An HL7 FHIR and GraphQL approach for interoperability between heterogeneous Electronic Health Record systems.

Area of Science:

• Health Informatics
• Computer Science

Background:

• Electronic Health Record (EHR) systems face interoperability challenges due to data representation and process heterogeneities.
• Existing healthcare standards like Clinical Document Architecture (CDA), OpenEHR, and HL7 FHIR, while beneficial, introduce further complexity at structural and implementation levels.
• Achieving semantic data interoperability necessitates mechanisms to resolve data-level heterogeneity between disparate systems.

Purpose of the Study:

• To propose and implement an architecture for resolving data-level heterogeneity and enabling semantic interoperability between heterogeneous EHR systems.
• To demonstrate the efficacy of a resource server utilizing GraphQL and HL7 FHIR for facilitating communication between distinct EHRs.
• To establish secure and accurate data mapping for timely health information exchange.

Main Methods:

• Developed a novel architecture featuring a resource server leveraging GraphQL and HL7 FHIR.
• Implemented the proposed architecture to connect two heterogeneous EHR systems: HL7 FHIR and OpenMRS.
• Established secure communication channels and defined accurate data mappings between the integrated EHRs.

Main Results:

• The implemented architecture successfully established secure communication between heterogeneous EHR systems.
• Accurate data mappings were achieved, facilitating timely health information exchange.
• The solution effectively addressed data-level heterogeneity, enhancing semantic interoperability.

Conclusions:

• The proposed architecture provides a viable solution for achieving semantic interoperability between heterogeneous EHR systems.
• Utilizing GraphQL and HL7 FHIR offers a robust mechanism for secure and efficient health information exchange.
• This approach contributes to overcoming critical barriers in healthcare data integration.