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Risk prediction tools in multiple long-term conditions management: a qualitative study.
Stella Arakelyan1, Atul Anand2,3, Stewart W Mercer4
1Advanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK stella.arakelyan@ed.ac.uk.
Summary
Risk prediction tools for multiple long-term conditions (MLTC) show promise but require careful implementation. Healthcare professionals and patients highlight the need for tools that integrate clinical judgment, consider psychosocial factors, and align with patient priorities.
Area of Science:
- Health Services Research
- Clinical Informatics
- Patient-Centered Care
Background:
- Risk stratification is crucial for effective healthcare delivery.
- Its application in managing patients with multiple long-term conditions (MLTC) requires further investigation.
Purpose of the Study:
- To explore healthcare professionals', patients', and carers' views on the benefits and challenges of using risk prediction tools in MLTC management.
Main Methods:
- The study involved thematic analysis of interviews with 30 healthcare professionals and six focus groups with 28 patients with MLTC and their carers.
- Data collection occurred between May 2023 and May 2024 across four Scottish integrated Health and Social Care Partnerships.
Main Results:
- Healthcare professionals raised concerns about the clinical utility and algorithmic bias of current risk prediction tools, emphasizing the need for integration with clinical judgment and psychosocial factors.
- Patients and carers expressed apprehension regarding potential anxiety and loss of autonomy from risk communication, stressing the importance of contextual relevance and patient priorities.
- Artificial intelligence (AI) and routine data hold potential for enhancing predictive accuracy, but require robust IT infrastructure, training, and human oversight.
Conclusions:
- AI-informed risk stratification tools may be beneficial for MLTC management if they do not increase workload, support clinical decision-making, and incorporate patient complexity and preferences.
- Effective implementation necessitates addressing concerns about clinical utility, workload, and patient-centered communication.
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