A qualitative systematic review protocol of patient experiences of using large language models for health information seeking and healthcare decision making

Abstract

Abstract Background Large language models such as ChatGPT have rapidly become popular tools for seeking health information, with surveys indicating that nearly half of consumers use generative AI for health-related inquiries. Despite documented accuracy limitations and hallucination risks, patients increasingly consult these tools when making healthcare decisions, effectively using them as supplementary or alternative sources of health information alongside or in lieu of traditional medical consultation. Objectives To systematically identify, appraise, and synthesise qualitative research exploring the lived experiences of patients who use large language models to inform healthcare decision-making, examining motivations, information-seeking behaviours, trust and scepticism, perceived impacts on healthcare decisions, and the meaning patients ascribe to AI-generated health information. Methods This protocol follows PRISMA-P and ENTREQ guidelines. Systematic searches will be conducted across MEDLINE, PsycINFO, CINAHL, Web of Science, ACM Digital Library, and Scopus from November 2022 through December 2025. Studies employing qualitative methods to explore patient experiences with LLM-based health information seeking will be included. Study selection and quality assessment using the CASP Qualitative Checklist will be conducted independently by two reviewers using Covidence. Data will be synthesised using thematic synthesis following Thomas and Harden’s approach. Discussion This protocol establishes a rigorous framework for synthesising qualitative evidence on an emergent healthcare phenomenon with significant implications for patient safety, health literacy, shared decision-making, and the evolving patient-provider relationship in the age of generative AI.

Ähnliche Arbeiten