Patient Perceptions of Artificial Intelligence-Supported Shared Decision-Making in UK Primary Care for Multiple Long-Term Conditions: Qualitative Study.

Background: The prevalence of multiple long-term conditions (MLTCs) is increasing globally, leading to complex health care needs and polypharmacy. Shared decision-making (SDM) is important for supporting patient-centered care, yet barriers such as limited consultation time, discontinuity of care, and communication challenges hinder implementation. Artificial intelligence (AI) has the potential to support SDM by providing personalized, data-driven recommendations, particularly for medication management in patients with MLTCs. Objective: This study aimed to explore the perspectives of patients with MLTCs regarding SDM with their general practitioners (GPs) and to explore patients' views about the use of an AI tool to support SDM, particularly in relation to prescribing decisions. Methods: This qualitative study explored the perspectives of 18 patients with MLTCs on SDM and the use of an AI tool prototype during GP consultations. Semistructured interviews used a simulated patient vignette and a visual AI tool dashboard to facilitate discussion. Participants were recruited through GP practices via the Clinical Practice Research Datalink and community-based organizations across the West Midlands. The data were then analyzed using thematic analysis. Results: Two overarching categories were identified: SDM in GP consultations and the AI tool for SDM. Within SDM, themes included communication and collaboration and system-level barriers, such as limited consultation time, lack of continuity, and fragmented records. Within the AI tool category, themes were related to practical design and implementation, implications for clinical practice and decision-making, and perceived risks and limitations. Participants valued the tool's potential to summarize health information and support discussions but highlighted the need for clear explanations, accessible design, and clinician guidance. Concerns included time pressures, depersonalization, trust, and transparency, with participants emphasizing that AI should support rather than replace clinical judgment. Conclusions: Overall, patients perceived AI as a promising way to enhance SDM by improving communication and collaboration between patient and clinician. However, patients also had concerns about the accuracy and veracity of AI. The study provides recommendations for AI tools in GP consultations, emphasizing clear, accessible outputs and the use of lay language. AI tools should enhance rather than replace clinical judgment, be transparent about data sources, and be developed with diverse patient input to ensure inclusivity and usability, particularly for those with MLTCs.