Thesis Thursday: Thomas Hoe

On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Thomas Hoe who has a PhD from University College London. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Essays on the economics of health care provision
Supervisors
Richard Blundell, Orazio Attanasio
Repository link
http://discovery.ucl.ac.uk/10048627/

What data do you use in your analyses and what are your main analytical methods?

I use data from the English National Health Service (NHS). One of the great features of the NHS is the centralized data it collects, with the Hospital Episodes Statistics (HES) containing information on every public hospital visit in England.

In my thesis, I primarily use two empirical approaches. In my work on trauma and orthopaedic departments, I exploit the fact that the number of emergency trauma admissions to hospital each day is random. This randomness allows me to conduct a quasi-experiment to assess how hospitals perform when they are more or less busy.

The second approach I use, in my work on emergency departments with Jonathan Gruber and George Stoye, is based on bunching techniques that originated in the tax literature (Chetty et al, 2013; Kleven and Waseem, 2013; Saez, 2010). These techniques use interpolation to infer how discontinuities in incentive schemes affect outcomes. We apply and extend these techniques to evaluate the impact of the ‘4-hour target’ in English emergency departments.

How did you characterise and measure quality in your research?

Measuring the quality of health care outcomes is always a challenge in empirical research. Since my research primarily relies on administrative data from HES, I use the patient outcomes that can be directly constructed from this data: in-hospital mortality, and unplanned readmission.

Mortality is, of course, an outcome that is widely used, and offers an unambiguous interpretation. Readmission, on the other hand, is an outcome that has gained more acceptance as a measure of quality in recent years, particularly following the implementation of readmission penalties in the UK and the US.

What is ‘crowding’, and how can it affect the quality of care?

I use the term crowding to refer, in a fairly general sense, to how busy a hospital is. This could mean that the hospital is physically very crowded, with lots of patients in close proximity to one another, or that the number of patients outstrips the available resources.

In practice, I evaluate how crowding affects quality of care by comparing hospital performance and patient outcomes on days when hospitals deal with different levels of admissions (due to random spikes in the number of trauma admissions). I find that hospitals respond by not only cancelling some planned admissions, such as elective hip and knee replacements, but also discharge existing patients sooner. For these discharged patients, the shorter-than-otherwise stay in the hospital is associated with poorer health outcomes for patients, most notably an increase in subsequent hospital visits (unplanned readmissions).

How might incentives faced by hospitals lead to negative consequences?

One of the strongest incentives faced by public hospitals in England is to meet the government-set waiting time target for elective care. This target has been very successful at reducing wait times. In doing so, however, it may have contributed to hospitals shortening patient stays and increasing patient admissions.

My research shows that shorter hospitals stays, in turn, can lead to increases in unplanned readmissions. Setting strong wait time targets, then, is in effect trading off shorter waits (from which patients benefit) with crowding effects (which may harm patients).

Your research highlights the importance of time in the hospital production process. How does this play out?

I look at this from three dimensions, each a separate part of a patient’s journey through hospital.

The first two relate to waiting for treatment. For elective patients, this means waiting for an appointment, and previous work has shown that patients attach significant value to reductions in these wait times. I show that trauma and orthopaedic patients would be better off with further wait time reductions, even if that leads to more crowding.

Emergency patients, in contrast, wait for treatment while physically in a hospital emergency department. I show that these waiting times can be very harmful and that by shortening these wait times we can actually save lives.

The third dimension relates to how long a patient spends in hospital recovering from surgery. I show that, at least on the margin of care for trauma and orthopaedic patients, an additional day in hospital has tangible benefits in terms of reducing the likelihood of experiencing an unplanned readmission.

How could your findings be practically employed in the NHS to improve productivity?

I would highlight two areas of my research that speak directly to the policy debate about NHS productivity.

First, while the wait time targets for elective care may have led to some crowding problems and subsequently more readmissions, the net benefit of these targets to trauma and orthopaedic patients is positive. Second, the wait time target for emergency departments also appears to have benefited patients: it saved lives at a reasonably cost-effective rate.

From the perspective of patients, therefore, I would argue these policies have been relatively successful and should be maintained.

Thesis Thursday: Francesco Longo

On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Francesco Longo who has a PhD from the University of York. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Essays on hospital performance in England
Supervisor
Luigi Siciliani
Repository link
http://etheses.whiterose.ac.uk/18975/

What do you mean by ‘hospital performance’, and how is it measured?

