The PLOS One protocol examines how family physicians perceive large language model (LLM) adoption in clinical practice, and the primary keyword for this post is "LLM adoption in primary care". Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. According to the PLOS One protocol, primary care physicians in British Columbia will be interviewed using a theory-driven semi-structured guide to surface perceptions, barriers, and facilitators relevant to clinical LLM use, a payoff that helps researchers design safer implementation policies.
Key Takeaways
According to the PLOS One protocol, the study aims to describe how primary care physicians perceive LLM adoption in clinical practice using semi-structured interviews and the Technology Adoption Behavior (TAB) framework (PLOS One).
- The PLOS One protocol was published on August 7, 2026, and the authors report the study design will use semi-structured interviews and thematic analysis (Pokharel et al., PLOS One, 2026).
- The protocol sets an anticipated recruitment and data collection window from August 1, 2025 to January 31, 2026, according to PLOS One.
- The protocol classifies physician LLM experience into three buckets: 50+ uses, 5–50 uses, and fewer than 5 uses in clinical contexts, reported in the PLOS One methods (Pokharel et al., 2026).
- The protocol cites that the number of Canadians without a family doctor rose from 4.5 million in 2019 to about 6.5 million in 2023, framing primary care urgency (PLOS One, 2026).
- The protocol states, "The study may provide timely input into relevant policies, practices, and behaviors for policymakers, health professionals, and patients, " attributing the authors' stated dissemination goals (Pokharel et al., PLOS One, 2026).
What Happened and How the Study Works
According to the PLOS One protocol, the authors developed a qualitative descriptive study using purposive sampling of practicing family physicians in British Columbia to capture a range of LLM experience levels.
According to the PLOS One protocol, recruitment will seek physicians with frequent (50+ uses), occasional (5–50 uses), and rare (<5 uses) exposure to LLMs to reach informational sufficiency, and interviews will be audio-recorded, transcribed verbatim, and deidentified.
According to the PLOS One protocol, the study uses the Technology Adoption Behavior (TAB) framework that integrates perceptions (perceived usefulness and ease of use) and task–technology fit to guide both data collection and deductive coding.
According to the PLOS One protocol, the research team plans to combine deductive codes from TAB with inductive codes from the data, use reflexive memos, and analyze the dataset in NVivo to generate themes.
Findings Snapshot
| Date / Timeline | Metric | Value / Detail | Implication |
|---|---|---|---|
| August 7, 2026 | Publication | Protocol published (Pokharel et al., PLOS One) | Signals peer-reviewed, citable study design for later results |
| July 18, 2025 → July 22, 2026 | Manuscript lifecycle | Received July 18, 2025; Accepted July 22, 2026 | Documented development and peer-review timeframe |
| Aug 1, 2025 → Jan 31, 2026 | Planned data collection | Anticipated 6-month recruitment and interview window | Enables replication timelines for comparative studies |
| 2019 → 2023 | Primary care access statistic | Patients without a family doctor: 4.5M (2019) → ~6.5M (2023) | Positions LLM uptake discussions within access and workload pressures |
| Usage thresholds | LLM experience groups | Frequent: 50+; Occasional: 5–50; Rare: <5 lifetime uses in healthcare | Supports stratified, cross-segment qualitative analysis |
Implications for Qualitative Researchers and Health Researchers
According to the PLOS One protocol, using an explicit theoretical framework like TAB improves study rigor by mapping interview prompts to constructs that inform adoption behavior.
According to the PLOS One protocol, purposive sampling across predefined experience bands (50+, 5–50, <5) enables cross-segment thematic comparison, which qualitative researchers can adopt to surface contrasts in perceived usefulness and ease of use.
According to the PLOS One protocol, reflexive memos and multidisciplinary coding teams are recommended to manage interpretive bias and to refine codes iteratively, a practice that strengthens trustworthiness in qualitative health research.
How Evidano Helps
Problem: Theory-driven interviews produce large, complex text corpora
Solution: Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
According to Evidano tools, thematic analysis workflows can be accelerated by automated transcription, deidentification, and initial deductive code tagging to match TAB constructs, which preserves researchers' capacity to perform reflexive interpretation.
Problem: Cross-segment comparisons are time consuming
Solution: Evidano supports cross-segment frequency and co-occurrence analytics to quantify theme prevalence across the PLOS One protocol’s experience bands (50+, 5–50, <5), enabling transparent comparisons tied to the study’s design.
According to Evidano features, researchers can export codebooks and visualizations that align with NVivo-style outputs while reducing manual coding time, supporting the iterative codebook refinement described in the PLOS One protocol (Evidano features).
Problem: Secure handling of identifiable audio and transcripts
Solution: Evidano provides encrypted transcription with PII redaction to match the PLOS One protocol’s ethics measures for destroying original audio and working with deidentified transcripts.
According to Evidano documentation, the platform keeps data private and does not use uploaded content to train third-party models, which supports ethical requirements in clinical qualitative research.
FAQ: llm adoption in primary care
What is the PLOS One protocol about and who conducted it?
Answer: The PLOS One protocol describes a qualitative study protocol to explore family physicians' perceptions of LLM adoption in clinical practice, authored by Pokharel, Hsu, Hedden, Nimmon, Bloom, and Tsuei (PLOS One, 2026).
According to PLOS One, the multi-author team developed the Technology Adoption Behavior (TAB) framework to guide both data collection and deductive coding.
How will participants be sampled and categorized?
Answer: Participants will be purposively sampled in British Columbia and categorized by LLM experience: frequent (50+ uses), occasional (5–50 uses), and rare (<5 uses), according to the PLOS One protocol.
According to PLOS One, recruitment will target variation in years of practice and LLM exposure to reach informational sufficiency.
What analysis approach will the study use?
Answer: The study will use thematic analysis combining deductive codes from the TAB framework and inductive codes emerging from interviews, as stated in the PLOS One protocol.
According to PLOS One, coding will be iterative with reflexive memos and team discussions, and the team will use NVivo for analysis.
Can AI-enabled qualitative tools speed this kind of research?
Answer: Yes, AI-enabled qualitative tools can speed transcription, initial code-suggestion, and cross-segment frequency analysis while preserving researcher-led interpretation.
According to recent practice and the PLOS One protocol, automating routine steps such as verbatim transcription and first-pass code tagging frees researchers to focus on reflexivity, theme development, and policy translation.
Conclusion & Next Steps
According to the PLOS One protocol, the authors expect the study to produce actionable insights for policymakers, clinicians, and researchers on safe LLM integration in primary care.
According to Pokharel et al., PLOS One (2026), the protocol will guide interviews and thematic analysis that can inform policy and practice at local and provincial levels.
If you are preparing a qualitative study on AI adoption or need to analyze interview transcripts against a theoretical framework like TAB, Evidano can accelerate transcription, deidentification, thematic coding, and cross-segment analysis.
To try these workflows yourself, Try Evidano for free.
Topics
- llm adoption in primary care
- physician perceptions of llms
- qualitative analysis of llm adoption
- technology adoption behavior framework
- ai qualitative research tools
Keep reading
- Commentary on NewsStudy Protocol: Physician Perceptions of LLMsReview of a PLOS One protocol on primary care physician perceptions of LLMs, with dates, sample metrics, and AI-enabled qualitative research guidance.
- Commentary on NewsPhysician Perceptions of LLMs: Adoption in Primary CarePractical explainer of physician perceptions of LLMs from a PLOS One protocol (7 Aug 2026). Learn methods, timelines, and how AI-enabled qualitative research accelerates insight.
- 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.