Aligning Semantic Interoperability Frameworks with the FOXS Stack for FAIR Health Data.

BACKGROUND: FAIR Guiding Principles present a synergy with the use cases for digital health records, in that clinical data need to be found, accessible within a range of environments, and data must interoperate between systems and subsequently reused. The use of HL7 FHIR, openEHR, IHE XDS, and SNOMED CT (FOXS) together represents a specification to create an open digital health platform for modern health care applications. OBJECTIVES: To describe where logical FOXS components align to the European Open Science Cloud Interoperability Framework (EOSC-IF) reference architecture for semantic interoperability. This should provide a means of defining if FOXS aligns to FAIR principles and to establish the data models and structures that support longitudinal care records as being fit to underpin scientific research. METHODS: The EOSC-IF Semantic View is a representation of semantic interoperability where meaning is preserved between systems and users. This was analyzed and cross-referenced with FOXS architectural components, mapping concepts, and objects that describe content such as catalogues and semantic artifacts. RESULTS: Majority of conceptual Semantic View components were featured within the FOXS architecture. Semantic Business Objects are composed of a range of elements such as openEHR archetypes and templates, FHIR resources and profiles, SNOMED CT concepts, and XDS document identifiers. Semantic Functional Content comprises catalogues of metadata that were also supported by openEHR and FHIR tools. CONCLUSIONS: Despite some elements of EOSC-IF being vague (e.g., FAIR Digital Object), there was a broad conformance to the framework concepts and the components of a FOXS platform. This work supports a health-domain-specific view of semantic interoperability and how this may be achieved to support FAIR data for health research via a standardized framework.

Utilising the FOXS Stack for FAIR Architected Data Access.

A FOXS stack assembles HL7 FHIR, openEHR, IHE XDS and SNOMED CT as an operational clinical data platform to build digital systems. This paper analyses its applicability for FAIR-enabled medical research based on a summary of key principles. It highlights the benefit of the blended approach to operational technology stacks for health systems, and a need for industry standard technologies to enable greater semantic coherence for primary/secondary data use.

openEHR Based Contextual Problem List.

The problem list is a key facet of the digital patient record that has historically been difficult to curate. This paper presents an implementation of a contextual problem list using openEHR. It describes the modelling approach, key model elements, and how these are assembled to underpin a Problem Oriented Medical Record. Finally, it discusses issues associated with how problem lists may be used.

Defining the Contextual Problem List.

The Problem Oriented Medical Record (POMR) is considered a key charting method to support clinical care. Although not uniformly represented amongst digital health systems, this paper presents a clinical model to represent multiple clinical perspectives from a single problem list. The contextual problem list model is defined according to primary diagnosis, comorbidities and problems arising from the primary condition. It is represented within the patient record as a single composition according to the prescribed context. The model pattern could help alleviate the traditional criticisms of paper and digitally based problem records.