AimWe evaluated the introduction of a predictive risk stratification model (PRISM) into primary care. Contemporaneously National Health Service (NHS) Wales introduced Quality and Outcomes Framework payments to general practices to focus care on those at highest risk of emergency admission to hospital. The aim of this study was to evaluate the costs and effects of introducing PRISM into primary care.MethodsRandomised stepped wedge trial with 32 general practices in one Welsh health board. The intervention comprised: PRISM software; practice-based training; clinical support through two ‘general practitioner (GP) champions’ and technical support. The primary outcome was emergency hospital admissions.ResultsAcross 230 099 participants, PRISM implementation increased use of health services: emergency hospital admission rates by 1 % when untransformed (while change in log-transformed rate ΔL=0.011, 95% CI 0.010 to 0.013); emergency department (ED) attendance rates by untransformed 3 % (while ΔL=0.030, 95% CI 0.028 to 0.032); outpatient visit rates by untransformed 5 % (while ΔL=0.055, 95% CI 0.051 to 0.058); the proportion of days with recorded GP activity by untransformed 1 % (while ΔL=0.011, 95% CI 0.007 to 0.014) and time in hospital by untransformed 3 % (while ΔL=0.029, 95% CI 0.026 to 0.031). Thus NHS costs per participant increased by £76 (95% CI £46 to £106).ConclusionsIntroduction of PRISM resulted in a statistically significant increase in emergency hospital admissions and use of other NHS services without evidence of benefits to patients or the NHS.

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. 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.

INTRODUCTION:New approaches are needed to safely reduce emergency admissions to hospital by targeting interventions effectively in primary care. A predictive risk stratification tool (PRISM) identifies each registered patient's risk of an emergency admission in the following year, allowing practitioners to identify and manage those at higher risk. We evaluated the introduction of PRISM in primary care in one area of the United Kingdom, assessing its impact on emergency admissions and other service use.METHODS:We conducted a randomized stepped wedge trial with cluster-defined control and intervention phases, and participant-level anonymized linked outcomes. PRISM was implemented in eleven primary care practice clusters (total thirty-two practices) over a year from March 2013. We analyzed routine linked data outcomes for 18 months.RESULTS:We included outcomes for 230,099 registered patients, assigned to ranked risk groups.Overall, the rate of emergency admissions was higher in the intervention phase than in the control phase: adjusted difference in number of emergency admissions per participant per year at risk, delta = .011 (95 percent Confidence Interval, CI .010, .013). Patients in the intervention phase spent more days in hospital per year: adjusted delta = .029 (95 percent CI .026, .031). Both effects were consistent across risk groups.Primary care activity increased in the intervention phase overall delta = .011 (95 percent CI .007, .014), except for the two highest risk groups which showed a decrease in the number of days with recorded activity.CONCLUSIONS:Introduction of a predictive risk model in primary care was associated with increased emergency episodes across the general practice population and at each risk level, in contrast to the intended purpose of the model. Future evaluation work could assess the impact of targeting of different services to patients across different levels of risk, rather than the current policy focus on those at highest risk.

INTRODUCTION:A predictive risk stratification tool (PRISM) to estimate a patient's risk of an emergency hospital admission in the following year was trialled in general practice in an area of the United Kingdom. PRISM's introduction coincided with a new incentive payment (‘QOF’) in the regional contract for family doctors to identify and manage the care of people at high risk of emergency hospital admission.METHODS:Alongside the trial, we carried out a complementary qualitative study of processes of change associated with PRISM's implementation. We aimed to describe how PRISM was understood, communicated, adopted, and used by practitioners, managers, local commissioners and policy makers. We gathered data through focus groups, interviews and questionnaires at three time points (baseline, mid-trial and end-trial). We analyzed data thematically, informed by Normalisation Process Theory (1).RESULTS:All groups showed high awareness of PRISM, but raised concerns about whether it could identify patients not yet known, and about whether there were sufficient community-based services to respond to care needs identified. All practices reported using PRISM to fulfil their QOF targets, but after the QOF reporting period ended, only two practices continued to use it. Family doctors said PRISM changed their awareness of patients and focused them on targeting the highest-risk patients, though they were uncertain about the potential for positive impact on this group.CONCLUSIONS:Though external factors supported its uptake in the short term, with a focus on the highest risk patients, PRISM did not become a sustained part of normal practice for primary care practitioners.

for each variation method for the ten most influential inputs. We assessed the number of inputs with a mean difference between lower and upper ICERs of >10 percent of the deterministic ICER.