JMIR Publications

ABSTRACT

Background:

Multimorbidity, living with two or more long-term health conditions, is increasing globally and now affects over a quarter of adults in England. People with multiple long-term conditions (MLTC) face complex health and treatment challenges, often experiencing fragmented care within systems oriented toward single-disease management. Traditional evidence-based approaches and clinical trials frequently exclude those with MLTC, limiting the applicability of current clinical guidance. Artificial intelligence (AI) has the potential to support clinicians and patients by analysing complex health data, optimising treatment strategies, and predicting disease trajectories.

Objective:

The OPTIMAL (Optimising Therapies, Disease Trajectories, and AI-Assisted Clinical Management for Patients Living with Complex Multimorbidity) project aims to develop AI-enabled tools to support shared decision-making in primary care. This study explored how patients with MLTC perceive the use of AI to inform joint decision-making in primary care. It sought to understand perceived benefits, risks, and barriers to AI implementation, and to identify factors that influence acceptance and trust in AI-supported clinical consultations.

Methods:

Semi-structured interviews were conducted with 29 adults living with MLTC between July and November 2023. Participants were recruited through GP practices via the Clinical Practice Research Datalink (CPRD) and community-based organisations across the West Midlands. Interviews were conducted via telephone or video call, transcribed verbatim, and analysed thematically using an inductive approach. Members of a patient advisory group were involved in developing study materials, refining the interview guide, and reviewing emerging findings to ensure relevance and authenticity.

Results:

Participants identified potential benefits of AI in improving consultation efficiency, enhancing diagnostic accuracy, supporting medication management, integrating care across conditions, and promoting early detection of health changes. AI was also viewed as potentially empowering for patients through improved access to health information. However, concerns were raised about loss of human interaction, data privacy and security, transparency of algorithms, and the potential for bias and inequity in AI systems. Trust and acceptance varied by age and familiarity with technology. Some participants expressed uncertainty about what AI entails and how it could be used in primary care.

Conclusions:

Patients with MLTC viewed AI-assisted decision-making in primary care with cautious optimism. While many recognised potential benefits for coordination and personalisation of care, others expressed reservations about privacy, fairness, and the risk of diminished human connection. Clinical Trial: N/A