Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC)

Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC) Helen Snooks,1* Kerry Bailey-Jones,2 Deborah Burge-Jones,2 Jeremy Dale,3 Jan Davies,4 Bridie Evans,1 Angela Farr,5 Deborah Fitzsimmons,5 Jane Harrison,6 Martin Heaven,7 Helen Howson,8 Hayley Hutchings,1 Gareth John,9 Mark Kingston,1 Leo Lewis,10 Ceri Phillips,5 Alison Porter,1 Bernadette Sewell,5 Daniel Warm,11 Alan Watkins,1 Shirley Whitman,4 Victoria Williams1 and Ian T Russell1 1Swansea University Medical School, Swansea, UK 2Abertawe Bro Morgannwg University Health Board, Port Talbot, UK 3Warwick Medical School, University of Warwick, Coventry, UK 4Independent service user 5Swansea Centre for Health Economics, Swansea University, Swansea, UK 6Public Health Wales, Cardiff, UK 7The FARR Institute, Swansea University Medical School, Swansea, UK 8Bevan Commission, School of Management, Swansea University, Swansea, UK 9NHS Wales Informatics Service, Cardiff, UK 10International Foundation for Integrated Care, Oxford, UK 11Hywel Dda University Health Board, Hafan Derwen, Carmarthen, UK *Corresponding author h.a. snooks@swansea.ac. uk Background: With a higher proportion of older people in the UK population, new approaches are needed to reduce emergency hospital admissions, thereby shifting care delivery out of hospital when possible and safe. Study aim: To evaluate the introduction of predictive risk stratification in primary care. Objectives: To (1) measure the effects on service usage, particularly emergency admissions to hospital; (2) assess the effects of the Predictive RIsk Stratification Model (PRISM) on quality of life and satisfaction; (3) assess the technical performance of PRISM; (4) estimate the costs of PRISM implementation and its effects; and (5) describe the processes of change associated with PRISM. Design: Randomised stepped-wedge trial with economic and qualitative components. Setting: Abertawe Bro Morgannwg University Health Board, south Wales. Participants: Patients registered with 32 participating general practices. Intervention: PRISM software, which stratifies patients into four (emergency admission) risk groups; practice-based training; and clinical support. DOI: 10.3310/hsdr06010 HEALTH SERVICES AND DELIVERY RESEARCH 2018 VOL. 6 NO. 1 © Queen’s Printer and Controller of HMSO 2018. This work was produced by Snooks et al. under the terms of a commissioning contract issued by the Secretary of State for Health. This issue may be freely reproduced for the purposes of private research and study and extracts (or indeed, the full report) may be included in professional journals provided that suitable acknowledgement is made and the reproduction is not associated with any form of advertising. Applications for commercial reproduction should be addressed to: NIHR Journals Library, National Institute for Health Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK. v Main outcome measures: Primary outcome – emergency hospital admissions. Secondary outcomes – emergency department (ED) and outpatient attendances, general practitioner (GP) activity, time in hospital, quality of life, satisfaction and costs. Data sources: Routine anonymised linked health service use data, self-completed questionnaires and staff focus groups and interviews. Results: Across 230,099 participants, PRISM implementation led to increased emergency admissions to hospital [ΔL = 0.011, 95% confidence interval (CI) 0.010 to 0.013], ED attendances (ΔL = 0.030, 95% CI 0.028 to 0.032), GP event-days (ΔL = 0.011, 95% CI 0.007 to 0.014), outpatient visits (ΔL = 0.055, 95% CI 0.051 to 0.058) and time spent in hospital (ΔL = 0.029, 95% CI 0.026 to 0.031). Quality-of-life scores related to mental health were similar between phases (Δ = –0.720, 95% CI –1.469 to 0.030); physical health scores improved in the intervention phase (Δ = 1.465, 95% CI 0.774 to 2.157); and satisfaction levels were lower (Δ = –0.074, 95% CI – 0.133 to –0.015). PRISM implementation cost £0.12 per patient per year and costs of health-care use per patient were higher in the intervention phase (Δ = £76, 95% CI £46 to £106). There was no evidence of any significant difference in deaths between phases (9.58 per 1000 patients per year in the control phase and 9.25 per 1000 patients per year in the intervention phase). PRISM showed good general technical performance, comparable with existing risk prediction tools (c-statistic of 0.749). Qualitative data showed low use by GPs and practice staff, although they all reported using PRISM to generate lists of patients to target for prioritised care to meet Quality and Outcomes Framework (QOF) targets. Limitations: In Wales during the study period, QOF targets were introduced into general practice to encourage targeting care to those at highest risk of emergency admission to hospital. Within this dynamic context, we therefore evaluated the combined effects of PRISM and this contemporaneous policy initiative. Conclusions: Introduction of PRISM increased emergency episodes, hospitalisation and costs across, and within, risk levels without clear evidence of benefits to patients. Future research: (1) Evaluation of targeting of different services to different levels of risk; (2) investigation of effects on vulnerable populations and health inequalities; (3) secondary analysis of the Predictive Risk Stratification: A Trial in Chronic Conditions Management data set by health condition type; and (4) acceptability of predictive risk stratification to patients and practitioners. Trial and study registration: Current Controlled Trials ISRCTN55538212 and PROSPERO CRD42015016874. Funding: The National Institute for Health Research Health Services Delivery and Research programme. ABSTRACT NIHR Journals Library www.journalslibrary. nihr.ac. uk vi