Semantic Interoperability in Healthcare: Medical Ontologies and GraphQL Integration Framework

Abstract

Semantic interoperability—the ability of systems to exchange data with unambiguous, shared meaning—is foundational to modern healthcare interoperability. We present a GraphQL Integration Framework that leverages medical ontologies (SNOMED CT, LOINC, ICD-10) and a GraphQL façade over HL7 FHIR to enable flexible, fine-grained, and semantically coherent data access. Key components include: (1) an ontology service (RDF/OWL store with SPARQL and Neo4j), (2) a GraphQL gateway implemented with Apollo Federation and GraphQL-Mesh translating queries into FHIR REST/GraphQL calls

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Keywords

Semantic Interoperability · Healthcare · Medical Ontologies · GraphQL · HL7 FHIR · Apollo Federation · GraphQL-Mesh · SPARQL · Neo4j

1. Introduction

Semantic interoperability in healthcare ensures that exchanged data retains precise meaning across disparate systems, reducing vendor lock-in and data silos. While HL7 FHIR provides resource definitions, REST APIs often force clients into rigid request/response patterns. GraphQL, as a schema-driven query language, offers flexible, client-specified projections and nested queries, but must be aligned with clinical terminologies to preserve semantics

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2. Background

2.1 Semantic Interoperability Challenges

Electronic Health Records (EHRs) from different vendors often use proprietary data models, hindering cross-organizational exchange. A systematic review identifies the need for shared terminologies and semantic web technologies (OWL, RDF, SPARQL) to reconcile heterogeneous schemas and ensure that “the meaning of data is preserved”

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2.2 Medical Ontologies for Shared Meaning

Key ontologies—SNOMED CT, LOINC, ICD-10, RxNorm—provide standardized concept sets and hierarchical relationships. Metadata repositories (MDRs) manage data element definitions, while terminology servers serve code systems and value sets, forming the semantic backbone for interoperability frameworks

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2.3 GraphQL & HL7 FHIR Integration

The FHIR specification includes an optional GraphQL interface that exposes resources as types and allows nested queries (e.g., Patient → Observation via _include semantics). Implementations such as graphql-fhir (Node.js/Express) and Graphir (.NET/HotChocolate) act as façade services over RESTful FHIR APIs, translating GraphQL queries into FHIR operations

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2.4 GraphQL Frameworks for Healthcare

graphql-fhir (Bluehalo): Secure Node.js/Express server mapping FHIR resources to GraphQL schemas.

Graphir (Microsoft): .NET proxy with HotChocolate providing GraphQL over Azure FHIR backends

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GraphQL-Mesh: Schema stitching across REST, SOAP, and SPARQL endpoints to unify FHIR and ontology services into a single GraphQL API.

3. Integration Framework

3.1 Architectural Overview

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subgraph Ontology_Service

A1[SNOMED CT, LOINC, ICD-10 OWL] --> A2(Blazegraph/SPARQL)

A1 --> A3(Neo4j Graph DB)

end

subgraph FHIR_Backend

B1[Conformant FHIR Servers] --> B2(REST API)

B1 --> B3(GraphQL Interface)

end

subgraph GraphQL_Gateway

C1[Apollo Federation] --> C2(GraphQL-Mesh)

C2 --> A2

C2 --> B3

C2 --> B2

end

subgraph Client_Apps

D1[Web/Mobile UIs] --> C1

end

The Ontology_Service maintains terminologies with SPARQL for rich semantic queries and Neo4j for hierarchical traversals. The FHIR_Backend provides REST/GraphQL endpoints. The GraphQL_Gateway uses Apollo Federation to compose schemas and GraphQL-Mesh to transform GraphQL queries into FHIR and SPARQL calls.

3.2 Ontology Service

Storage: Blazegraph for RDF/OWL triples; Neo4j for graph algorithms and path traversals.

Access: SPARQL endpoints for fine-grained semantic queries; Cypher for hierarchy exploration.

3.3 GraphQL Gateway & Schema Federation

Apollo Server: Defines federated subgraphs for Patient, Observation, and CodeSystem types.

GraphQL-Mesh: Connects multiple data sources (FHIR REST, FHIR GraphQL, SPARQL) under a unified GraphQL schema.

