1) A clinical trial using direct data entry (DDE) of patient data into an electronic data capture (EDC) system saw major reductions in screening errors, transcription errors, onsite monitoring needs, and overall costs compared to trials using paper records. 2) DDE allowed for rapid identification and correction of compliance issues and safety concerns in real time. 3) The clinical site estimated they saved 70 hours of data entry labor by not using paper as the original source records.

1. Clinical Trials
Lessons Learned From a Direct Data Entry
Phase 2 Clinical Trial Under a US
Investigational New Drug Application
Jules T. Mitchel, MBA, PhD1
, Judith M. Schloss Markowitz, MS1
, Hua (Helen) Yin, MS1
,
Dean Gittleman, MS1
, Timothy Cho, BS1
, Yong Joong Kim, MS1
, Joonhyuk Choi, BS1
,
Mitchell D. Efros, MD, FACS2
, Kerri Weingard, ANP, MS, BSN, RN2
,
Vadim Tantsyura, MS, MA3
, and Dario Carrara, PhD4
Abstract
In order to assess the impact of direct data entry (DDE) on the clinical trial process, a single-site, phase 2 clinical trial, under a US
investigational new drug application (IND), was performed where the clinical site entered each subject’s data into an electronic
data capture (EDC) system at the time of the office visit and the clinical research team implemented a risk-based monitoring
(RBM) plan. For DDE, the trial used EDC for data collection and the electronic clinical trial record (eCTR) as the subject’s elec-
tronic source (eSource) record. A clinical data monitoring plan (CDMoP) defined the scope of source document verification, the
frequency and scope of online data review, and the criteria for when to perform onsite monitoring. As a result of this novel
approach to clinical research operations, (1) there were no protocol violations as screening errors were picked up prior to treat-
ment; (2) because there were minimal transcription errors from paper source records to the EDC system, there was a major
reduction in onsite monitoring compared to comparable studies that use paper source records; (3) EDC edit checks were able
to be modified early in the course of the clinical trial; (4) compliance issues were identified in real time and corrected; (5) there
was rapid transparency and detection of safety issues; and (6) the clinical site indicated that there were major cost savings overall
and estimated that just in terms of data entry, it was able to save 70 hours of labor by not using paper as the original source
records. It is postulated that once the pharmaceutical industry adopts DDE and RBM, there will be major increases in productivity
for sponsors, clinical sites, and CROs, as well as reduced time to database lock and the statistical analyses. In addition to the pro-
ductivity increases, these processes and tools will improve data integrity and quality and potentially reduce overall monitoring
resources and efforts by an estimated 50% to 60%.
Keywords
direct data entry, EDC, data management, risk-based monitoring
Introduction
In order to encourage the use of direct data entry (DDE), in
2006, the Clinical Data Interchange Standards Consortium
(CDISC) Electronic Source Data Interchange Working Group
addressed the leveraging of the CDISC standards when elec-
tronic source data are used within clinical trials.1
In 2007, FDA
acknowledged that original data can be ‘‘recorded by direct
entry into a computerized system.’’2
In 2010, both FDA and
EMA discussed the advantages of DDE and proposed guidance
to the pharmaceutical industry on what issues to address when
moving from paper to electronic (eSource) records.3,4
In 2011,
EMA and FDA issued draft guidances on risk-based monitor-
ing of clinical trials.5,6
These latter documents were, in part, the
result of work by the Clinical Trials Transformation Initiative,
a public–private partnership formally established in 2008 by
the FDA and Duke University to identify practices that,
through broad adoption, will increase the quality and efficiency
1
Target Health Inc, New York, NY, USA
2
AccuMed Research, Garden City, NY, USA
3
NECDQ Consulting, Danbury, CT, USA
4
Ferring Galeschines Labor AG, Basel, Switzerland
Submitted 28-Feb-2012; accepted 24-Apr-2012.
Corresponding Author:
Jules T. Mitchel, Target Health Inc, 261 Madison Avenue, New York, NY 10016,
USA (email: jmitchel@targethealth.com)
Drug Information Journal
46(4) 464-471
ª The Author(s) 2012
Reprints and permission:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0092861512449530
http://dij.sagepub.com

2. of clinical trials.7
Clearly, the roles of clinical research associ-
ates (CRAs)8
and data managers9
have changed in the world of
electronic data capture (EDC). In addition, there is now a
movement toward rational approaches to the conduct and mon-
itoring of clinical trials based on a shift to risk-based monitor-
ing (RBM),10
quality-by-design methods,11
and DDE.12
Methods
A US investigational new drug application (IND), with a com-
prehensive protocol section addressing DDE and RBM, was
cleared by FDA in 2011. The clinical trial was a phase 2 study
performed at a single site and was designed to evaluate the
pharmacokinetics of a topically applied drug product in a
patient population. The clinical research site was based at an
established urology practice located in the New York metropol-
itan area. The site had experience with EDC systems but had
never worked directly with Target Health Incorporated. Target
Health monitored the clinical site, managed the study budget,
performed data management, analyzed the study results, and
wrote the clinical study report. Except for the site’s experience
in clinical research and willingness to participate in the study
and provide active feedback, there were no unique characteris-
tics of the clinical site that would undermine extrapolation of
the current results to the wider clinical research community.
