l Understanding whether or not high-volume CCGs are eliminating unmet need.
l Understanding low referral rates at practices with high rates of patient treatment.
l The APC analysis suggests that the period effect is dominant and it would be beneficial to determine why.
l Understanding better the reasons for varying referral rates for practices with different sociodemographic characteristics.
l Theoretical modelling and further empirical research is required to clarify the relationship between emergency and elective treatments, from the viewpoint of patient demand and hospital supply.
Funding
Funding for this study was provided by the Health Services and Delivery Research programme of the National Institute for Health Research.
Chapter 1
Background and research objectives
T
here has been considerable growth in the number of planned care episodes–between 2001/2 and 2011/12, admissions increased by 35.4%–but such growth during a period of financial pressure would create substantial fiscal and hospital management problems. The causes of this rise in activity are poorly understood and, consequently, the likely path of planned care growth is also poorly understood. There are useful databases available both to model demand and, if required, to moderate it with least impact on patients [e.g. Hospital Episode Statistics (HES), patient-reported outcome measures (PROMs), referral numbers from Choose and Book by general practitioner (GP), practice, specialty and hospital], but these raw data are of limited value in both strategic and patient-level decisions. We aim to add to the literature by interrogating some of these data sets and producing analyses that will help commissioners to make appropriate decisions.The overall aim of this project is to contribute to a better understanding of how to moderate activity growth in ways that minimise the loss of patient health, by exploring hypotheses that would extend the literature by deepening understanding of local health economies and providing evidence for commissioners to minimise the health loss that may accompany diminished budgets, and to support GPs both as commissioners and in terms of their clinical performance. This is done in a collection of related but independent studies that look at differing aspects of planned care, considering national health policies, more local interventions in primary and secondary care, and the provision of benchmarking information for Clinical Commissioning Groups (CCGs), using similar ideas to those in the NHS Atlases.1
Both system reform and population ageing are possible drivers of admissions growth and provide barriers to readily introducing measures to manage growth. We ask how important these two issues are in explaining rapid admissions growth.Chapter 2presents estimates of the separate influences of system reform and capacity growth in explaining the post-2002 increase in elective care at local levels. This is intended to examine how far the rise in planned admissions has been prompted by system reform rather than by increases in capacity using the Scottish health-care system, which did not undergo the same reforms, as a control.
One specific piece of reform, namely the introduction of a tariff system [Payment by Results (PbR)] to replace a block grant funding model in some service areas has influenced provision and may have contributed to changing activity patterns.Chapter 3examines the effect of such system reform on the extent of variation across the NHS. This study complements others that have examined the influence of reform on the rates of admission.2
The ageing population has also been regarded as a key driver of elective admissions growth (e.g. see Reinhardt3) and we examine this inChapter 4. Using an age
–period–cohort (APC) analysis of elective admissions and bed-days per 1000 population, we separate the roles of age, year of admission and year of birth on the rates of admission and the rates of bed-days used. Thus, we partition the increase in elective activity from 1997/8 to 2014/15 into an age effect (factors associated with the patient’s age, A), a period effect (the year of the patient’s admission, P) and a cohort effect (the patient’s year of birth, C). This allows the impact of ageing to be assessed while also allowing the likelihood of entering, or continuing in, hospital to vary with the year of birth of the individual. In particular, we study whether or not later generations are less likely to enter hospital at a given age. We also apply APC analysis to selected groups of procedures to test consistency across different conditions.
InChapters 5and6, we provide an analysis of two policies that commissioners have considered, and that some have adopted, to ameliorate admissions: (1) an increase in the provision of GPs and (2) a constraint or target imposed on GP referrals. To study whether or not an increase in the density of GP provision would lead to reduced admissions, we begin by providing a model of GP referrals that is consistent with NHS objectives to maximise patient welfare. We discuss how a single-payer health system is better
incentivised than a competitive insurance system to train and monitor GPs to maximise patient welfare, and how NHS GPs may act as gatekeepers when making referrals. This builds on Mariñoso and Jelovac,4
one of the few theoretical studies of the relative benefits of gatekeeping. In Mariñoso and Jelovac’s4
model, gatekeeping arises because GP treatments are cheaper; our model does not make this assumption. The provision of more GPs reduces the patient load and changes referral behaviour. We carry out an empirical investigation to gauge the effect that these density variables have on admissions, and then to assess how far a policy of reducing referrals may lead to a reduction in treatment. We also consider the impact of practice size by full-time equivalent (FTE) GP. This study provides information on the effectiveness of a policy that works by increasing GP treatment in primary care and reducing referrals, rather than by acting on hospital incentives to reduce activity.
One concern is that growth in elective care has reduced referral thresholds (e.g. see Keenanet al.,5which
looks at time trends and geographical variation in cataract surgery rates), and some CCGs have introduced policies to require GPs to refer fewer patients. Commissioners are also likely to value evidence on how far changing thresholds for pre-operative conditions for patients reflect the decisions of GP referrals or those of consultants.Chapter 6uses a panel of GP practice data to study (1) the rate of first referral per 1000 patients and (2) the rate of hospital admission for those given a first referral. We examine the cross-sectional relationship between referral rates and treatment rates to see if it is broadly consistent with the view that patients in certain local areas are healthier than others and will experience lower referrals and lower admissions. We also explore the impact of specific exogenous influences on referrals, such as GP experience and sex and patient and practice characteristics. The rate of treatments following first referral at a practice is then modelled as a function of the referral rate and exogenous influences, allowing for local practice-level unobserved health effects to impact on both referrals and admissions. These models of treatment and referral enable us to trace the impact of reduced referrals on the level of treatment when practices have sharply different referral rates.
Devlin and Appleby6note the potential value of PROMs data for commissioning, but, to date, PROMs data
have been used only to analyse providers’services (e.g. Streetet al.7) and patient benefits. The studies in
Chapters 7and8use PROMs data to focus on ways to (1) minimise the consequences of reduced hospital activity, by identifying pre-operative characteristics of patients who gain least from intervention and
(2) uncover whether or not variation in patient heath gain across CCGs, adjusted for case mix, is attributable to the CCGs themselves, the providers they commission and the surgical teams the providers employ. Finally, we consider whether or not any attempts to manage demand for elective care will have undesired consequences for the demand of emergency care, using an extensive data set and the natural experiment provided by the introduction of independent sector treatment centres (ISTCs) in the past decade. Many studies have looked at elective and emergency activity levels separately.8–10However, work studying the
interaction between emergency and elective care has been limited. Equally, there have been several studies of ISTCs (e.g. Cooperet al.11) but little work on their effect on emergency activity.
This is a challenging time for the NHS and novel and imaginative solutions are required to ensure that the NHS can continue to treat patients in the best way possible. We hope that the work that follows in this report will help to inform the debate and improve understanding of how commissioners and providers can continue to provide the level of performance expected by their patients while operating under budgetary pressures.