HL7 FHIR with SNOMED-CT to Achieve Semantic and Structural Interoperability in Personal Health Data: A...

Area of Science:

• Digital Health
• Health Informatics
• Interoperability Standards

Background:

• Data heterogeneity in digital health information systems poses challenges for semantic and structural integrity.
• Existing methods struggle to effectively integrate diverse health data sources like sensors and questionnaires into electronic health records.
• Fast Healthcare Interoperable Resources (FHIR) offers a structural standard to map various data models.

Purpose of the Study:

• To design and develop a smartphone application (eCoach) demonstrating the feasibility of bidirectional data exchange between a personal health record (PHR) and an electronic health record (EHR).
• To validate the use of HL7 FHIR and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) for semantic and structural data compatibility.
• To assess the effectiveness of FHIR modular resources for flexible data management within a PHR prototype.

Main Methods:

• Development of an intuitive health coaching (eCoach) smartphone application as a personal health record (PHR) prototype.
• Integration of HL7 FHIR and SNOMED-CT vocabularies for data exchange using JavaScript Object Notation (JSON).
• Implementation of a Services for Sensitive Data (TSD) security mechanism for end-to-end data protection.

Main Results:

• Achieved 0% data loss and 0% unreliable performance during data transfer between the PHR prototype and EHR.
• The eCoach prototype successfully implemented most PHR-S Functional Model (FM) functions, meeting interoperability quality standards.
• Demonstrated the effectiveness of FHIR modular resources for flexible management of data components within the PHR.

Conclusions:

• The developed eCoach application effectively demonstrates a structurally and logically compatible tethered PHR system.
• The combination of HL7 FHIR and SNOMED-CT facilitates seamless and secure personal health data exchange.
• This approach enhances the flexibility and reliability of digital health information systems, paving the way for improved patient data management.