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Automating the Integration of Longitudinal Clinical Trial Data Using REDCap
Sowjanya Batchu1, Gerrit Burkhardt2, Ulrich Mansmann1
1Institut für Medizinische Informationsverarbeitung Biometrie und Epidemiologie.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
Automated data integration using R workflows streamlined clinical trial data sharing. This improved data interoperability and reduced redundant research efforts for secondary analyses.
Area of Science:
- Clinical research informatics
- Data science in healthcare
- Biomedical data management
Background:
- Inefficient data exchange in clinical research leads to duplicated efforts and hinders secondary analyses.
- Inconsistent data formats and coded values across CSV files impede data interoperability and reuse.
- Standardizing data integration is essential for efficient multicenter clinical trial data management.
Purpose of the Study:
- To develop and implement an automated data integration workflow for clinical trial data.
- To improve data interoperability and facilitate secondary analyses of multicenter trial data.
- To address challenges posed by inconsistent field names and coded values in data exchange.
Main Methods:
- Developed a metadata-driven R workflow for automated data integration.
- Utilized the REDCap API for seamless data transfer from the Depression DC clinical trial.
- Implemented automated data harmonization to address inconsistencies in field names and coded values.
Main Results:
- Successfully integrated multicenter, longitudinal Depression DC clinical trial data into REDCap.
- The R workflow automated data harmonization, ensuring consistency and interoperability.
- Demonstrated efficient data exchange, reducing manual effort and potential errors.
Conclusions:
- Metadata-driven R workflows and API integration enable efficient and automated clinical trial data management.
- Automated data integration enhances data interoperability, supporting secondary research analyses.
- Standardized data exchange protocols are vital for accelerating clinical research and maximizing data utility.
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