Title: Predictive RIsk Stratification Models: AssessmenT of Implementation Consequences (PRISMATIC 2)
Funder: Health and Care Research Wales (Welsh Government)

Abstract: We know that when patients are admitted to hospital as an emergency, they may stay in hospital for a long time and are at risk of losing mobility and independence. UK policy has supported initiatives to reduce emergency admissions, where possible. One widely implemented initiative in primary care has been the installation of software that predicts each patient’s risk of emergency hospital admission. The software allocates a risk score to each patient that allows GPs to target management, such as medication review, to those at highest risk. This software has been introduced without knowing whether it brings benefits to patients or to the NHS. However, our evaluation of the introduction of predictive risk software in 32 general practices in the South Wales area (PRISMATIC trial) found that emergency admissions to hospital, Emergency Department (ED) attendances and days spent in hospital all increased when the software was available to GPs. We propose to undertake PRISMATIC 2 to build on these findings to: 1. Assess effects of introducing emergency admission risk prediction software across England 2. Investigate whether GPs change how they manage risk and make decisions about admitting patients to hospital when the software is introduced 3. Understand patients’ views about communication of risk scores by their GP – do they want to know if they are classified as high risk of emergency admission to hospital? 1. Using national data, we will look at changes in trends in emergency admission and other healthcare use that occurred when the software was made available. This approach is suited to this study with over 200 health commissioners - Clinical Commissioning Groups (CCGs) - across England and a range of dates when risk prediction software was implemented. We will look at anonymised data on emergency admissions, Emergency Department attendances and days spent in hospital in each CCG before and after the software was available so that we can understand whether there were any effects when it was introduced. 2. To understand why any changes might have happened, we will look more closely at the data in 16 GP practices in eight contrasting CCGs. We will explore whether GPs changed their decision making when the risk prediction software became available. For instance, did GPs lower their threshold for deciding to admit patients? Or did they prioritise patients in different ways once their risk scores were available to them? We will interview 48 members of staff including GPs at these practices to find out whether they think practice changed with implementation of the software. 3. We will carry out focus groups (x2) and interviews (x16) with patients to ask them whether they think hearing their own risk score may affect them, and if so, how. PRISMATIC 2 will build on what we learnt in PRISMATIC and comprises the same core research team with its strong track record of research delivery. We have worked with patients and the public throughout this research programme. We will continue to involve PPI contributors in all aspects of research development, management, oversight, delivery and dissemination. We will communicate findings to: * Patients to inform them about the use of risk prediction software and allow them to participate more fully in decisions about their own healthcare * NHS and social care staff and policy makers to ensure that the best evidence about the effects of risk prediction software can be used to inform practice and policy.

Lay abstract: We know that emergency admissions can lead to lengthy hospital stays, loss of independence and mobility which do not always benefit the patient and are costly to the NHS. Software installed into GP practices that predicts patients’ risk of emergency hospital admission has been widely implemented across the UK, and has been supported by national policy. Research in this area has concentrated on technical performance of the software, with an assumption that identifying patients at high risk of admission to hospital enables effective targeting of preventative services and reduces avoidable admissions. However, effects can be unintended. Our PRISMATIC evaluation of introduction of predictive risk software in 32 general practices in the Swansea area found that emergency admissions to hospital, Emergency Department attendances and days spent in hospital increased. We propose to undertake a further study to build on findings of PRISMATIC, to 1. assess the effects and costs of the introduction of emergency admission predictive risk stratification tools across England 2. Investigate how GPs may change their practice in relation to managing risk when the new software is introduced 3. Understand patients’ views on communication of risk scores by their GP or other primary care staff – do they want to know if they are classified as at high risk of emergency admission to hospital? Does that make a difference to their health behaviours and self-care? We will look at changes in trends in emergency admission and other healthcare use across the population and for patients with ambulatory care sensitive conditions, related to software introduction. The multiple interrupted time series approach is powerful and well suited to this context, with a large number of health commissioners across the UK (n = 211) and a range of dates when predictive risk stratification software was implemented. We will analyse anonymised data on emergency admissions, ED attendances and days spent in hospital at Clinical Commissioning Group (CCG) level between 2010 and 2021 by date of introduction of predictive risk stratification software. To try to understand mechanisms underlying any changes, we will look more closely at emergency admissions from primary care before and after risk prediction software became available in selected practices (n = 16) in eight contrasting CCGs. We will use routine data to examine whether the threshold for deciding to admit patients or casemix at these practices changed. We will interview healthcare staff including GPs at these practices to find out how they use the risk prediction software; whether this has changed over time; and their views about how the software may have influenced their practice. We will hold two focus groups with patients, and interview 16 patients to explore experiences of emergency admission, views about hearing about their own risk levels, and how they think this may affect them, including self-care. We have worked with patients and members of the public throughout this research programme, and will continue to include PPI members in all aspects of research development, management, oversight, delivery and dissemination. The delivery focused PRISMATIC2 trial team will build on earlier findings in PRISMATIC. The proposed study will give policy makers and clinicians a better understanding of effects of predictive risk software on costs, processes and outcomes of care, in order to inform future policy and practice.

Research activity codes: 8.1 ORGANISATION AND DELIVERY OF SERVICES
Health categories: GENERIC HEALTH RELEVANCE