Resolvers:

For Patient.observations, Mesh maps to FHIR _include=Observation:patient.

For CodeSystem.concepts, Mesh queries Blazegraph SPARQL with concept filters.

3.4 Implementation Details

Language: Node.js (v18) with TypeScript

Server: Apollo Server 4, Express.js middleware

Mesh Plugins: @graphql-mesh/openapi, @graphql-mesh/odata, @graphql-mesh/neo4j

Deployment: Docker containers orchestrated by Kubernetes (EKS/AKS/GKE) with Helm; Istio for service mesh.

Security: OAuth2/OIDC via Keycloak; FHIR scope enforcement; TLS 1.3; audit logging to ELK Stack.

4. Case Studies

4.1 Heterogeneous EHR Integration

An academic consortium integrated two vendor EHRs (Epic, Cerner) using the framework. A single GraphQL query retrieved Patient { id, name, observations(code: 8302-2) { value, effectiveDateTime } } across both systems, with LOINC code resolution via the ontology service

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4.2 Data Element Repository Access

A national registry exposed its data-element repository (MDR) via SPARQL. Using GraphQL-Mesh, custom queries like dataElement(id: gender) { label, definition, dataType } returned unified metadata from multiple MDRs, enabling dynamic form generation in a clinical trial platform

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5. Discussion

5.1 Benefits

Client-Driven Data Shaping: GraphQL allows UIs to request exactly needed fields, reducing payloads.

Semantic Fidelity: Ontology service ensures code lookups, hierarchy navigation, and value-set expansions are uniform.

Extensibility: New subgraphs (e.g., genomic data) can be federated without changing client code.

5.2 Challenges

Performance: Nested GraphQL queries may spawn multiple FHIR/SPARQL calls; mitigated via DataLoader batching and caching.

Schema Evolution: Ontology updates and FHIR version changes require coordinated schema migrations.

Security: Fine-grained authorization at field-level necessitates comprehensive policy-as-code (OPA) enforcement

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6. Best Practices & Future Directions

Schema Versioning: Tag federated subgraphs by FHIR and ontology release versions to avoid breaking changes.

Query Cost Analysis: Employ depth limits and complexity analysis to prevent expensive queries.

AI-Augmented Resolvers: Integrate transformer models (e.g., ClinicalBERT) to normalize free-text input into FHIR search parameters and suggest code mappings. Use a hybrid RAG (Retrieval-Augmented Generation) approach for user-friendly interfaces

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Monitoring & Observability: Track end-to-end latency, error rates, and semantic correctness via Grafana dashboards and custom SPARQL test suites.

7. Conclusion

By marrying medical ontologies with a GraphQL federation over HL7 FHIR, our framework delivers semantically coherent, flexible healthcare data access. It streamlines cross-vendor EHR integration, supports dynamic metadata repositories, and lays the groundwork for AI-driven clinical applications. The open-source implementation is available on GitHub for community adoption and extension.

References

HL7 FHIR Specification. Using GraphQL with FHIR.

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Salgado, R. et al. Semantic interoperability in Electronic Health Records: systematic literature review. Int. J. Med. Inform. (2022).

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Gutiérrez, D. et al. Using metadata to support integration of healthcare instance data. Res. Gatew. (2019).

ResearchGate

bluehalo/graphql-fhir. A secure GraphQL implementation for the HL7 FHIR specification. GitHub.

GitHub

Microsoft. Graphir: GraphQL interface over FHIR API. GitHub.

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Nahiduzzaman, M. et al. Architecture for semantic interoperability between heterogeneous EHRs using GraphQL and HL7 FHIR. Health Inform. J. (2021).

PubMed

Zhang, Y. et al. Toward better semantic interoperability of data elements in healthcare using repositories. J. Med. Internet Res. (2024).

JMI - JMIR Medical Informatics

The Guild. A FHIR to GraphQL plugin for unopinionated schema mapping.

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Alsentzer, E. et al. Bio_ClinicalBERT: Pretrained clinical language models. Hugging Face.

Hugging Face

Tsang, S. ClinicalBERT embeddings review. Medium.