TY - JOUR
T1 - Evaluating the Impact of Electronic Health Record to Electronic Data Capture Technology on Workflow Efficiency
T2 - a Site Perspective
AU - Patruno, Anna
AU - Panzarella, Michael Owen
AU - Buckley, Michael
AU - Silverman, Milena
AU - Salazar, Evelyn
AU - Panchal, Renata
AU - Lengfellner, Joseph
AU - Iasonos, Alexia
AU - Garza, Maryam
AU - Choi, Byeong Yeob
AU - Zozus, Meredith
AU - Terzulli, Stephanie
AU - Sabbatini, Paul
N1 - Publisher Copyright:
© The Author(s) 2025. Published by Oxford University Press on behalf of the American Medical Informatics Association.
PY - 2025/10/1
Y1 - 2025/10/1
N2 - Introduction: Clinical trial data is still predominantly manually entered by site staff into Electronic Data Capture (EDC) systems. This process of abstracting and manually transcribing patient data is time-consuming, inefficient and error prone. Use of Electronic Health Record to Electronic Data Capture (EHR-To-EDC) technologies that digitize this process would improve these inefficiencies. Objectives: This study measured the impact of EHR-To-EDC technology on the data entry workflow of clinical trial data managers. The primary objective was to compare the speed and accuracy of the EHR-To-EDC enabled data entry method to the traditional, manual method. The secondary objective was to measure end user satisfaction. Materials and Methods: Five data managers ranging in experience from 9 months to over 2 years, were assigned an investigator-initiated, Memorial Sloan Kettering-sponsored oncology study within their disease area of expertise. Each data manager performed one-hour of manual data entry, and a week later, one-hour of data entry using IgniteData’s EHR-To-EDC solution, Archer, on a predetermined set of patients, timepoints and data domains (labs, vitals). The data entered into the EDC were compared side-by-side and used to evaluate the speed and accuracy of the EHR-To-EDC enabled method versus traditional, manual data entry. A user satisfaction survey using a 5-point Likert scale was used to collect feedback regarding the selected platform’s learnability, ease of use, perceived time savings, perceived efficiency, and preference over the manual method. Results: The EHR-To-EDC method resulted in 58% more data entered versus the manual method (difference, 1745 data points; manual, 3023 data points; EHR-To-EDC, 4768 data points). The number of data entry errors was reduced by 99% (manual, 100 data points; EHR-To-EDC, 1 data point). Regarding user satisfaction, data managers either agreed or strongly agreed that the EHR-To-EDC workflow was easy to learn (5/5), easy to use (4.6/5), saved time (5/5), was more efficient (4.8/5), and preferred it over the manual entry workflow (4/5). Conclusion: EHR-To-EDC enabled data entry increases data manager productivity, reduces errors and is preferred by data managers over manual data entry.
AB - Introduction: Clinical trial data is still predominantly manually entered by site staff into Electronic Data Capture (EDC) systems. This process of abstracting and manually transcribing patient data is time-consuming, inefficient and error prone. Use of Electronic Health Record to Electronic Data Capture (EHR-To-EDC) technologies that digitize this process would improve these inefficiencies. Objectives: This study measured the impact of EHR-To-EDC technology on the data entry workflow of clinical trial data managers. The primary objective was to compare the speed and accuracy of the EHR-To-EDC enabled data entry method to the traditional, manual method. The secondary objective was to measure end user satisfaction. Materials and Methods: Five data managers ranging in experience from 9 months to over 2 years, were assigned an investigator-initiated, Memorial Sloan Kettering-sponsored oncology study within their disease area of expertise. Each data manager performed one-hour of manual data entry, and a week later, one-hour of data entry using IgniteData’s EHR-To-EDC solution, Archer, on a predetermined set of patients, timepoints and data domains (labs, vitals). The data entered into the EDC were compared side-by-side and used to evaluate the speed and accuracy of the EHR-To-EDC enabled method versus traditional, manual data entry. A user satisfaction survey using a 5-point Likert scale was used to collect feedback regarding the selected platform’s learnability, ease of use, perceived time savings, perceived efficiency, and preference over the manual method. Results: The EHR-To-EDC method resulted in 58% more data entered versus the manual method (difference, 1745 data points; manual, 3023 data points; EHR-To-EDC, 4768 data points). The number of data entry errors was reduced by 99% (manual, 100 data points; EHR-To-EDC, 1 data point). Regarding user satisfaction, data managers either agreed or strongly agreed that the EHR-To-EDC workflow was easy to learn (5/5), easy to use (4.6/5), saved time (5/5), was more efficient (4.8/5), and preferred it over the manual entry workflow (4/5). Conclusion: EHR-To-EDC enabled data entry increases data manager productivity, reduces errors and is preferred by data managers over manual data entry.
KW - EHR-To-EDC
KW - clinical research
KW - data managers
KW - electronic data capture
KW - technologies
KW - throughput
UR - https://www.scopus.com/pages/publications/105020259387
UR - https://www.scopus.com/pages/publications/105020259387#tab=citedBy
U2 - 10.1093/jamiaopen/ooaf139
DO - 10.1093/jamiaopen/ooaf139
M3 - Article
C2 - 41180891
AN - SCOPUS:105020259387
SN - 2574-2531
VL - 8
JO - JAMIA Open
JF - JAMIA Open
IS - 5
M1 - ooaf139
ER -