Pulse Brain · Growing Health Evidence Index
Tier 3 — Observational / field trialPeer-reviewed

Evaluating automated electronic case report form data entry from electronic health records

Alex Cheng, Mary K. Banasiewicz, Jakea Johnson, Lina Sulieman, Nan Kennedy, Francesco Delacqua, Adam Lewis, Meghan M. Joly, Amanda J. Bistran-Hall, Sean P. Collins, Wesley H. Self, Matthew S. Shotwell, Christopher J. Lindsell, Paul A. Harris

Journal of Clinical and Translational Science · 2022

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Summary

This study tested automated data transfer from electronic health records to electronic case report forms in a clinical trial of 40 hospitalised COVID-19 patients. The automated system populated 84% of data fields with 89% exact concordance with manually entered values, particularly high for laboratory results (94% concordance). The findings suggest that automated EHR-to-eCRF transfer could substantially reduce study personnel burden whilst improving data accuracy, though implementation requires careful validation of discrepancies.

UK applicability

The findings are relevant to UK clinical research infrastructure, as the National Health Service and NHS trusts increasingly utilise EHR systems. However, direct applicability depends on whether UK EHR systems and eCRF platforms share similar data structure and integration capabilities as those tested in this United States hospital setting.

Key measures

Coverage (proportion of coordinator-completed values populated by automated EHR feed: 84%), concordance (exact value matching: 89% overall, 94% for daily lab results), personnel resource requirements (30 minutes per participant for daily lab results), and error categorisation from detailed discrepancy analysis

Outcomes reported

The study evaluated the coverage and concordance of automated electronic health record (EHR) data transfer to electronic case report forms (eCRFs) in a COVID-19 clinical trial. Key metrics included the proportion of coordinator-entered data that could be automated from the EHR and the frequency of exact value matching between automated and manually entered data.

Theme
Measurement & metrics
Subject
Measurement methods & nutrient profiling
Study type
Research
Study design
Field trial
Source type
Peer-reviewed study
Status
Published
Geography
United States
System type
Human clinical
DOI
10.1017/cts.2022.514
Catalogue ID
BFmoso8xrl-itrxrn

Topic tags

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