The sponsor agreed that for the clinical program, in lieu of
requiring the clinical site to first enter source data on a piece
of paper and then transcribe the data into an EDC system, the
site would perform DDE of subject data into the EDC system
during the office visit, similar to what is done when electronic
medical records (EMRs) are used. Prior to submitting the pro-
tocol to the IND, discussions were held with the FDA to ensure
that the approach to DDE would comply with 21 Code of Fed-
eral Regulations part 11 rules and regulations. In addition, to
ensure compliance with CDISC, EMA, and FDA requirements
that the site have sole control and continuous access to the elec-
tronic source (eSource) record, an electronic clinical trial record
(eCTR) was created before the data were transmitted to the EDC
database. For the eCTR, a transaction rule was configured such
that at the time of data entry, a Web-based pdf file was created
that was transferred electronically to a read-only eCTR Viewer,
which acted as a ‘‘trusted third party’’ environment. Only when
this transaction was electronically confirmed were the data then
transmitted to the EDC database. The clinical site had control of
user access to the eCTR Viewer, and the system maintained an
audit trail of all changes to the eSource records. At any point dur-
ing the study the clinical site could generate a bookmarked pdf
copy of individual electronic case report forms (eCRFs) or the
eCRFs for all subjects.
In addition to using an eSource method, the clinical data mon-
itoring plan (CDMoP) allowed for centralized monitoring as
well as risk-based monitoring of certain data that were collected
initially using paper records (eg, 2 questionnaires and paper
printouts from the site’s EMR). Finally, a decision log (of the
issues that arose during the study and their resolution) was main-
tained in Excel, and an online monitoring report, fully integrated
with the EDC system, maintained an online electronic Issues and
Resolution Log associated with central and onsite monitoring.
In order to perform risk-based monitoring, online reports
were created to track the following:
1. Key inclusion and exclusion criteria
2. Data entry status
3. CRA review status of the eCRF forms
4. Edit checks that fired for each form and specific variables
5. Time for query resolution
6. Medications
7. Adverse events
In addition, the EDC system supported the following:
1. Online qualification, initiation, interim, and closeout visit
reports, all with eSignatures
2. Online MedWatch Form 3500a and the Council for Interna-
tional Organizations of Medical Sciences (CIOMS) forms
3. Coding of adverse events, medications, and medical
history
To maintain data confidentiality, certain identifiers and results
have been masked.
Results
Study Timelines
At the time of site qualification, the site staff were asked
whether they were willing to participate in the clinical trial and
to be part of an innovative program in DDE. They immediately
agreed. After institutional review board (IRB) approval was
obtained, screening of study subjects was completed in 14 days,
treatment in 44 days, and database lock within 19 days of the
last patient’s last visit (Table 1).
Table 1. Study timelines.
Task Day
Clinical site qualified 0
Institutional review board approval 7
Clinical trial initiated 18
Screening of 42 study subjects initiated 24
Treatment begins with 20 subjects 38
Last patient last visit 82
Soft lock of individual completed subjects 94
Signing by investigator of eCRFs 95
Database lock 101
Mitchel et al 465

3. Source Document Verification
The CDMoP noted that since the study was primarily using
DDE, the following paper source records, if available, were
to be reviewed against the eCRF record:
1. Demographic data derived from the site’s EMRs
2. Medical history
3. Patient-reported outcome (PRO) forms
At each onsite monitoring visit, the monitor was to determine
whether any additional paper source records existed. In terms of
monitoring, the CDMoP required that the CRA do the following:
1. Be present at the first patient visit that involved blood sam-
pling and processing.
2. Request EDC changes immediately to refine the edit
checks to allow for better data flow, as needed.
3. Perform a daily review of all entered eCRF data.
4. Review online reports daily.
5. Review all new central laboratory data.
6. Review eCRF data entry procedures with the clinical site,
as needed.
Nosourcedocumentverification(SDV) wasperformedremotely.
