Evidence map›Paper›PMID 39696827›Full record

ArticleHealth expectations : an international journal of public participation in health care and health policy2024

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.

Andrew S Moriarty, Joanne Castleton, Dean McMillan, Richard D Riley, Kym I E Snell, Lucinda Archer, Lewis W Paton, Simon Gilbody, Carolyn A Chew-Graham

Abstract read
In one paragraph

Article in Health expectations : an international journal of public participation in health care and health policy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Andrew S MoriartyDepartment of Health Sciences, Hull York Medical School, University of York, York, UK.ORCID 0000-0003-0770-3262
Joanne CastletonMember of Patient Advisory Group, University of York, York, UK.
Dean McMillanDepartment of Health Sciences, Hull York Medical School, University of York, York, UK.
Richard D RileyInstitute of Applied Health Research, University of Birmingham and National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK.
Kym I E SnellInstitute of Applied Health Research, University of Birmingham and National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK.
Lucinda ArcherInstitute of Applied Health Research, University of Birmingham and National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK.
Lewis W PatonDepartment of Health Sciences, Hull York Medical School, University of York, York, UK.
Simon GilbodyDepartment of Health Sciences, Hull York Medical School, University of York, York, UK.
Carolyn A Chew-GrahamSchool of Medicine, Keele University, Keele, UK.

Funding

This study was supported by National Institute for Health and Care Research, Grant/Award Number: DRF-2018-11-ST2-044; WM ARC; National Institute for Health Research.
6 · The paper itself

Abstract

introductionPrediction 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.

methodsQualitative 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.

resultsGPs 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.

conclusionClinical 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. PATIENT OR PUBLIC CONTRIBUTION: 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).

Indexed as

DepressionGeneral PracticeGeneral PractitionersQualitative ResearchAdultAgedClinical Decision-MakingFemaleHumansInterviews as TopicMaleMiddle AgedPrimary Health Caredepressiondiagnosisgeneral practiceprediction modelprimary careprognosis

Identifiers

PMID39696827
PMCPMC11655671

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.