Analysing Patient-Level Data using Hospital Episode Statistics (HES)

This course includes instruction on how to:

  • understand, manage and manipulate the data
  • construct and analyse key variables such as waiting times or length of stay
  • analyse individual patient records defined as Finished Consultant Episodes, Provider Spells and Continuous Inpatient Spells
  • monitor emergency readmissions
  • aggregate data by Healthcare Resource Group or providers/commissioners
  • cost data by HRG and reference costs
  • evaluate Patient Reported Outcome Measures (PROMS)
  • use the data for benchmarking and policy evaluation

The tutors have worked extensively with HES data and will guide participants through the potential pitfalls using case studies, practical examples and problem-solving exercises.

Analysing Patient-Level Data using HES Workshop

This intensive workshop introduces participants to HES (Hospital Episode Statistics) data and how to handle and manipulate these very large patient-level data sets using computer software. Understanding and interpreting the data is a key first step for using these data in economic evaluation or evaluating health care policy and practice. Participants will engage in lectures and problem-solving exercises, analysing the information in highly interactive sessions. Data manipulation and statistical analysis will be taught and demonstrated using Stata.

This workshop is offered to people in the academic, public and commercial sectors.  It is useful for analysts who wish to harness the power of HES non-randomised episode level patient data to shed further light on such things as patient costs and pathways, re-admissions and outcomes and provider performance.  The workshop is suitable for individuals working in NHS hospitals, commissioning organisations, NHS England, Monitor, and the Department of Health and Social Care, pharmaceutical companies or consultancy companies and for health care researchers and PhD students.  Overseas participants may find the tuition helpful for their own country, but note that the course is heavily oriented towards understanding HES data for England.

The workshop fee is 900GBP for the public sector; 1,400GBP for the commercial sector. This includes all tuition, course materials, lunches, the welcome and drinks reception, the workshop dinner and refreshments, but does not include accommodation.

Online registration is now open; further information and registration is at: https://www.york.ac.uk/che/courses/patient-data/

Subsidised places are available for full-time PhD students. If this is applicable to you, please email the workshop administrators and request an Application Form.

Contact: Gillian or Louise, Workshop Administrators, at: che-apd@york.ac.uk;  tel: +44 (0)1904 321436

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.