Multimorbidity and AI-enabled health and social care: A methodological illustration of integrating large language models into qualitative analytic workflows

Abstract

Background: People living with multimorbidity often experience unmet social care needs, which can negatively affect wellbeing and increase pressure on health and social care systems. Artificial intelligence (AI)–enabled tools may support more timely and tailored responses to these needs. Large language models (LLMs) are emerging as tools to support qualitative research, although research detailing their integration into qualitative analytic workflows remains limited. Methods: We conducted a secondary thematic analysis of 75 qualitative interview transcripts involving people with multimorbidity and their carers. The dataset was coded according to an analytic framework of exploratory, interpretive, and integrative layers of meaning. The dataset was analysed according to two parallel analytic streams: human reflexive thematic analysis, and qualitative analysis using Claude Sonnet 4. Model outputs were iteratively reviewed and compared against manual thematic analysis for convergence and