ArticlePloS one2026
Understanding primary care physicians' perceptions of large language model adoption in clinical practice: A qualitative research protocol leveraging the technology adoption behavior framework.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
6 authors.
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Abstract
introductionLarge language models' (LLMs') rapid evolution and intersection with diverse groups and institutions require up-to-date policies, practices, and behaviors to ensure safe and effective implementation. Because physicians play a central role in care provision and face the dual mandate of embracing innovations and safeguarding patient welfare, understanding physicians' views on LLMs can elucidate the complex interplay of technical, ethical, and professional considerations influencing LLM adoption. METHODS AND ANALYSIS: This qualitative study aims to employ a descriptive qualitative design to explore how primary care physicians perceive the adoption of LLMs in the context of their clinical practice. We plan to use semi-structured interviews with purposively sampled primary care physicians from British Columbia, Canada. The data collection will draw on the technology adoption behavior framework, a novel model that integrates the most advanced theories of technological uptake. We expect to use thematic analysis drawing on deductive and inductive approaches to describe physicians' perceptions. The multidisciplinary research team will prepare and conduct reflexive memos and discussions to ensure nuanced interpretations. DISSEMINATION: We aim to disseminate the findings through peer-reviewed journals, professional organizations, and policymaker briefings to support the development of policies, practices, and behaviors that support safe and effective LLM integration into health care. STRENGTHS AND LIMITATIONS: The study may provide timely input into relevant policies, practices, and behaviors for policymakers, health professionals, and patients around the use of large language models in health care services.This study uses the technology adoption behavior framework to guide the design of data collection and analysis.The semi-structured interview may reveal the interviewees' internal views but limits the variety of insights gleaned.
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