Application of Large Language Models in Chronic Disease Care: Mixed Methods Systematic Review and Thematic Synthesis
Abstract
BACKGROUND: Chronic diseases account for nearly three-quarters of global deaths and demand continuous, personalized long-term management; yet traditional care models often fall short in delivering such sustained support. Large language models, with advanced conversational and analytical capabilities, present promising opportunities to address the problem by offering scalable, interactive support. However, a comprehensive synthesis of evidence across diverse study designs, which moves beyond isolated technical metrics to evaluate large language models through a structured, theory-driven lens, remains limited.
OBJECTIVE: This study aimed to synthesize quantitative, qualitative, and mixed methods evidence on technical performance, application scenarios, and documented challenges of large language models in chronic disease care, and to critically evaluate these findings through a theory-driven, 3D framework informed by Orem's Self-Care Theory.
METHODS: The mixed methods systematic review adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 and SWiM (Synthesis Without Meta-Analysis) guidelines. A comprehensive computer-based search was conducted across PubMed, Web of Science, Embase, Cochrane Library, CINAHL, Wiley Online Library, SpringerLink, ScienceDirect, China National Knowledge Infrastructure, Wanfang Data, VIP Database, and the China Biology Medicine Disc from inception to April 2026. Two independent researchers performed study…
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