VP172 Clinical Effectiveness Of A Predictive Risk Model In Primary Care. (2017)

• Record Type: Journal Article
• Authors: Snooks, Helen; Porter, Alison; Kingston, Mark; Watkins, Alan; Hutchings, Hayley; Whitman, Shirley; Davies, Jan; Evans, Bridie; Bailey-Jones, Kerry; Burge-Jones, Deborah; Dale, Jeremy; Fitzsimmons, Deborah; Heaven, Martin; Howson, Helen; John, Gareth; Lewis, Leo; Philips, Ceri; Sewell, Bernadette; Williams, Victoria; Russell, Ian
• Is Part Of: International journal of technology assessment in health care. Volume 33:Supplement 1(2017)
• Page Start: 229
• Page End: 229
• Publication Date: 2017
• Subjects: Medical technology -- Periodicals; Technology assessment -- Periodicals; 610.28
• DOI: 10.1017/S026646231700407X
• Languages: English
• ISSNs: 0266-4623

Abstract

Abstract : 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.