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

Background: Although 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. Methods: Qualitative 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. Results: Financially 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. Conclusions: Those 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.