Title: Data-driven integrated commissioning for prevention: Exploiting multi-sector linked data to prevent emergency hospital admissions for Community Acquired Pneumonia
Funder: Health Education England

Abstract: Background: Emergency hospital admissions cost the NHS 12.5 billion a year and are rising year on year. Identifying which patients could benefit from interventions designed to avoid such admissions is of pressing importance for health and social care organisations. Emerging local level whole-population, multi-sector, linked data initiatives offer unprecedented opportunities to understand how local health services, social care and housing impact on risk of admission. These provide the potential to predict which segments of the population are most at risk, understand why they are at risk and guide commissioning of preventative programmes targeted at these population segments.It is currently uncertain a) what the 1-2 year risk for emergency admissions for Community Acquired Pneumonia, an important cause of potentially avoidable emergency admissions, is; b) what characteristics population segments with differing risk profiles have; or c) how such information is or could be used by those responsible for planning programmes to prevent such admissions in the current era of localised multi-sector patient-level datasets. Aim and Objectives: Aim: To investigate the value of risk stratification with population segmentation to support commissioning for prevention using whole population, multi-sector, linked data, identifying enablers and barriers to implementation of these tools, using CAP as a case study. Objectives: 1. To identify current practice in the use of risk stratification to inform commissioning for prevention, as well as enablers and barriers to the use of such intelligence, within the current context of increasing use of local linked patient-level datasets. 2. To develop and validate a risk stratification model with population segmentation model for emergency admissions for CAP using the Classification And Regression Tree (CART) statistical technique, utilising the rich linked electronic health records available at scale (~2 million patients) in the CALIBER research platform. 3. To develop and validate risk stratification model for emergency admissions for CAP which incorporate additional data on the wider determinants of health, using the novel whole population patient-level Kent Integrated Dataset (KID) covering 1.5 million residents, and compare these to the model developed under Objective 2. Plan of Investigation: Objective 1 will addressed by a postal survey to a random sample of Clinical Commissioning Groups (CCGs), followed by up to 35 in-depth qualitative interviews with commissioners where risk stratification to inform commissioning has penetrated the CCG decision-making processes. Interviews will focus on the ways in which has risk stratification has been used for commissioning, enablers and barriers their use and the impact of the use of such tools. Interviews will be taped and the transcriptions analysed for emergent themes. Objective 2 will be addressed through a modelling study, developing a risk stratification model for a cohort of patients aged 18+ from the linked CALIBER dataset using CART. The model will be validated in KID and measures of model accuracy and calibration calculated and compared to current risk prediction models in use in the NHS. Objective 3 will be similarly addressed using the KID to create the cohort and incorporating environmental and social variables in the model to determine if their inclusion improves model accuracy.Potential benefits to patients and the NHS: The proposed research will develop and test tools to improve the commissioning of programmes to prevent emergency admissions, with direct benefits for patients who are able to remain in their own homes whilst potentially saving the NHS the costs of avoidable hospital admissions. Studying reasons for the implementation and diffusion of risk stratification tools for commissioning is an essential part of ensuring they have an impact on patient outcomes.

Lay abstract: Background: Emergency hospital admissions are rising year on year with consequences for patients, taxpayers and NHS services. There are 5.5 million emergency hospital admissions a year, costing over 12 billion. If we can figure out which patients likely to be admitted to hospital in an emergency before they become unwell, measures can be put in place to avoid those admissions. Researchers can predict whether an individual patient is likely to have an emergency admission in the future, using information about what has happened in the past to similar patients. The group of tools developed for this purpose are called risk-prediction tools. General Practitioners (GPs) now use these tools to find their patients who have a high chance of being admitted to hospital in an emergency. GPs then get additional support for these patients to head off possible emergency hospital admissions.The statutory NHS organisations responsible for the planning and commissioning health care services in local area are Clinical Commissioning Groups (CCGs). As part of their work, CCGs also aim to reduce emergency hospital admissions. They do this by planning services designed to prevent emergency admissions. No one has yet looked at whether CCGs use -- or have tried to use existing risk-prediction tools to help make decisions about which preventative services are needed. Objectives: My research has two linked objectives. First, I want to know whether and how CCGs use risk-prediction tools to decide what sort of preventative services they will commission. Second, I want to develop new risk-prediction tools, which should be more useful for CCGs than existing tools. These new tools will provide information on groups of patients at risk of an emergency hospital admissions who share similar characteristics. Design and methods used: For the first objective, I will carry out a survey of some CCGs. I will then interview CCG staff to find out what they think about using risk-prediction tools to help make decisions about local preventative services. For my second objective, I will develop two risk-prediction tools. For the first, I will use information from anonymised electronic health records from general practice, hospital admissions, and death certificates. For the second, I will add in information from social care and other local government services. Throughout the research, I will focus on developing tools to predict emergency hospital admissions for pneumonia. Many hospital admissions for pneumonia could potentially be avoided with the right preventative care. I will also work with CCGs to design this approach to risk prediction, to ensure that any tools that I develop are genuinely useful to their decision-making processes. Patient and Public Involvement: I will work with a small panel of Patient and Public Involvement (PPI) members to inform the research as it develops. People with an interest in, or experience of, NHS commissioning, will be involved. I will identify with my PPI members what learning or development needs they have to be fully involved in the development of my research programme and support them to receive it. The PPI members will be actively involved in developing aspects of the risk-prediction tools with CCGs.Dissemination: I will make sure that the findings of my research are shared with academic, public, local government and NHS audiences. I will publish in academic journals and present at key scientific conferences aimed at those undertaking research in health services and public health. I will spread my findings through a range of NHS and public health networks at local, regional and national level, as well as speaking at conferences aimed at NHS commissioners and Public Health professionals.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: INFECTION