ArticleJAMIA open2025
Leveraging ChatGPT for thematic analysis of medical best practice advisory data.
Article in JAMIA open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Article
- The Patients' Voice inmedRxiv : the preprint server for health sciences · 2026Article
- Multimodule Human-Artificial Intelligence Collaboration Pipeline for Large Language Model-Assisted Thematic Analysis Across Digital Health Interview Studies: Comparative Evaluation Study.JMIR medical informatics · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: To evaluate ChatGPT's ability to perform thematic analysis of medical Best Practice Advisory (BPA) free-text comments and identify prompt engineering strategies that optimize performance. Materials and Methods: We analyzed 778 BPA comments from a pilot AI-enabled clinical deterioration intervention at Stanford Hospital, categorized as reasons for deterioration (Category 1) and care team actions (Category 2). Prompt engineering strategies (role, context specification, stepwise instructions, few-shot prompting, and dialogue-based calibration) were tested on a 20% random subsample to determine the best-performing prompt. Using that prompt, ChatGPT conducted deductive coding on the full dataset followed by inductive analysis. Agreement with human coding was assessed as inter-rater reliability (IRR) using Cohen's Kappa (κ). Results: With structured prompts and calibration, ChatGPT achieved substantial agreement with human coding (κ = 0.76 for Category 1; κ = 0.78 for Category 2). Baseline agreement was higher for Category 1 than Category 2, reflecting differences in comment type and complexity, but calibration improved both. Inductive analysis yielded 9 themes, with ChatGPT-generated themes closely aligning with human coding. Discussion: ChatGPT can accelerate qualitative analysis, but its rigor depends heavily on prompt engineering. Key strategies included role and context specification, pulse-check calibration, and safeguard techniques, which enhanced reliability and reproducibility. Conclusion: This study demonstrates the feasibility of ChatGPT-assisted thematic analysis and introduces a structured approach for applying LLMs to qualitative analysis of clinical free-text data, underscoring prompt engineering as a methodological lever.
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Registered trials
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