Identifying and Appraising Knowledge Graphs for Hospital Information Systems

Hospital information systems (HIS) collect and store large amounts of clinical, administrative, and operational data from many sources. However, integrating these data across departments and making them interoperable remain difficult. Recent research has turned to knowledge graphs (KGs), which organize different types of healthcare data semantically and can support clinical decision-making, workflow optimization, and data exchange between systems. This article identifies and appraises existing KG applications in HIS on the basis of recent peer-reviewed literature. It surveys the implementation frameworks, ontological models, and integration architectures that researchers have adopted in hospitals, and it evaluates how well they address semantic heterogeneity, data silos, and real-time decision-making. The findings show that KGs have great potential in hospitals, but several barriers stand in the way of their adoption in the health information ecosystem, among them limited scalability, the lack of standardization of operational data, and low institutional readiness.