The Value of Clinical Prediction Models in General Practice: A Qualitative Study Exploring the Perspectives of People With Lived Experience of Depression and General Practitioners

• Published in: Health expectations : an international journal of public participation in health care and health policy 2024-12, Vol.27 (6), p.e70059
• Main Authors: Moriarty, Andrew S, Castleton, Joanne, McMillan, Dean, Riley, Richard D, Snell, Kym I E, Archer, Lucinda, Paton, Lewis W, Gilbody, Simon, Chew-Graham, Carolyn A
• Format: Article
• Language: English
• Subjects: Adult; Aged; Clinical Decision-Making; Depression - psychology; Female; General Practice; General Practitioners - psychology; Humans; Interviews as Topic; Male; Middle Aged; Original; Primary Health Care; Qualitative Research
• ISSN: 1369-7625; 1369-6513; 1369-7625
• DOI: 10.1111/hex.70059

Prediction models are increasingly being used to guide clinical decision making in primary care. There is a lack of evidence exploring the views of patients and general practitioners (GPs) in primary care around their use and implementation. We aimed to better understand the perspectives of GPs and people with lived experience of depression around the use of prediction models and communication of risk in primary care. Qualitative methods were used. Data were generated over 6 months (April to October 2022) through semi-structured interviews with 23 people with lived experience of depression and 22 GPs. A multidisciplinary research team and Patient Advisory Group were involved throughout the study. Data were analysed inductively using thematic analysis. GPs describe using prediction models in consultations only when the models are either perceived to be useful (e.g., because they help address an important clinical problem) or if GPs feel compelled to use them to meet financial or contractual targets. These two situations are not mutually exclusive, but if neither criterion is met, a model is unlikely to be used in practice. People with lived experience of depression and GPs reported that communication of model outputs should involve a combination of risk categories, numerical information and visualisations, with discussions being tailored to the individual patients involved. Risk prediction in a mental health context was perceived to be more challenging than for physical health conditions. Clinical prediction models are used in practice but thought must be given at the study development stage to how results will be presented and discussed with patients. Meaningful, embedded public and patient involvement and engagement are recommended when developing or implementing clinical prediction models. We used a combination of embedded consultation and collaboration/co-production in our approach to public and patient involvement in this study. A Patient Advisory Group made up of people with lived experience of depression were involved from study conception and contributed to study design, participant recruitment, interpretation of findings and dissemination (including in the preparation of this manuscript).