Designing an Ontology for a Smart Learning Health System Framework

Area of Science:

• Health Informatics
• Knowledge Engineering
• Ontology Development

Background:

• Existing Learning Health System (LHS) frameworks lack standardization and adaptability.
• Healthcare leaders face challenges adopting appropriate LHS frameworks for transformation.
• Current frameworks struggle with sociotechnical changes and performance evaluation initiatives.

Purpose of the Study:

• To design a standardized Smart Learning Health System Ontology (SMARTLHS).
• To address limitations in existing LHS frameworks.
• To facilitate consensus and collaboration through a shared vocabulary.

Main Methods:

• A four-step realist synthesis methodology was employed.
• Steps included ontology requirements specification, conceptualization, and refinement.
• Ontology evaluation ensured its validity and applicability.

Main Results:

• The SMARTLHS ontology includes 475 unique classes and 135 object properties.
• SMARTLHS defines clear relationships between concepts within a learning health system.
• The ontology addresses critical limitations of prior LHS frameworks.

Conclusions:

• SMARTLHS provides a standardized knowledge base for healthcare professionals.
• The ontology can enhance healthcare organizational performance evaluation.
• SMARTLHS is suitable for integration with AI applications like Retrieval Augmented Generation (RAG) and large language models.