Leveraging ChatGPT for thematic analysis of medical best practice advisory data.

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

SUBMITTER: Jeong Y 

PROVIDER: S-EPMC12757007 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature