What question did this study set out to answer?

Identify key determinants governing the adoption of large language models across healthcare settings by establishing an expanded sociotechnical readiness framework.

October 15, 2025SHILAP Revista de lepidopterologíaOpen Access

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

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Identify key determinants governing the adoption of large language models across healthcare settings by establishing an expanded sociotechnical readiness framework.
Conducted a multicenter, cross-sectional, mixed methods observational study across diverse healthcare user groups.
Integrated constructs of ethical trust, digital literacy, and institutional support into a unified predictive adoption framework.
Trust and perceived usefulness exerted a greater influence on adoption than demographic or structural variables.
The sociotechnical model achieved a predictive accuracy exceeding 0.9 across all evaluated user cohorts.

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Implication

Multicenter observational study reveals trust and perceived usefulness drive AI adoption in healthcare, indicating sociotechnical readiness outweighs pure algorithmic performance.

Cite This Study

Yang et al. (2025) studied this question.

synapsesocial.com/papers/69d9abb600ab073a27837289 https://doi.org/10.2196/84918

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Implication

Multicenter observational study reveals trust and perceived usefulness drive AI adoption in healthcare, indicating sociotechnical readiness outweighs pure algorithmic performance.

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

Key Points

Discussion

Implication

Cite This Study

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

Key Points

Discussion

Implication

Cite This Study

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