Ontology-driven generation of parameters for health technology assessment models: A prompt engineering study

Objective:Ontologies support transparent and reproducible conceptual modeling in Health Technology Assessment (HTA), but their population remains resource-intensive and reliant on expert input.This study evaluates the feasibility, reliability, and methodological implications of using generative artificial intelligence (GenAI) to populate ontology individuals for HTA applications.Methods: A factorial experimental framework was developed using the Ontology for Simulation Modeling (OSDi) and three HTA-relevant use cases of varying complexity.Two GenAI systems were evaluated under multiple experimental conditions, including prompting strategy, serialization format, and provision of supporting information.Generated ontology individuals were validated by an HTA expert and assessed across four quality dimensions: consistency, relevance, completeness, and adequacy.Multivariate and regression analyses were conducted to examine the effects of experimental factors on quality outcomes and hallucination likelihood.