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Automating Data Entry from Electronic Health Record to Electronic Data Capture Using a Trusted Cloud-Based

Keith Goodman^1,2, Chris Cook^1,2, Dani Weatherbee^1,3

¹SWOG Cancer Research Network Statistics and Data Management Center, Seattle, WA, USA.

Journal of the Society for Clinical Data Management

|June 13, 2025

Summary

This summary is machine-generated.

A new cloud-based application streamlines data transfer from electronic health records (EHR) to electronic data capture (EDC) systems, significantly saving time and improving data accuracy for clinical trials.

Keywords:

EHR2EDC data capture data quality electronic health record technology adoption technology evaluation

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Area of Science:

Clinical Informatics
Health Data Management
Cancer Research Technology

Background:

Seamless data transfer from Electronic Health Records (EHR) to Electronic Data Capture (EDC) systems is a long-standing challenge in clinical research.
Improving data accuracy and quality while reducing the burden on clinical research professionals are key goals.

Purpose of the Study:

To evaluate a secure, third-party cloud-based application linking EHR databases and EDC systems for clinical trials.
To assess the impact of this technology on site staff workload, data accuracy, and completeness.

Main Methods:

Three SWOG Cancer Research Network sites participated in the study.
A cloud-based EHR-to-EDC application was used to collect study data for tumor assessment forms for a limited number of patients.
Data collection was compared against manual medical record abstraction, measuring time savings, error rates, and interrater reliability.

Main Results:

The software-assisted method demonstrated substantial time savings compared to manual abstraction.
Significant improvements in data quality were observed, particularly for automatically captured data fields.
The use of the cloud-based application led to higher data accuracy.

Conclusions:

Secure, cloud-based applications effectively link EHR and EDC systems for clinical trial data collection.
This technology offers considerable time savings for clinical research professionals.
The approach enhances data accuracy in clinical trial research.