Title: The impact of medical labour on variation in patient outcomes: evidence from English public hospitals
Funder: Economic and Social Research Council

Abstract: There is mounting evidence of wide variation across regions and providers in healthcare costs, treatments provided, and patient outcomes. These variations cannot be explained by patient type, geography or prices (Newhouse et al., 2013; Skinner, 2012). Variation exists even among clinicians working in the same hospitals and treating similar patients. This has led to recent efforts to better understand the effect of individual clinicians and different ways of organising care on patient outcomes and medical productivity (e.g. Cutler et al. (2017), Chan (2016), Currie et al. (2016)). But the extent of existing quantitative research is limited because it demands large data sets and information on clinicians linked to patient outcomes. Further, most of what is known is derived from one health care setting, the USA. The US system is much more complex than many other healthcare systems and is characterised by many, often overlapping, payment and incentive structures, all of which may affect the behaviour of medical labour (Propper and Nicholson 2012). The aim of our proposed research is to bring about a step change in the understanding of the determinants of variation in patient outcomes arising from the organisation of medical professionals. We will examine this in a setting where the impact of complex contractual and payment systems is switched off in order to isolate the effect of skill and organisation of clinician delivery arrangements. Our specific aims are to: 1) Understand the extent, and drivers, of variation across senior physicians in the quality of care they provide to their patients. 2) Understand team production, in particular the role of senior and junior physicians, and of temporary and permanent staff, in determining the quality of care to patients. We will exploit large scale administrative data to uncover regularities and exploit natural experiments to obtain causal estimates. We will also seek to demonstrate the usefulness of new administrative datasets for addressing questions relating to workforce quality and productivity and to aid policy making on workforce allocation and planning of staff training. Our test-bed is the English National Health Service (NHS). The NHS is the largest employer in England, employing more than a million workers at a cost of £50 billion in 2016/17 (Department of Health, 2017). It is an ideal environment for understanding the determinants of variation in labour productivity that arise from the way that labour is organised within hospitals. It is a single employer and has the same conditions of employment across different hospitals, so shutting-off variation which arises from different contractual arrangements for similar staff employed by different organisations. The single payer status of the NHS and the use of centrally fixed prices means there is no variation in outcomes that may arise from differences in contracts for care or output prices. Its large scale means that there are numerous providers of similar services, which means we have many observations and also allows us to exploit mobility across organisations as well as natural experiments to establish causality. In addition, despite the importance of the NHS as an employer and provider of healthcare in the UK, numerous official bodies have noted a worrying lack of evidence on how to efficiently organise the existing workforce and the factors that drive longer term labour supply (e.g, National Audit Office (2016)). Our research will utilise two main datasets. First, we will use NHS administrative discharge data (the Hospital Episodes Statistics) on hospital activity. These data have been extensively used for clinical research but remain under-utilised for workforce related issues. Second, we will make use of an entirely new source of highly granular data for five hospitals in England, which link staffing to patients and their treatments, costs and clinical outcomes.

Lay abstract: Motivation: The provision of healthcare is highly labour intensive, requiring a multi-disciplinary workforce with many years of training. Quality of care provided to patients depends crucially on both the availability and quality of individual staff and how they work together in teams. The NHS is the largest employer in England, employing more than a million people at a cost of more than £50 billion in 2016 (Department of Health, 2017). But NHS pay review bodies and the UK National Audit Office have noted that evidence on the efficient allocation of existing workers is scarce and long-term workforce planning is lacking (NHS Pay Review Body, 2017; Review Body on Doctor's and Dentist's Remuneration, 2017; National Audit Office, 2016). Such evidence and planning are important in order to contain costs and ensure that patients receive the highest possible quality of care. Aims and methodology: Our proposed research will use econometric techniques and rich administrative data to identify the causal effects of the way in which the health care workforce is organised on healthcare use and patient outcomes. Our first project explores the determinants of variation in the quality and productivity of the most senior doctors (NHS consultants). It is becoming clear that there is wide variation across doctors in their patient outcomes, even within the same hospital. Some of this may be due to patient allocation across doctors, for example, giving the most experienced harder to treat patients. But this does not appear to be the only driver. Our research will seek to quantify this variation and to determine what factors associated with the doctors are associated with this variation. We will use large scale data in order to separate out the effect of the individual doctor and the hospitals in which they work by exploiting movement in doctors across hospitals during their careers. Our second and third projects examine team production. Teams are the dominant form of organisation of staff in healthcare and it is therefore important to understand the causal effects of changes to teams. Project two examines explores the impacts of anticipated and unanticipated disruptions to nursing teams on patient care and costs. To do so, we will exploit new data sources that provide detailed data on staff rotas across all wards in 5 hospitals, which can be linked to treatments and patient outcomes. Project three will explore the relationship between doctor seniority, productivity and patient outcomes by analysing a series of strikes by junior doctors in 2016 and 2017. These strikes changed the mix of staff treating patients, leading to a temporarily higher proportion of senior staff (NHS consultants) working in these teams. To conduct our research we will exploit several data sources, including rich administrative data from the Hospital Episode Statistics and newly available, highly granular, data from one large London NHS hospital group. Applications, benefits and impact: Our ultimate aim is to allow policymakers to better understand the role of the workforce in variation in productivity, hospital utilisation and patient outcomes. Our findings will provide information and tools that help policymakers improve the efficiency of the existing workforce, raise the quality of patient care, and inform future workforce planning. We will maximise impact by producing a range of outputs that communicate the results to multiple audiences. We will submit a series of academic articles to top economic journals. We will also produce a number of press-released non-technical reports, which summarise the key findings directed at journalists, policymakers and other non-academic users. In particular, we will target national policymakers, including the Department of Health and Social Care and Health Education England, and health care providers, such as individual Acute Trusts. We will also engage with health care workers and their representatives.

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