The concept of performance in the healthcare sector covers a number of dimensions including responsiveness, affordability, accessibility, quality, and efficiency. A PhD does not normally provide enough time to investigate all these aspects and, hence, my thesis mostly focuses on quality and efficiency in the hospital sector. The concept of quality or efficiency of a hospital is also surprisingly broad and, as a consequence, perfect quality and efficiency measures do not exist. For example, mortality and readmissions are good clinical quality measures but the majority of hospital patients do not die and are not readmitted. How well does the hospital treat these patients? Similarly for efficiency: knowing that a hospital is more efficient because it now has lower costs is essential, but how is that hospital actually reducing costs? My thesis tries to answer also these questions by analysing various quality and efficiency indicators. For example, Chapter 3 uses quality measures such as overall and condition-specific mortality, overall readmissions, and patient-reported outcomes for hip replacement. It also uses efficiency indicators such as bed occupancy, cancelled elective operations, and cost indexes. Chapter 4 analyses additional efficiency indicators, such as admissions per bed, the proportion of day cases, and proportion of untouched meals.

You dedicated a lot of effort to comparing specialist and general hospitals. Why is this important?

The first part of my thesis focuses on specialisation, i.e. an organisational form which is supposed to generate greater efficiency, quality, and responsiveness but not necessarily lower costs. Some evidence from the US suggests that orthopaedic and surgical hospitals had 20 percent higher inpatient costs because of, for example, higher staffing levels and better quality of care. In the English NHS, specialist hospitals play an important role because they deliver high proportions of specialised services, commonly low-volume but high-cost treatments for patients with complex and rare conditions. Specialist hospitals, therefore, allow the achievement of a critical mass of clinical expertise to ensure patients receive specialised treatments that produce better health outcomes. More precisely, my thesis focuses on specialist orthopaedic hospitals which, for instance, provide 90% of bone and soft tissue sarcomas surgeries, and 50% of scoliosis treatments. It is therefore important to investigate the financial viability of specialist orthopaedic hospitals relative to general hospitals that undertake similar activities, under the current payment system. The thesis implements weighted least square regressions to compare profit margins between specialist and general hospitals. Specialist orthopaedic hospitals are found to have lower profit margins, which are explained by patient characteristics such as age and severity. This means that, under the current payment system, providers that generally attract more complex patients such as specialist orthopaedic hospitals may be financially disadvantaged.

In what way is your analysis of competition in the NHS distinct from that of previous studies?

The second part of my thesis investigates the effect of competition on quality and efficiency under two different perspectives. First, it explores whether under competitive pressures neighbouring hospitals strategically interact in quality and efficiency, i.e. whether a hospital’s quality and efficiency respond to neighbouring hospitals’ quality and efficiency. Previous studies on English hospitals analyse strategic interactions only in quality and they employ cross-sectional spatial econometric models. Instead, my thesis uses panel spatial econometric models and a cross-sectional IV model in order to make causal statements about the existence of strategic interactions among rival hospitals. Second, the thesis examines the direct effect of hospital competition on efficiency. The previous empirical literature has studied this topic by focusing on two measures of efficiency such as unit costs and length of stay measured at the aggregate level or for a specific procedure (hip and knee replacement). My thesis provides a richer analysis by examining a wider range of efficiency dimensions. It combines a difference-in-difference strategy, commonly used in the literature, with Seemingly Unrelated Regression models to estimate the effect of competition on efficiency and enhance the precision of the estimates. Moreover, the thesis tests whether the effect of competition varies for more or less efficient hospitals using an unconditional quantile regression approach.

Where should researchers turn next to help policymakers understand hospital performance?

Hospitals are complex organisations and the idea of performance within this context is multifaceted. Even when we focus on a single performance dimension such as quality or efficiency, it is difficult to identify a measure that could work as a comprehensive proxy. It is therefore important to decompose as much as possible the analysis by exploring indicators capturing complementary aspects of the performance dimension of interest. This practice is likely to generate findings that are readily interpretable by policymakers. For instance, some results from my thesis suggest that hospital competition improves efficiency by reducing admissions per bed. Such an effect is driven by a reduction in the number of beds rather than an increase in the number of admissions. In addition, competition improves efficiency by pushing hospitals to increase the proportion of day cases. These findings may help to explain why other studies in the literature find that competition decreases length of stay: hospitals may replace elective patients, who occupy hospital beds for one or more nights, with day case patients, who are instead likely to be discharged the same day of admission.

