Knowledge Graphs in Healthcare Technologies for Knowledge Sharing, Traceability, and Interoperability: A Hospital Pharmaceutical Logistics Case Study

Healthcare technologies increasingly depend on the ability to connect fragmented data, ensure traceability, and support knowledge sharing across clinical and operational environments. Electronic health records, laboratory systems, imaging platforms, and pharmaceutical supply chains often operate as isolated data silos, which limits interoperability and reduces the value of accumulated expertise. Knowledge graphs (KGs) offer a semantic, machine-readable approach for organizing healthcare knowledge as structured, connected representations that can be queried and reasoned over. This paper positions KGs as a semantic integration layer for healthcare technologies and examines their contribution to three priorities: knowledge sharing, traceability, and semantic interoperability. The approach is demonstrated through a hospital pharmaceutical logistics case study set in an Algerian hospital environment, using HCDTLO (Hospital Critical Drug Traceability and Logistics Ontology), an OWL ontology implemented in Protégé and instantiated as a knowledge graph. Three SQWRL-based reasoning tasks were evaluated: shortage detection, priority-based allocation, and end-to-end traceability. The results show that the knowledge graph correctly identifies drug items below minimum stock thresholds, including Ceftriaxone and Midazolam, aligns allocation with ward priority levels such as the Intensive Care Unit, and links each drug item to its batch, supplier, and storage location. These functional demonstrations illustrate how ontology-based knowledge graphs support knowledge sharing, traceability, and interoperability in hospital drug logistics, and provide a proof-of-concept basis for broader healthcare knowledge graph applications.