Evidence map›Paper›PMID 40588809›Full record

ArticleJournal of public health (Oxford, England)2025

Using population risk prediction for healthcare planning: a qualitative study of healthcare planners' experiences and views.

Julie George, Angus I G Ramsay, Sonya Crowe, Andrew Hayward

Abstract read
In one paragraph

Article in Journal of public health (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Julie GeorgeInstitute of Health Informatics, UCL, London WC1E 7HB, UK.ORCID 0000-0003-2410-2696
Angus I G RamsayDepartment of Behavioural Science and Health, Institute of Epidemiology and Health Care, UCL, London WC1E 7HB, UK.ORCID 0000-0002-4446-6916
Sonya CroweClinical Operational Research Unit, UCL, London WC1E 7HB, UK.ORCID 0000-0003-1882-5476
Andrew HaywardDepartment of Epidemiology and Public Health, Institute of Epidemiology and Health Care, UCL, London WC1E 7HB, UK.ORCID 0000-0002-3549-6232

Funding

NIHR
6 · The paper itself

Abstract

backgroundAlthough English National Health Service (NHS) policymakers are eager to mandate use of data analytics to inform healthcare planning and prevention, little is known about what happens in practice. This study investigated the ways in which planners within the local payer organizations use population risk prediction models to inform their planning of healthcare and enablers and barriers to use of such tools.

methodsQualitative case study design across five payer organizations. Interviews (n = 20) were conducted with senior decision-makers from various backgrounds. Analysis was guided by diffusion of innovation frameworks.

resultsFinancially stable organizations with existing investment in health intelligence using linked data were more likely to report use of risk prediction in their planning practice. Obstacles to uptake identified were financial instability; workforce capacity to consider use of such intelligence; distraction by centrally mandated system changes; concerns about completeness, accuracy, and timeliness of data; and interest in other sources of insight to inform planning such as patient experience.

conclusionsThose working in healthcare, public health, or health intelligence need to recognize that financial and organizational stability are as important as investment in staff capacity/skills and data systems to increase the use of risk prediction to support prevention in the NHS.

Indexed as

Health PlanningEnglandHumansInterviews as TopicQualitative ResearchRisk AssessmentState MedicineUnited Kingdomcommissioningdata analyticsdiffusion of innovationhealth intelligencehealth service researchrisk prediction

Identifiers

PMID40588809
PMCPMC12395939

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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.