From Excel to Automation: The Transplant Mapper for Interoperable Transplant Data Management

Area of Science:

• Medical Informatics
• Data Management in Healthcare
• Transplantation Research

Background:

• Manual data integration using Excel for Eurotransplant waitlist and transplant reports at LMU Hospital led to significant workload and errors.
• Existing data governance practices hindered efficient clinical decision support and research activities.

Purpose of the Study:

• To develop an automated, modular framework for synchronizing Eurotransplant data into REDCap.
• To establish a scalable, auditable, and FAIR-aligned data infrastructure for transplant registries.

Main Methods:

• Development of the Transplant Mapper, a framework utilizing Python Shiny dashboards for monitoring and a Django web interface for triggering mapping scripts.
• Implementation of differential updates, waitlist consistency checks, and support for multiple transplant instances via REDCap repeating events.
• Deployment for liver and kidney transplant programs, focusing on automated data synchronization.

Main Results:

• Successful synchronization of Eurotransplant report data into REDCap, replacing error-prone manual Excel workflows.
• Automated data synchronization completed in under one minute per instance.
• Established a scalable, auditable, and FAIR-aligned infrastructure adaptable to other transplant registries.

Conclusions:

• The Transplant Mapper provides a robust solution for secure transplant data governance, improving efficiency and data quality.
• This framework supports clinical decision-making and research by ensuring consistent and accessible transplant data.
• The system's modularity and adaptability make it suitable for broader application across different transplant registries and organ programs.