Alastair Canaway’s journal round-up for 31st October 2016

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

Ethical hurdles in the prioritization of oncology care. Applied Health Economics and Health Policy [PubMedPublished 21st October 2016

Recently between health economists, there has been significant scrutiny and disquiet directed towards the Cancer Drugs Fund with Professor Karl Claxton describing it as “an appalling, unfair use of NHS resources”. With the latest reorganization of the Cancer Drugs Fund in mind, this article examining the ethical issues surrounding prioritisation of cancer care was of particular interest. As all health economists will tell you, there is an opportunity cost with any allocation of scarce resources. Likewise, with prioritisation of specific disease groups, there may be equity issues with specific patients’ lives essentially being valued more greatly than those suffering other conditions. This article conducts a systematic review of the oncology literature to examine the ethical issues surrounding inequity in healthcare. The review found that public and political attention often focuses on ‘availability’ of pharmacological treatment in addition to factors that lead to good outcomes. The public and political focus on availability can have perverse consequences as highlighted by the Cancer Drugs Fund: resources are diverted towards availability and away from other more cost-effective areas, and in turn this may have had a detrimental effect on care for non-cancer patients. Additionally, by approving high cost, less cost-effective agents, strain will be placed upon health budgets and causing problems for existing cost-effectiveness thresholds. If prioritisation for cancer drugs is to be pursued then the authors suggest that the question of how to fund new therapies equitably will need to be addressed. Although the above issues will not be new to most, the paper is still worth reading as it: i) gives an overview of the different prioritisation frameworks used across Europe, ii) provides several suggestions for how, if prioritization is to be pursued, it can be done in a fairer manner rather than simply overriding typical HTA decision processes, iii) considers the potential legal consequences of prioritisation and iv) the impact of prioritisation on the sustainability of healthcare funding.

Doctor-patient differences in risk and time preferences: a field experiment. Journal of Health Economics Published 19th October 2016

The patient-doctor agency interaction, and associated issues due to asymmetrical information is something that was discussed often during my health economics MSc, but rarely during my day to day work. Despite being very familiar with supplier induced demand, differences in risk and time preferences in the patient-doctor dyad wasn’t something I’d considered in recent times. Upon reading, immediately, it is clear that if risk and time preferences do differ, then what is seen as the optimal treatment for the patient may be very different to that of the doctor. This may lead to poorer adherence to treatments and worse outcomes. This paper sought to investigate whether patients and their doctors had similar time and risk preferences using a framed field experiment with 300 patients and 67 doctors in Athens, Greece in a natural clinical setting. The authors claim to be the first to attempt this, and have three main findings: i) there were significant time preference differences between the patients and doctors – doctors discounted future health gains and financial outcomes less heavily than patients; ii) there were no significant differences in risk preferences for health with both doctors and patients being mildly risk averse; iii) there were however risk preference differences for financial impacts with doctors being more risk averse than patients. The implication of this paper is that there is potential for improvements in doctor-patient communication for treatments, and as agents for patients, doctors should attempts to gauge their patient’s preferences and attitudes before recommending treatment. For those who heavily discount the future it may be preferable to provide care that increases the short term benefits.

Hospital productivity growth in the English NHS 2008/09 to 2013/14 [PDF]. Centre for Health Economics Research Paper [RePEcPublished 21st October 2016

Although this is technically a ‘journal round-up’, this week I’ve chosen to include the latest CHE report as I think it is something which may be of wider interest to the AHEBlog community. Given limited resources, there is an unerring call for both productivity and efficiency gains within the NHS. The CHE report examines the extent to which NHS hospitals have improved productivity: have they made better use of their resources by increasing the number of patients they treat and the services they deliver for the same or fewer inputs. To assess productivity, the report uses established methods: Total Factor Productivity (TFP) which is the ratio of all outputs to all inputs. Growth in TFP is seen as being key to improving patient care with limited resources. The primary report finding was that TFP growth at the trust level exhibits ‘extraordinary volatility’. For example one year there maybe TFP growth followed by negative growth the next year, and then positive growth. The authors assert that much of the TFP growth measured is in fact implausible, and much of the changes are driven largely by nominal effects alongside some real changes. These nominal effects may be data entry errors or changes in accounting practices and data recording processes which results in changes to the timing of the recording of outputs and inputs. This is an important finding for research assessing productivity growth within the NHS. The TFP approach is an established methodology, yet as this research demonstrates, such methods do not provide credible measures of productivity at the hospital level. If hospital level productivity growth is to be measured credibly, then a new methodology will be required.

Credits