Evaluating the Impact of Electronic Health Record to Electronic Data Capture Technology on Workflow Efficiency: a Site Perspective

  • Anna Patruno
  • , Michael Owen Panzarella
  • , Michael Buckley
  • , Milena Silverman
  • , Evelyn Salazar
  • , Renata Panchal
  • , Joseph Lengfellner
  • , Alexia Iasonos
  • , Maryam Garza
  • , Byeong Yeob Choi
  • , Meredith Zozus
  • , Stephanie Terzulli
  • , Paul Sabbatini

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article numberooaf139
JournalJAMIA Open
Volume8
Issue number5
DOIs
StatePublished - Oct 1 2025

Keywords

  • EHR-To-EDC
  • clinical research
  • data managers
  • electronic data capture
  • technologies
  • throughput

ASJC Scopus subject areas

  • Health Informatics

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