Value of using artificial intelligence derived clusters by health and social care need in primary care: A qualitative interview study with patients living with multiple long-term conditions, carers and health care professionals
Background People living with MLTCs attending primary care often 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. Evidence is needed on views about the use of AI-deriv...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-06-01
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| Series: | Journal of Multimorbidity and Comorbidity |
| Online Access: | https://doi.org/10.1177/26335565251353016 |
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| author | Sian Holt Glenn Simpson Miriam Santer Hazel Everitt Andrew Farmer Kuangji Zhou Zhiling Qian Firoza Davies Hajira Dambha-Miller Leanne Morrison |
| author_facet | Sian Holt Glenn Simpson Miriam Santer Hazel Everitt Andrew Farmer Kuangji Zhou Zhiling Qian Firoza Davies Hajira Dambha-Miller Leanne Morrison |
| author_sort | Sian Holt |
| collection | DOAJ |
| description | Background People living with MLTCs attending primary care often 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. Evidence is needed on views about the use of AI-derived clusters, to inform acceptable and meaningful implementation within interventions. Method Qualitative semi-structured interviews (online and telephone), including a description of AI-derived clusters and a tailored vignette, with 24 people living with MLTCs and 20 people involved in the care of MLTCs (carers and health care professionals). Interviews were analysed using Reflexive and Codebook 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 support. AI was perceived as a tool that could potentially increase capacity 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 implementing AI-derived clusters to identify and support SCNs in primary care is perceived as valuable and can be used as a tool to inform and prioritse effective clinical conversations. But concerns must be addressed, including how AI-derived clusters can be used in a way that considers personal context. |
| format | Article |
| id | doaj-art-fe4d4f43cca043b68aee58f1a471cffa |
| institution | Kabale University |
| issn | 2633-5565 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of Multimorbidity and Comorbidity |
| spelling | doaj-art-fe4d4f43cca043b68aee58f1a471cffa2025-08-20T03:30:14ZengSAGE PublishingJournal of Multimorbidity and Comorbidity2633-55652025-06-011510.1177/26335565251353016Value of using artificial intelligence derived clusters by health and social care need in primary care: A qualitative interview study with patients living with multiple long-term conditions, carers and health care professionalsSian HoltGlenn SimpsonMiriam SanterHazel EverittAndrew FarmerKuangji ZhouZhiling QianFiroza DaviesHajira Dambha-MillerLeanne MorrisonBackground People living with MLTCs attending primary care often 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. Evidence is needed on views about the use of AI-derived clusters, to inform acceptable and meaningful implementation within interventions. Method Qualitative semi-structured interviews (online and telephone), including a description of AI-derived clusters and a tailored vignette, with 24 people living with MLTCs and 20 people involved in the care of MLTCs (carers and health care professionals). Interviews were analysed using Reflexive and Codebook 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 support. AI was perceived as a tool that could potentially increase capacity 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 implementing AI-derived clusters to identify and support SCNs in primary care is perceived as valuable and can be used as a tool to inform and prioritse effective clinical conversations. But concerns must be addressed, including how AI-derived clusters can be used in a way that considers personal context.https://doi.org/10.1177/26335565251353016 |
| spellingShingle | Sian Holt Glenn Simpson Miriam Santer Hazel Everitt Andrew Farmer Kuangji Zhou Zhiling Qian Firoza Davies Hajira Dambha-Miller Leanne Morrison Value of using artificial intelligence derived clusters by health and social care need in primary care: A qualitative interview study with patients living with multiple long-term conditions, carers and health care professionals Journal of Multimorbidity and Comorbidity |
| title | Value of using artificial intelligence derived clusters by health and social care need in primary care: A qualitative interview study with patients living with multiple long-term conditions, carers and health care professionals |
| title_full | Value of using artificial intelligence derived clusters by health and social care need in primary care: A qualitative interview study with patients living with multiple long-term conditions, carers and health care professionals |
| title_fullStr | Value of using artificial intelligence derived clusters by health and social care need in primary care: A qualitative interview study with patients living with multiple long-term conditions, carers and health care professionals |
| title_full_unstemmed | Value of using artificial intelligence derived clusters by health and social care need in primary care: A qualitative interview study with patients living with multiple long-term conditions, carers and health care professionals |
| title_short | Value of using artificial intelligence derived clusters by health and social care need in primary care: A qualitative interview study with patients living with multiple long-term conditions, carers and health care professionals |
| title_sort | value of using artificial intelligence derived clusters by health and social care need in primary care a qualitative interview study with patients living with multiple long term conditions carers and health care professionals |
| url | https://doi.org/10.1177/26335565251353016 |
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