Cross border semantic interoperability for learning health systems: The EHR4CR semantic resources and services.

Area of Science:

• Health Informatics
• Bioinformatics
• Data Science

Background:

• Increasing integration of phenome, genome, and exposome data in international research necessitates advanced data management.
• Reusing clinical data requires computable, portable phenotype algorithms across diverse electronic health record (EHR) systems.
• Cross-border and cross-domain semantic interoperability are key challenges in federated EHRs.

Purpose of the Study:

• To propose a framework for describing and comparing mediation platforms for cross-border phenotype identification within federated EHRs.
• To evaluate the effectiveness of such platforms using real-world project data.

Main Methods:

• Development of a framework to assess semantic interoperability platforms.
• Application of the framework to the EHR4CR project.
• Evaluation of a platform accessing semantically equivalent data across 11 European EHR systems from 5 countries.

Main Results:

• The proposed framework facilitates the description and comparison of mediation platforms.
• Experience from the EHR4CR project demonstrated the platform's capability in accessing semantically equivalent data across diverse EHR systems.
• Core requirements for cross-border semantic integration are being addressed by platform developers.

Conclusions:

• A standardized framework is essential for evaluating and developing semantic interoperability solutions for federated EHRs.
• Successful cross-border data integration relies on computable phenotype algorithms and semantically equivalent data elements.
• Continued development of semantic interoperability platforms is progressing to meet research data integration needs.