Construction of Medical Knowledge Graphs: In-depth Analysis of the Hospital Management Ontology Design Pattern (HM-ODP)

This article will conduct an in-depth analysis of the Hospital Management Ontology Design Pattern (HM-ODP), exploring how it models the complex ecosystem of medical systems through ontology to address the semantic gap in healthcare informatization and provide a standardized knowledge representation foundation for AI healthcare applications. HM-ODP fills the gap in existing medical terminology sets regarding coverage of hospital management processes, supporting multi-scenario applications such as data integration and intelligent decision-making.

The healthcare industry is data-intensive, but data is often trapped in information silos with inconsistent formats and ambiguous semantics. Traditional databases lack precise descriptions of domain concepts and definitions of relationships—for example, the term 'patient' has different meanings in registration, pharmacy, and financial systems—hindering data interconnection and limiting the deep application of AI.

HM-ODP focuses on semantic modeling in the hospital management domain. As a design pattern, it not only provides ontology definitions but also demonstrates a systematic method for building knowledge graphs. Its core objectives include: 1. Establishing a unified terminology system; 2. Defining semantic associations between concepts; 3. Supporting description logic reasoning; 4. Providing a methodology for data source mapping.

HM-ODP constructs a hierarchical concept system covering four major dimensions:

• Organizational Dimension: Refined modeling of institutions (hospitals/departments), roles (doctors/patients), and positions (chief physicians/head nurses);
• Resource Dimension: Classified definitions of human resources (skills/scheduling), equipment (status/maintenance), and materials (inventory/supply chain);
• Process Dimension: Clinical (outpatient/surgery) and management (personnel/finance) processes and key events;
• Patient Dimension: Demographic, medical, service information, and privacy rules.

HM-ODP is based on W3C semantic web standards:

• OWL2: Used for class and property definitions and reasoning rules (e.g., subclass inheritance);
• RDF: Stores instance data as triples, forming the foundation of knowledge graphs;
• SPARQL: Supports complex association queries (e.g., 'Patients who underwent cardiac surgery in 2024 and were operated on by chief physicians').

The application scenarios of HM-ODP include:

• Data Integration: Mapping data from heterogeneous systems to a unified ontology to eliminate barriers;
• Intelligent Decision-Making: Resource optimization, risk early warning, and compliance checks;
• LLM Enhancement: Retrieval-augmented generation, structured output, and fact verification;
• Research Quality: Cohort studies, indicator calculation, and trend analysis.

When implementing HM-ODP, attention should be paid to:

• Domain Expert Participation: Ensure the ontology reflects real clinical practices;
• Progressive Evolution: Modular design, with iterative expansion after core stability;
• Standard Alignment: Mapping with SNOMED CT, HL7 FHIR, etc.;
• Tool Support: Using Protégé for modeling, and Apache Jena/GraphDB for storage and reasoning.

HM-ODP is an important direction for the evolution of healthcare informatization towards semanticization and intelligence, laying the foundation for breaking information silos and improving medical quality. In the future, its integration with LLMs and knowledge graphs will promote the development of medical AI from an auxiliary tool to an intelligent partner, playing a greater role in diagnosis, treatment, and operations.