Examining variations in hospital productivity in the English NHS.

OBJECTIVES: Numerous papers have measured hospital efficiency, mainly using a technique known as data envelopment analysis (DEA). A shortcoming of this technique is that the number of outputs for each hospital generally outstrips the number of hospitals. In this paper, we propose an alternative approach, involving the use of explicit weights to combine diverse outputs into a single index, thereby avoiding the need for DEA. METHODS: Hospital productivity is measured as the ratio of outputs to inputs. Outputs capture quantity and quality of care for hospital patients; inputs include staff, equipment, and capital resources applied to patient care. Ordinary least squares regression is used to analyse why output and productivity varies between hospitals. We assess whether results are sensitive to consideration of quality. RESULTS: Hospital productivity varies substantially across hospitals but is highly correlated year on year. Allowing for quality has little impact on relative productivity. We find that productivity is lower in hospitals with greater financial autonomy, and where a large proportion of income derives from education, research and development, and training activities. Hospitals treating greater proportions of children or elderly patients also tend to be less productive. CONCLUSIONS: We have set out a means of assessing hospital productivity that captures their multiple outputs and inputs. We find substantial variation in productivity among English hospitals, suggesting scope for productivity improvement.

Regional variation in the productivity of the English national health service.

Variation in the provision of health care has long been a policy concern. We adapt the framework for productivity measurement used in the National Accounts, making it applicable for sub-national comparisons using cross-sectional data. We assess the productivity of the National Health Service (NHS) across regions of England, termed Strategic Health Authorities (SHAs). Productivity is calculated by comparing the total amount of healthcare output to total inputs for each region, standardised to the national average. Healthcare output comprises 6500 different categories, capturing the number and type of NHS patients treated and the quality of care received. Healthcare inputs include NHS and agency staff, supplies, equipment and capital. We find that productivity varies from 5% above to 6% below the national average. Productivity is highest in South West SHA and lowest in East Midlands, South Central and Yorkshire and The Humber SHAs. We estimate that if all regions were as productive as the most productive region in England, the NHS could treat the same number of patients with £3.2bn fewer resources each year. The methods developed lend themselves to investigate variations in productivity in other types of healthcare organisations and health systems.

How well do diagnosis-related groups for appendectomy explain variations in resource use? An analysis of patient-level data from 10 European countries.

Appendectomy is a common and relatively simple procedure to remove an inflamed appendix, but the rate of appendectomy varies widely across Europe. This paper investigates factors that explain differences in resource use for appendectomy. We analysed 106,929 appendectomy patients treated in 939 hospitals in 10 European countries. In stage 1, we tested the performance of three models in explaining variation in the (log of) cost of the inpatient stay (seven countries) or length of stay (three countries). The first model used only the diagnosis-related groups (DRGs) to which patients were coded, the second model used a core set of general patient-level and appendectomy-specific variables, and the third model combined both sets of variables. In stage two, we investigated hospital-level variation. In classifying appendectomy patients, most DRG systems take account of complex diagnoses and comorbidities but use different numbers of DRGs (range: 2 to 8). The capacity of DRGs and patient-level variables to explain patient-level cost variation ranges from 34% in Spain to over 60% in England and France. All DRG systems can make better use of administrative data such as the patient's age, diagnoses and procedures, and all countries have outlying hospitals that could improve their management of resources for appendectomy.

Why do patients having coronary artery bypass grafts have different costs or length of stay? An analysis across 10 European countries.

We analyse variations in cost or length of stay (LoS) for 66,587 patients from 10 European countries receiving a coronary artery bypass graft (CABG) procedure. In five of these countries, variations in cost are analysed using log-linear models. In the other five countries, negative binomial regression models are used to explore variations in LoS. We compare how well each country's diagnosis-related group (DRG) system and a set of patient-level characteristics explain these variations. The most important explanatory factors are the total number of diagnoses and procedures, although no clear effects are evident for our CABG-specific diagnostic and procedural variables. Wound infections significantly increase LoS and costs in most countries. There is no evidence that countries using larger numbers of DRGs to group CABG patients are better at explaining variations in cost or LoS. However, refinements to the construction of DRGs to group CABG patients might recognise first and subsequent CABGs or other specific surgical procedures, such as multiple valve repair.

Getting out what we put in: productivity of the English National Health Service.

Many countries are incorporating direct measures of non-market outputs in the national accounts. For any particular output to be included there has to be data about it for two adjacent periods. This is problematic because the classification of non-market outputs is often subject to wholesale revision. We outline the challenges associated with classification changes and propose a solution. To illustrate we construct output and input indices and estimate productivity growth of the English National Health Service (NHS) for the period 2003-2004 to 2007-2008. Our index of output growth incorporates all care provided to NHS patients and captures improvements in survival rates, waiting times and disease management. We find that more patients are being treated and the quality of the care they receive has been improving. We implement our approach to dealing with changes as to how health services are defined and show what effect this has on estimates of output growth. Our index of input growth captures all labour, intermediate and capital inputs into health service production and we improve on how capital has been measured in the past. Inputs have increased over time but there has also been a slowdown since 2005-2006, primarily the result of a levelling off in staff recruitment and less reliance on the use of agency staff. Productivity is assessed by comparing output growth with growth in inputs, the net effect being constant productivity growth between 2003-2004 and 2007-2008.