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A study on interoperability between two Personal Health Train infrastructures in leukodystrophy data analysis
Sascha Welten1, Marius de Arruda Botelho Herr2,3, Lars Hempel4,5,6
1RWTH Aachen University, Chair of Computer Science 5, Aachen, 52074, Germany. welten@dbis.rwth-aachen.de.
Scientific Data
|June 22, 2024
Summary
This study presents a technical framework for interoperable Personal Health Train (PHT) ecosystems, enabling distributed analytics across diverse infrastructures. The framework ensures data integration and security, facilitating cross-institutional research efficiently.
Area of Science:
- Health Informatics
- Distributed Systems
- Bioinformatics
Background:
- Distributed analytics platforms are crucial for meeting data governance and legal constraints.
- The Personal Health Train (PHT) is a key platform, but challenges arise when integrating multiple PHT infrastructures due to differing ecosystems.
- Interoperability is essential for seamless data sharing and analysis across institutions with varied PHT setups.
Purpose of the Study:
- To introduce a conceptual framework for achieving technical interoperability between different Personal Health Train (PHT) infrastructures.
- To address challenges in data governance, regulatory compliance, and workflow modifications when combining multiple PHT ecosystems.
- To enable distributed analytics across institutions by ensuring seamless data integration and analysis.
Main Methods:
- Developed a conceptual framework focusing on five key requirements: data integration, unified station identifiers, mutual metadata, aligned security protocols, and business logic.
- Evaluated the framework through a feasibility study involving two distinct PHT infrastructures: PHT-meDIC and PADME.
- Analyzed patient data on leukodystrophy and differential diagnoses from University Hospitals of Tübingen, Leipzig, and Aachen.
Main Results:
- Demonstrated technical interoperability between the PHT-meDIC and PADME infrastructures.
- Enabled researchers to perform distributed analyses across participating institutions.
- The proposed method is more space-efficient than multi-homing strategies with minimal time overhead.
Conclusions:
- The conceptual framework successfully establishes technical interoperability for Personal Health Train (PHT) platforms.
- This interoperability facilitates cross-institutional research by allowing unified data analysis.
- The approach offers an efficient and effective solution for integrating diverse PHT ecosystems.
