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

Abstract

Background: Large language models (LLMs) such as ChatGPT are transforming how health information is accessed, communicated, and applied. However, their adoption in health care remains limited by uncertainties surrounding trust, privacy, and digital readiness, particularly in low- and middle-income contexts. Objective: This study aimed to examine how trust, information behavior, and sociotechnical readiness influence the willingness of health care professionals (HCPs) and patients or caregivers (PCs) in China to adopt LLMs for medical information and decision support. Methods: We conducted a multicenter, cross-sectional mixed methods observational study across five tertiary hospitals, combining surveys of 240 HCPs and 480 PCs with semistructured interviews (n=30). Quantitative analyses included logistic regression (LR), random forest (RF), and extreme gradient boosting models with Shapley additive explanations-based interpretability. Qualitative data were thematically analyzed to capture role-specific concerns and expectations. Results:: Among HCPs, mean age 39.9 (SD 6.5 years; 159/240, 66.2% physicians), 69.2% (166/240) were aware of LLMs and 36.7% (88/240) had previous experience. Among PCs (mean age 50.1, SD 12.6 years; 242/480, 50.4% male), only 26% (125/480) had previous exposure. Trust, perceived usefulness, and digital readiness were the strongest facilitators of adoption. Multivariable models identified trust as the dominant predictor for both groups (HCPs: odds ratio [OR] 3.78, 95% CI 2.15‐6.63; PCs: OR 36.34, 95% CI 18.41‐71.74; P