A Semantic Ontology-Driven Architecture for Personalized Health Insurance Assignment in Smart Healthcare Ecosystems

The growing complexity of healthcare systems and the surge in demand for personalized patient care have exposed the limitations of traditional insurance models, which remain largely static, reactive, and disconnected from real-time clinical data. This study presents a novel, ontology-driven health insurance framework that leverages semantic reasoning to deliver dynamically adaptive, context-aware insurance policy recommendations based on Electronic Health Records (EHR), health status, and clinical diagnoses. The proposed architecture integrates Internet of Things (IoT) devices, fog computing infrastructure, and cloud-based data repositories within a modular, multi-layered system design. Central to the framework is an OWL-based ontology that formalizes the relationships between patient attributes and policy components, enhanced with Semantic Web Rule Language (SWRL) for inferencing and SPARQL for semantic querying. A prototype implementation was developed using Protégé, Apache Jena, and the Pellet reasoner, and evaluated on five representative patient scenarios. Results demonstrate sub-second policy inference time, high semantic accuracy, and the ability to construct composite insurance packages aligned with individual clinical profiles. This approach not only improves interoperability and policy automation but also supports regulatory traceability and patient-centric service delivery. The findings underscore the potential of semantic technologies to revolutionize value-based insurance systems through intelligent decision-making, real-time policy customization, and scalable integration across heterogeneous healthcare domains.