Value of using artificial intelligence derived clusters by health and social care need in Primary Care: A qualitative interview study

Abstract

Purpose People living with MLTCs attending consultations in primary care frequently have unmet social care needs (SCNs), which can be challenging to identify and address. Artificial intelligence (AI) derived clusters could help to identify patients at risk of SCNs. Understanding the views of people living with MLTCs and those involved in their care can help inform the design of effective interventions informed by AI-derived clusters to address SCNs. Methods Qualitative study using semi-structured online and telephone interviews with 24 people living with MLTCs and 20 people involved in the care of MLTCs. Interviews were analysed using Reflexive Thematic Analysis. Results Primary care was viewed as an appropriate place to have conversations about SCNs. However, participants felt health care professionals lack capacity to have these conversations and to identify sources of support. AI was perceived as a tool that could potentially increase capacity for this but only when supplemented with effective, clinical conversations. Interventions harnessing AI should be brief, be easy to use and remain relevant over time, to ensure no additional burden on clinical capacity. Interventions must allow flexibility to be used by multidisciplinary teams within primary care, frame messages positively and facilitate conversations that remain patient centered. Conclusion Our findings suggest that AI-derived clusters to identify and support SCNs in primary care have perceived value, but there were some concerns including the need to consider personal context. AI derived clusters can be used as a tool to inform and prioritise effective clinical conversations.