Factors Influencing Adoption of Large Language Models in Health Care: Multicenter Cross-Sectional Mixed Methods Observational Study

Abstract

Adoption of LLMs in health care depends less on algorithmic performance than on the management of trust, literacy, and institutional readiness. Trust functions as a multidimensional construct rooted in transparency, reliability, and contextual validation. Theoretically, this study extends technology adoption frameworks by embedding ethical trust, digital literacy, and institutional support within a unified sociotechnical readiness model, advancing information management theory beyond performance-centric paradigms. Empirically, trust and perceived usefulness outweighed demographic or structural factors, with predictive accuracy exceeding 0.9 across user groups. Practically, these findings offer actionable guidance for the design and governance of artificial intelligence systems, emphasizing role-sensitive interfaces, plain-language communication, and transparent accountability mechanisms to promote equitable and trustworthy adoption.

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