The results of onsite SDV are presented in Table 2. Only 1 tran-
scription error was found in over 200 reviewed records. Once this
observation was made, it was decided that there was no reason to
perform additional onsite SDV as there was no identified risk
based on the observed incidence of transcription errors.
Visit Schedule
In order to assist the clinical site to ensure subject compli-
ance with the planned study visits, once the initial treatment
was scheduled, the EDC system automatically created a
study schedule that could be provided to each subject
(Table 3). The site coordinator was also able to use the visit
schedule to quickly assess compliance with the treatment
schedule.
Table 2. Onsite source document verification.
Subject Data Verified
% Verified for
Treated Subjects
% Verified for
Screen Failures Observations
Patient’s identity consistent in source
documents and eCRFs (ie, demographic
data consistent, including age, race, and date
of birth
100% 100% No transcription errors
Informed consent signed prior to subject’s
participation in the trial
100% 100% No transcription errors
Medical history 20% N/A No errors
Blood draw times vs paper source 100% for 2 periods N/A One minor transcription error as a
result of reviewing 160 data fields
Table 3. Visit schedule.
Visit No. Expected Date Actual Date
Signed
Informed Consent
02 Jun 2011
Thursday
Screening
(Day –14 - Day 0)
02 Jun 2011
Thursday
Visit 2 (Day 1) 02 Jun 11 Thursday - 16 Jun 11 Thursday
06 Jun 2011
Monday
Visit 3 (Day 2) 07 Jun 2011 Tuesday - 09 Jun 2011 Thursday
07 Jun 2011
Tuesday
Visit 4 (Day 7) 10 Jun 2011 Friday - 14 Jun 2011 Tuesday
13 Jun 2011
Monday
466 Drug Information Journal 46(4)

4. Real-Time Monitoring of eCRF Data
The EDC system tracked the date and time of data entry as well
as the date and time of eCRF review by the CRA. To evaluate
time to initial data review, the Registration, Demographics,
Medical History, Visit Date, Vital Signs, and Clinical Summary
forms were selected as performance indicators (Table 4).
Table 4 summarizes the time from data entry to initial data
review by the CRA for 6 representative forms. The median
time to initial review for Clinical Summary form was the short-
est (18 minutes) and Vital Signs form the longest (7 hours and 1
minute). One issue detected was the absence of a backup plan
during an annual vacation by one of the CRAs. Once remedied,
form review generally occurred within minutes or 1 to 2 hours
of data entry. Figure 1 illustrates the time in hours from data
entry to initial data review by the CRA combining the 6 repre-
sentative forms. The majority of forms were reviewed within 1
business day, and approximately 75% of forms were reviewed
within 1 business day.
Data Entry Status
In order to support risk-based monitoring, a series of reports
were configured to evaluate critical metrics that in themselves
were able to provide operational signals. Table 5 provides a
real-time snapshot of the status of subject enrollment as well
as the status of the reviewed forms. Once the top-line review
was made, it was possible to drill down to the data entry status
of individual subjects.
Navigation
In order for the CRA to quickly review the data entry status for
a given subject, a color-coded navigation report was config-
ured. In Table 6, except for the screening data, no other data are
entered for subject 04, who was a screen failure. In contrast,
all forms were entered and reviewed for subjects 22 and 23 for
visits 2, 3, and 4.
When the site or CRA viewed the data for individual
subjects, a form navigation module (see Table 7) was available.
Table 4. Time to initial review of data entry forms (h:min).
Registration Demographics Medical History Visit Date Vital Signs Clinical Summary
(n ¼ 42) (n ¼ 42) (n ¼ 142) (n ¼ 83) (n ¼ 120) (n ¼ 20)
Maximum 51:53 75:14 446:31 109:04 147:08 24:06
75th percentile 03:42 03:19 22:25 07:20 08:34 00:46
Median 01:39 01:15 02:25 01:59 07:01 00:18
25th percentile 00:23 01:15 00:43 00:20 02:15 00:07
Minimum 00:01 00:01 00:01 00:01 00:07 00:03
1
5
10
25
50
75
90
95
99 100
0
20
40
60
80
100
120
0.02 0.07 0.13 0.6 2.4 7.7 32.3 91.5 243.8 446.5
Cumulative%
Time to Initial Review (hours)
Figure 1. Time to CRF review during centralized monitoring.
Table 5. Example of the status of data entry.
