

6. Kanas G, Morimoto L, Mowat F, O’Malley C, Fryzek J, Nordyke R. Use of electronic medical
records in oncology outcomes research. ClinicoEconomics and outcomes research : CEOR. 2010;
2:1–14. [PubMed: 21935310]

7. Schmier JK, Kane DW, Halpern MT. Practical applications of usability theory to electronic data
collection for clinical trials. Contemporary clinical trials. 2005; 26(3):376–85. [PubMed: 15911471]

8. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture
(REDCap)—A metadata-driven methodology and workflow process for providing translational
research informatics support. Journal of Biomedical Informatics. 42(2):377–81. [PubMed:
18929686]

9. MySQL Database Provides Full Transactional Support. Worldwide Databases. 2002; 14(11) 0-N/A.

10. Oracle Improves Database Performance with Latest Development Milestone Release for MySQL
5.7; New Release of the World’s Most Popular Open Source Database is 2x Faster than MySQL
5.6 and Over 3x Faster than MySQL 5.5 in Benchmark Tests. Book Oracle Improves Database
Performance with Latest Development Milestone Release for MySQL 5.7; New Release of the
World’s Most Popular Open Source Database is 2x Faster than MySQL 5.6 and Over 3x Faster
than MySQL 5.5 in Benchmark Tests. City2014.

11. Xie SX, Baek Y, Grossman M, et al. Building an integrated neurodegenerative disease database at
an academic health center. Alzheimer’s & dementia : the journal of the Alzheimer’s Association.
2011; 7(4):e84–93.

12. Parkes, D.; Lowman, M.; Andres, C., et al., editors. Pro Python System Administration: Apress.
2010. Automatic MySQL Database Performance Tuning; p. 329-48.

13. Piriyapongsa J, Bootchai C, Ngamphiw C, Tongsima S. microPIR: an integrated database of
microRNA target sites within human promoter sequences. PloS one. 2012; 7(3):e33888. [PubMed:
22439011]

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Figure 1. The Dataflow Chart
This chart shows a general layout of the database server and its clients. It illustrates how the

database management system performs queries (orange circle) such as statistical analysis.

Multiple computers are granted access to the database. The blue rectangles represent the

database management system software. Researchers can utilize the database client graphical

user interface (GUI) to import data without needing to format. Researchers also control data

through the GUI. Queries are usually run through the GUI to provide wanted results. Once

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the results are obtained, researchers export the query to a spreadsheet, illustrated by the

green rectangle.

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Figure 2.
This figure illustrates the query interface. In this example query, a list of male patients over

the age of 40 with hepatocellular carcinoma (HCC) is wanted. The user inputs search criteria

for age, gender, and tumor type, “>40”, “m”, and “HCC” respectively. MRN: medical record

number.

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Figure 3.
This form illustrates how users input data to the database. The form is divided into three

parts:

(a) Patient Form – Data consists of basic patient information. Patient Identification (PID) is

a unique number generated by the database to uniquely identify patients. LAST MODIFIED

is a timestamp of when the data was most recently updated or added. MODIFIED BY is a

text box that records who updated/added data. (a1) shows the total amount of patients in the

database.

(b) Tumor – Data consists of a patient’s primary and secondary tumors in the liver. The

dropdown allows users to select a tumor or add new tumor types (e.g. metastatic disease).

(b1) shows how many tumors types the patient has in the liver.

(c) Embolization Procedures – Data consists of intra-arterial therapies (IATs) sessions. (c1)

shows how many IATs sessions a patient has went through.

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Figure 4.
This figure illustrates the tabular form where each group of related data is shown as

individual tabs to assist user navigation. The display of patient identification information

and comments are maintained while the user navigates to different tabs to preserve the scope

and field of view for each patient.

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Figure 5.
This form shows a patient’s Child-Pugh score and Barcelona Clinic Liver Cancer (BCLC)

stage. They are automatically calculated when provided with pertinent patient data. The

“Calculate” buttons are used to refresh the form should any patient data value change.

PT/INR: Prothrombin Time/International Normalized Ratio; PS: Performance Status.

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Figure 6.
The self evaluation results are from Table 1.

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Figure 7.
This figure illustrates the output of a query for male patients with hepatocellular carcinoma

(HCC). The interface outputs a list of all patients matching the search criteria.

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Figure 8.
This is the output of a query for patients who had undergone TACE in 2006

(P_PROC_DATE column) with Child Pugh Class A, here labeled as “Classification”. The

automatically calculated Child-Pugh Class can be used also for querying.

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Figure 9.
The database automatically calculates the days between TACE sessions for each patient as a

query (red circle). The current treatment “EMBODate,” is subtracted from the next

treatment, “Next_EMBO.” Empty fields indicate that the patient has undergone only one

treatment or the session is the latest treatment. Because the query is saved, double clicking

the query indicated by the red circle refreshes the calculation for the entire database of

patients.

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Table 1

Questionnaire Assessment

Response: Yes No

Question: I searched and filtered data manually
Example: Sorting and copying relevant data

Question: I inputted formulas and Excel functions to calculate scores, response rates, or statistics in my Excel spreadsheet

Question: I summarized my Excel data in a report
Example: Total number of Child Pugh A patients

Question: I converted non-binary data (volume measurements, numeric values, occurrence rates of symptoms) into binary data (0/1) by defining
a cut-off point to differentiate
Example: Between responder and non-responder to a given therapy for statistical analysis

Question: I have done statistical analysis myself

Question: I unknowingly produced duplicate data that I later found out was already collected by another colleague

Response: 0–20% 21–40% 41–60% 61–80% 81–100%

Question: From the beginning of data collection to finishing analysis, about what percentage of the total time spent for a single retrospective
study did you spend on:

Question: Querying/filtering/categorizing data?
Example: Defining subsets of patients with certain criteria such as patients treated only with cTACE or only with DEB-TACE

Question: Calculating data?
Example: Min, Max, Mean, Sum, Clinical Scores such as Child-Pugh

Response: Very Unlikely Unlikely Neutral Likely Very Likely

Question: If given the opportunity, how likely will you use software that:

Question: Produces group summaries with minimal effort?
Example: Total number of Child Pugh A patients

Question: Calculates clinical staging and score systems automatically?

Question: Allows multiple users to add and edit data into the same database so that redundant collection of the same patients by different
colleagues can be avoided?

Question: Allows users to track data modifications?

Question: Stores data in a centralized location with remote access?

Acad Radiol. Author manuscript; available in PMC 2016 April 01.