Simplifying electronic data capture in clinical trials: workflow embedded image and biosignal file integration and...

Area of Science:

• Clinical Informatics
• Biomedical Engineering
• Data Management

Background:

• Electronic data capture systems (EDCS) are standard in clinical trials but often lack integration with medical workflows and struggle with large data types like images and biosignals.
• Current EDCS limitations hinder the use of established surrogate endpoints such as electrocardiography (ECG) and medical imaging in clinical research.
• There is a need for enhanced EDCS capable of handling diverse data formats and integrating advanced analysis directly into the data capture process.

Purpose of the Study:

• To present an integrated workflow for EDCS, specifically OpenClinica, designed to overcome limitations in handling large data volumes and complex biosignals.
• To demonstrate seamless integration of study metadata, large binary objects (BLOBs), and automated analysis results within electronic case report forms (eCRFs).
• To validate the workflow's efficacy in a real-world multicenter clinical study involving long-term Holter ECG monitoring.

Main Methods:

• Developed a three-component integrated workflow: study metadata sharing, large volume data (BLOBs) integration into eCRFs, and automatic image/biosignal analysis.
• Utilized web services, JavaScript for metadata transfer, and secure file transfer protocol/hypertext transfer protocol for BLOB transmission.
• Implemented the workflow in a multicenter study requiring 7-day/24-hour Holter ECG monitoring for diabetic subjects.

Main Results:

• Successfully automated the transfer of study metadata into OpenClinica.
• Seamlessly integrated and processed large (4 GB) ECG data BLOBs directly within the eCRF.
• Achieved immediate feedback by writing signal analysis results back into the eCRF.

Conclusions:

• The presented integrated workflow enhances EDCS capabilities for managing complex clinical trial data, including large biosignal datasets.
• This approach improves data quality, cost-efficiency, and streamlines the integration of advanced data analysis into clinical research workflows.
• The successful application in a diabetes study demonstrates the workflow's potential for various clinical trials utilizing surrogate endpoints.