Designing an Ontology for a Smart Learning Health System Framework

The concept of a "Learning Health System Framework" has been defined in various forms, making it difficult for healthcare leaders to adopt an appropriate framework for organisational transformation. There is a pressing need to standardise its components and relationships. Limitations of the existing LHS frameworks include their lack of flexibility to adapt to sociotechnical changes and inability to guide the automatic evaluation initiative on health organisational performance. Ontologies facilitate consensus by aligning stakeholders on a shared vocabulary and structure, which can reduce ambiguity in communication and foster better collaboration across different stakeholder groups. Therefore, this study designed a Smart Learning Health System Ontology, abbreviated as "SMARTLHS". We followed a four-step realist synthesis methodology to develop SMARTLHS: (1) ontology requirements specification, (2) iterative and inductive ontology conceptualisation, (3) concept and relationship comparison and development, (4) ontology evaluation and refinement. The resultant SMARTLHS ontology comprises 475 unique classes and subclasses (concepts) and 135 object properties. The object properties define the relationships between these classes. SMARTLHS addresses the critical limitations of the existing LHS frameworks. It serves as a knowledge base for healthcare professionals. It can also be integrated into Retrieval Augmented Generative (RAG) applications and feed into large language models such as GPT 4o for advanced exploration of healthcare organisations' performance in developing learning health systems.