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

Area of Science:

• Clinical Informatics
• Health Data Management
• Biomedical Research

Background:

• Clinical trials frequently utilize real-world data.
• Manual abstraction of data from electronic health records (EHRs) into electronic case report forms (eCRFs) is time-consuming, labor-intensive, and prone to errors.
• Automated EHR-to-eCRF data transfer offers a solution to reduce burden and enhance data quality and safety.

Purpose of the Study:

• To evaluate the feasibility and accuracy of automated data transfer from EHRs to eCRFs.
• To assess the coverage and concordance of automated data transfer in a clinical trial setting.

Main Methods:

• A test of automated EHR-to-CRF data transfer was conducted for 40 participants in a COVID-19 clinical trial.
• Coverage was determined by assessing which coordinator-entered data could be automated from the EHR.
• Concordance was measured by comparing automated EHR feed values with values entered by study personnel.

Main Results:

• The automated EHR feed populated 84% (10,081/11,952) of coordinator-completed values.
• Exact value match between automation and personnel entry was 89% for fields with both data sources.
• Highest concordance (94%) was observed for daily lab results, which were the most time-consuming for personnel.

Conclusions:

• Automated EHR feeds can substantially decrease study personnel effort.
• This automation has the potential to significantly improve the accuracy of clinical trial data captured in eCRFs.
• The findings suggest a promising approach for more efficient and reliable clinical trial data management.