Ontology-based health data modeling for semantic interoperability in the Nigerian healthcare system

Healthcare data fragmentation and lack of interoperability remain critical challenges in Nigeria, limiting efficient data exchange and evidence-based clinical decision-making. This study proposes a context-aware ontology-based health data model to enhance semantic interoperability across heterogeneous healthcare systems.
A Design Science Research (DSR) methodology is adopted to develop a domain-specific ontology that formally captures healthcare concepts, relationships, and constraints using Semantic Web technologies. The model integrates standardized medical terminologies (e.g., SNOMED CT and ICD-10) and introduces a formal data mapping function for consistent semantic interpretation. Additionally, a semantic similarity measure is incorporated to improve concept alignment and support intelligent querying.
The proposed ontology is implemented within a simulated Electronic Health Record (EHR) environment and evaluated using interoperability performance metrics. Results demonstrate a significant improvement in interoperability, with the Semantic Interoperability Score increasing from 0.55 to 0.87, representing over 60% enhancement compared to traditional models.
Findings confirm that ontology-driven approaches effectively reduce data ambiguity, enhance semantic consistency, and support scalable healthcare system integration. This study contributes a practical and scalable interoperability framework tailored to resource-constrained environments, with direct implications for improving healthcare data management and decision support in Nigeria.