# of
Subjects
Entered
# of
Screen
Failures
# of
Subjects
Treated
# of
Pages
Entered
# of
Pages
Reviewed
# of
Subjects
Locked
# of
Subjects
Signed
35 5 30 4562 4562 35 35
)elpmaxe(nwodkaerBstcejbuS
14 105 105 YES YES
23 193 193 YES YES
Mitchel et al 467

5. Table 6. Data entry status by visit.
Subject No. Screening (Day –14 - Day 0) Visit 2 (Day 1) Visit 3 (Day 2) Visit 4 (Day 7)
04 19 May 2011
22 19 May 2011 06 Jun 2011 07 Jun 2011 13 Jun 2011
23 19 May 2011 06 Jun 2011 07 Jun 2011 13 Jun 2011
Table 7. Data entry status by form.
Subject 07
Screening Visit Date Demographics Medical History
Visit 2 (Day 1) Visit Date Eligibility Vital Signs
Visit 4 (Day 7) Vital Signs Blood Sampling Drug Administration
Visit 5 (Day 22) Visit Date Pre-Dose Vital Signs Blood Sampling
End Of Trial End Of Trial
AE Adverse Event
Medications Medications
Table 8. Query frequency by form report.
Form
Pending
Monitor Reply
Pending
Site Reply
Resolved Total
Adverse Event 0 0 2 2
Concomitant Medication 4 8 18 30
Demographics 0 0 3 3
Medical History 5 5 0 10
Sample Collection 8 12 12 32
Physical Examination 0 0 4 4
Table 9. Query frequency by edit check.
Edit ID
Pending
Monitor Reply
Pending
Site Reply
Resolved Total
Med-10 6 8 1 15
AE-15 0 0 1 1
Drug-18 0 0 7 7
Lab-08 0 0 1 1
Skin-10 3 9 0 12
Demog-06 0 0 20 20
Visit-007 10 2 8 20
Vitals-010 0 0 1 1
468 Drug Information Journal 46(4)

6. This color-coded module allowed the clinical research team to
see which forms had been reviewed using the following color
codes: (1) not been entered (green); (2) have data (blue); (3)
have been reviewed (gold); and (4) have been reviewed and
locked by the data manager role (red).
Edit Checks
A total of 261 edit checks were fired and a total of 360 queries
were issued for the 3948 eCRF pages that were entered. Table 8
illustrates an example of a midstudy report of the number of
queries generated by form and the status of their review. The
frequency of queries per form was a good indicator of the status
of data quality, how well the clinical study site understood the
protocol, and possible design issues of the EDC system.
In order to better understand the causes of queries and fired
edits checks, an online report was configured to assess the
status of edit checks by variable (Table 9). By evaluating the
outcomes of this table, the clinical research team could focus
on specific data quality issues.
Data Review
While the metrics described here were very useful to evaluate
operational signals, looking at data over time is also very use-
ful. It is especially valuable when outcomes appear to differ
between sites or when a sponsor needs a comfort level that sub-
jects are responding to treatment. In Table 10, the CRA and
clinical team were able to assess a critical PRO between screen-
ing and the end of the study and to see that a positive outcome
was occurring within the trial.
Online Monitoring Reports
The online monitoring report function allowed the CRAs and
project managers to manage site monitoring totally within the
EDC system, as demonstrated in Table 11. There was no need
to mechanically copy and paste information already captured in
the EDC system. Table 11 illustrates part of the monitoring report
where the status of the study subjects is obtained directly from the
EDC system and the CRA entered responses to online prompts.
There were 57 items tracked within the online monitoring
reports that required follow-up. This online tool allowed both
the CRA and project manager to track the status of all outstand-
ing items. Table 12 illustrates some of the findings and resolu-
tions. By doing real-time data review and logging findings
within the monitoring reports, the CRA was able to identify
potential protocol violations during the screening period and
immediately inform the site about these potential violations.
eCTR Deliverable
During the study, the clinical sited was able create a book-
marked pdf copy of the eCTR of each individual study subject
Table 10. Change in a patient-reported outcome (PRO).
PID
Total PRO Score
Difference
Screening Visit 12
01 53 35 –18
02 30 35 5
03 28 30 2
04 34 28 –6
05 34 37 3
06 53 45 –8
07 38 39 1
08 35 32 –3
09 47 46 –1
Table 11. Online monitoring report.
Study Status and Subject Enrollment Yes No N/A Comment
?yrotcafsitasssergorpydutsdnatnemllornesI
Is the subject enrollment log current and accurate? Enrollment Log not being used
13CIdengisohwstcejbuSforebmuN
Derived From EDC Database
1eruliaFneercS
0detaerTstcejbuSforebmuN
03stcejbuSevitcAforebmuN
0detelpmoCstcejbuSforebmuN
Mitchel et al 469

7. or all study subjects. At the end of the study after database
lock, the sponsor provided an electronic copy of each sub-
ject’s eCTR to the study site, as well as maintained the eCTR
‘‘live.’’
