Analyzing patient perspectives with large language models: a cross-sectional study of sentiment and thematic classification on exception from informed consent.

ABSTRACT: Large language models (LLMs) can improve text analysis efficiency in healthcare. This study explores the application of LLMs to analyze patient perspectives within the exception from informed consent (EFIC) process, which waives consent in emergency research. Our objective is to assess whether LLMs can analyze patient perspectives in EFIC interviews with performance comparable to human reviewers. We analyzed 102 EFIC community interviews from 9 sites, each with 46 questions, as part of the Pediatric Dose Optimization for Seizures in Emergency Medical Services study. We evaluated 5 LLMs, including GPT-4, to assess sentiment polarity on a 5-point scale and classify responses into predefined thematic classes. Three human reviewers conducted parallel analyses, with agreement measured by Cohen'

SUBMITTER: Kornblith AE 

PROVIDER: S-EPMC11842787 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature