Primary care teams and qualitative researchers need practical methods to study how physicians adopt large language models. The primary keyword for this guide is qualitative analysis of LLM adoption, and this post translates a detailed protocol into actionable steps for researchers and UX teams. According to the PLOS One study published on August 7, 2026, the protocol uses semi-structured interviews, purposive sampling, and thematic analysis anchored to a new Technology Adoption Behavior (TAB) framework to explore family physicians' views in British Columbia.
Key Takeaways
This post summarizes the PLOS One protocol and shows how AI-enabled qualitative research can accelerate and scale thematic insights on physician adoption of LLMs. PLOS One is the source for the protocol and timeline below.
- Pokharel et al., PLOS One (published August 7, 2026) plan semi-structured interviews with purposive sampling across LLM usage strata (50+, 5–50, <5 uses) and analysis using the TAB framework.
- The PLOS One protocol reports that the number of Canadians without a family doctor rose from 4.5 million in 2019 to about 6.5 million in 2023, motivating the focus on primary care (Pokharel et al., 2026).
- Recruitment in the protocol was planned between August 1, 2025 and January 31, 2026, with the team expecting results within a year after data collection begins (Pokharel et al., 2026).
- The authors note, “The study may provide timely input into relevant policies, practices, and behaviors, ” and they commit to thematic analysis using both deductive and inductive coding (Pokharel et al., 2026).
- The protocol will use NVivo for coding and a multidisciplinary reflexive team to maintain analytic rigor (Pokharel et al., 2026).
What happened and how the study works
Answer: Pokharel et al. published a qualitative study protocol in PLOS One on August 7, 2026 that outlines how to study primary care physicians' perceptions of LLM adoption.
According to Pokharel et al., PLOS One (2026), the protocol uses a descriptive qualitative design with semi-structured interviews, purposive sampling in British Columbia, and the Technology Adoption Behavior (TAB) framework to guide questions and coding.
According to Pokharel et al., PLOS One (2026), interviewees will be sampled across three LLM-usage strata defined as 50+ uses, 5–50 uses, and fewer than 5 uses in clinical contexts, and interviews will be recorded, transcribed, and deidentified before analysis.
According to Pokharel et al., PLOS One (2026), the team will combine deductive codes derived from the TAB framework with inductive codes from the data, run iterative reflexive memos, and finalize themes with monthly team reviews.
Findings snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| August 7, 2026 | Publication | PLOS One | Protocol available for replication and adaptation |
| 2019 | Canadians without a family doctor | 4.5 million | Motivates studying primary care adoption (Pokharel et al., 2026) |
| 2023 | Canadians without a family doctor | 6.5 million | Rising access gap underpins the study focus (Pokharel et al., 2026) |
| Aug 1, 2025 to Jan 31, 2026 | Planned recruitment window | 6 months | Sample sufficiency aimed via purposive strata |
| Study tools | Analysis software | NVivo | Structured coding and team reflexivity planned (Pokharel et al., 2026) |
Implications for qualitative researchers and UX teams
Answer: The PLOS One protocol shows three practical implications for researchers designing qualitative studies of LLM adoption.
First, Pokharel et al., PLOS One (2026) demonstrate that sampling across usage strata (frequent, occasional, rare) clarifies differences between hands-on users and skeptics; researchers should predefine strata such as 50+, 5–50, and <5 uses when relevant.
Second, Pokharel et al., PLOS One (2026) use the Technology Adoption Behavior (TAB) framework to combine perceived ease of use, perceived usefulness, and task-technology fit; UX teams should adapt TAB or an equivalent theoretical scaffold to keep coding deductively aligned with implementation levers.
Third, Pokharel et al., PLOS One (2026) plan reflexive memos and multidisciplinary team reviews to improve trustworthiness; qualitative teams should schedule recurring codebook reviews and log intersubjective decisions.
How Evidano helps: map problems to AI-enabled qualitative features
Problem: long manual transcription and delayed coding
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Solution: Use Evidano’s transcription workflow with custom dictionaries and PII redaction to turn interview audio into deidentified text faster than manual transcription, matching Pokharel et al.’s step of transcribing and deidentifying recordings before NVivo analysis.
Relevant link: See our Speech-to-Text page for details on accuracy and PII controls.
Problem: coding consistency across deductive (TAB) and inductive codes
Solution: Evidano auto-suggests thematic codes from your transcripts and supports hierarchical codes and subcodes so teams can mirror Pokharel et al.’s deductive TAB codes and then iteratively add inductive codes.
Relevant link: See Features for thematic extraction, code management, and visualizations.
Problem: rapid synthesis for policymakers and clinicians
Solution: Evidano generates thematic summaries, frequency counts, and cross-segment analyses (by usage strata, years of practice, and other metadata) so researchers can deliver the policy-relevant outputs Pokharel et al., PLOS One (2026) aim to disseminate.
Solution: Evidano’s AI chat over your documents lets non-technical stakeholders ask focused questions of the data and extract quotable evidence efficiently.
FAQ: qualitative analysis of LLM adoption
How many physicians should I plan to interview for a study like this?
Answer: Plan purposive sampling until thematic sufficiency, not a fixed numeric target.
Supporting detail: Pokharel et al., PLOS One (2026) recommend purposive sampling across LLM-usage strata and aiming for sufficiency, citing literature that treats interview-based sample size as guided by thematic depth rather than a single number.
Which theoretical framework should guide coding of physician perceptions of LLMs?
Answer: Use a framework that combines perceptions and task-technology fit, such as the TAB framework used by Pokharel et al., PLOS One (2026).
Supporting detail: The TAB framework integrates perceived ease of use, perceived usefulness, and task-environment fit to generate deductive codes that map to implementation levers.
Can AI tools reliably accelerate deductive and inductive thematic analysis?
Answer: Yes, when AI tools are combined with human reflexivity and validation steps.
Supporting detail: Pokharel et al., PLOS One (2026) plan human-led coding with NVivo and reflexive memos; Evidano complements that workflow by extracting candidate codes and producing frequency and cross-segment analyses that researchers then validate.
What practical timeline should I expect from protocol to publishable results?
Answer: Expect about 6 months for recruitment and up to a year for full analysis and write-up, depending on scope.
Supporting detail: Pokharel et al., PLOS One (2026) planned recruitment from August 1, 2025 to January 31, 2026 and expected results within a year of starting data collection.
Conclusion & Next Steps
Recap: The PLOS One protocol from August 7, 2026 provides a concrete blueprint for qualitative analysis of LLM adoption in primary care using semi-structured interviews, purposive strata, the TAB framework, and combined deductive and inductive coding.
Next step: replicate the protocol’s sampling strata and coding discipline while using AI-enabled tools to speed transcription, code suggestion, and cross-segment counts.
If you want to pilot the workflow described here, Try Evidano for free to transcribe, code, and generate thematic summaries that map directly to implementation questions and policy briefs.
Topics
- qualitative analysis of LLM adoption
- LLM adoption qualitative study
- physician perceptions LLM
- AI-enabled qualitative research
Keep reading
- 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 NewsAI-enabled Qualitative Analysis: LLM Adoption in Primary CarePractical guide to qualitative analysis of LLM adoption in primary care using the PLOS ONE protocol (Aug 7, 2026). Methods, numbers, and AI-enabled research tools explained.
- Commentary on NewsPhysician Perceptions of LLMs: Qualitative AnalysisWhat primary care physicians think about LLM adoption and how AI-enabled qualitative research can speed insight. Learn methodological takeaways and next steps.