Cost Savings
Under normal monitoring guidelines and with full SDV, it was
anticipated that the CRA would have had to spend 4 full days at
the clinical site plus travel time and preparation. In reality,
there was only 1 full-day monitoring visit at the time of the ini-
tial treatment phase and a 2-hour monitoring visit during the
course of the study. Thus, even if there were 2 full days of mon-
itoring for this study when using DDE, at an average cost of
US$3000 per monitoring visit, there would have been a cost
savings of US$6000 (2 visits) for the monitoring phase of this
single-center study.
Discussion and Conclusions
The Tufts Center for the Study of Drug Development recently
published the first global benchmark for CRA workload and
utilization indicating that CRAs worldwide spent approxi-
mately 20% of their time traveling and devoted only 41% of
their time at clinical trial sites.13
In addition, the study found
that CRA workload and time allocation vary widely by geo-
graphic region, with US-based study monitors spending more
time traveling and on site than their counterparts elsewhere.
European study monitors spend relatively more time perform-
ing off-site monitoring and administrative tasks.
Clearly, implementation of DDE, real-time monitoring at
the time of data entry, and risk-based monitoring will have pos-
itive effects on the clinical monitoring and data management
functions within the pharmaceutical and medical device indus-
tries. One of the biggest effects will be associated with the abil-
ity to see and evaluate data as they are being entered rather than
waiting days or weeks compared with the paper process. Since
transcription errors from paper records to an EDC system are
virtually eliminated,14
the clinical team can now have a com-
fort level that what is being observed in real time in the EDC
system is an accurate representation of the study subject’s data.
In addition, once the data are entered and monitored, often
within minutes of the office visit, the site can be queried imme-
diately. As a result, there is a good chance that the query will
be answered with data fresh in the mind of the study coordi-
nator. As a result, when the study subject leaves the office,
there is virtually nothing to do until the following office visit.
While the current report describes a single investigator
study, a multicenter study with 6 clinical sites designed to treat
Table 12. Online monitoring report—follow-up list.
Date Issue Follow-up Action
Date of
Resolution
27 May
2011
Patient 30 has RA.
Called the site and indicated to
the coordinator that since the
patient has RA, he is a screen
failure.
31 May
2011
26 May
2011
Study coordinator called regarding an
obese subject who had sleep apnea. He
had lost weight and no more apnea
anymore. Is this patient eligible?
Answer was no, as exclusion
criteria is history of sleep
apnea.
31 May
2011
26 May
2011
Patient 222 has a history of MS. Emailed site to SF patient.
13 Jun
2011
06 Jul
2011
Requested from Support that the
deviation from vital sign report be
removed.
Done
13 Jul
2011
06 Jul
2011
Medical History Page was incorrect.
Emailed Support to fix.
Fixed
13 Jul
2011
07 Jul
2011
The subject is scheduled for a Saturday
with blood draws.
Discussed with UUU from LAB.
Bloods will be picked up on
Saturday and LAB will analyze
them Sunday.
10 Jul
2011
08 Jul
2011
Confirmed with the study coordinator at
site that they have all the information
necessary for the samples
Confirmed
14 Jul
2011
470 Drug Information Journal 46(4)

8. 120 patients over 6 months is ongoing. The study is being man-
aged with just 1 CRA, the investigators and coordinators have
transitioned to DDE in a seamless manner, and there have been
no unexpected issues relating to DDE. In addition, a phase 3
study with 200 subjects is planned for Q3 2012 with 20 sites
with extensive training planned at the Investigator meeting to
address EDC, DDE, eCTR and the eTrial Master File.
It can be concluded that once the pharmaceutical industry
broadly adopts DDE and risk-based monitoring, the following
will result:
1. Significant increases in productivity for both the sponsors,
clinical sites, and contract research organizations (CROs)
as travel time and SDV will be reduced
2. Significant reductions in overall monitoring costs, as
onsite monitoring visits could possibly be scaled back by
50% to 60%
3. Earlier detection and analyses of adverse events resulting
in enhanced patient safety
Acknowledgments
The authors thank Joyce Hays, MS, CEO of Target Health Inc, for
reviewing the manuscript. For this publication, Target Health Incor-
porated’s e*CRF1
was used for EDC and Target’s e*CRF1
Viewer
was used to access the eSource records.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship,
and/or publication of this article.
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