This post explains the PLOS ONE qualitative research protocol on primary care physicians and large language model (LLM) adoption, and shows how AI-enabled qualitative analysis can accelerate and deepen that work. The primary keyword for this piece is "qualitative analysis of LLM adoption." According to the PLOS ONE study, the protocol targets family physicians in British Columbia and uses semi-structured interviews guided by the new Technology Adoption Behavior (TAB) framework to map perceptions, barriers, and facilitators (PLOS ONE). Researchers and UX teams can use this summary to plan sampling, coding, and synthesis with AI tools that preserve ethics and interpretive rigor.
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- qualitative analysis of LLM adoption
- LLM adoption qualitative research
- physicians perceptions of LLMs
- AI-enabled qualitative analysis
Run this analysis on your own data
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Keep reading
- Commentary on NewsPhysicians and LLMs: Qualitative analysis of LLM adoptionHow primary care physicians view LLMs and how to run qualitative analysis of LLM adoption: methods, timelines, direct quotes, and tools to act with Evidano.
- Commentary on NewsPhysician Views: Qualitative Analysis of LLM AdoptionPLOS ONE 2026 protocol on family physicians' views of LLMs, with practical guidance for AI-enabled qualitative research and tools to speed thematic synthesis.
- Commentary on NewsAdopting LLMs: Qualitative research on physician viewsHow to run LLM adoption qualitative research with physician interviews and the TAB framework, including methods, timelines, and AI-enabled analysis options. Learn practical next steps.
