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

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 divergence. Results: Across the analytic workflow, twelve themes from the original human-led analysis were used as a reference framework for examining areas of alignment, extension, or divergence in LLM-generated interpretations. The LLM-assisted analysis highlighted shifts in analytic emphasis and candidate interpretive nuances, including emotive tone and latent cross-cutting concerns, while requiring human oversight to determine evidential grounding. Conclusions: We present a structured methodological illustration for integrating LLM-assisted outputs within qualitative analysis. Using convergence-divergence mapping, we examine how LLM-generated interpretations may function as an additional analytic lens that can support reflexivity, transparency, and analytic auditability in qualitative research applied within the context of multimorbidity.