Adriana Castelli

A New Approach To Measuring Health System Output and Productivity

This paper considers methods to measure output and productivity in the delivery of health services, with an application to NHS hospital sector. It first develops a theoretical framework for measuring quality adjusted outputs and then considers how this might be implemented given available data. Measures of input use are discussed and productivity growth estimates are presented for the period 1998/9-2003/4. The paper concludes that available data are unlikely fully to capture quality improvements.

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.

Examining variations in hospital productivity in the English NHS

ObjectivesNumerous 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.MethodsHospital 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.ResultsHospital 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.ConclusionsWe 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.

The Determinants of Costs and Length of Stay for Hip Fracture Patients

Background and Purpose An ageing population at greater risk of proximal femoral fracture places an additional clinical and financial burden on hospital and community medical services. We analyse the variation in i) length of stay (LoS) in hospital and ii) costs across the acute care pathway for hip fracture from emergency admission, to hospital stay and follow-up outpatient appointments. Patients and Methods We analyse patient-level data from England for 2009/10 for around 60,000 hip fracture cases in 152 hospitals using a random effects generalized linear multi-level model where the dependent variable is given by the patient’s cost or length of stay (LoS). We control for socio-economic characteristics, type of fracture and intervention, co-morbidities, discharge destination of patients, and quality indicators. We also control for provider and social care characteristics. Results Older patients and those from more deprived areas have higher costs and LoS, as do those with specific co-morbidities or that develop pressure ulcers, and those transferred between hospitals or readmitted within 28 days. Costs are also higher for those having a computed tomography (CT) scan or cemented arthroscopy. Costs and LoS are lower for those admitted via a 24h emergency department, receiving surgery on the same day of admission, and discharged to their own homes. Interpretation Patient and treatment characteristics are more important as determinants of cost and LoS than provider or social care factors. A better understanding of the impact of these characteristics can support providers to develop treatment strategies and pathways to better manage this patient population.

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.

Health, policy and geography: Insights from a multi-level modelling approach

Improving the health and wellbeing of citizens ranks highly on the agenda of most governments. Policy action to enhance health and wellbeing can be targeted at a range of geographical levels and in England the focus has tended to shift away from the national level to smaller areas, such as communities and neighbourhoods. Our focus is to identify the potential for targeting policy interventions at the most appropriate geographical levels in order to enhance health and wellbeing. The rationale is that where variations in health and wellbeing indicators are larger, there may be greater potential for policy intervention targeted at that geographical level to have an impact on the outcomes of interest, compared with a strategy of targeting policy at those levels where relative variations are smaller. We use a multi-level regression approach to identify the degree of variation that exists in a set of health indicators at each level, taking account of the geographical hierarchical organisation of public sector organisations. We find that for each indicator, the proportion of total residual variance is greatest at smaller geographical areas. We also explore the variations in health indicators within a hierarchical level, but across the geographical areas for which public sector organisations are responsible. We show that it is feasible to identify a sub-set of organisations for which unexplained variation in health indicators is significantly greater relative to their counterparts. We demonstrate that adopting a geographical perspective to analyse the variation in indicators of health at different levels offers a potentially powerful analytical tool to signal where public sector organisations, faced increasingly with many competing demands, should target their policy efforts. This is relevant not only to the English context but also to other countries where responsibilities for health and wellbeing are being devolved to localities and communities.

Hospital trusts productivity in the English NHS: uncovering possible drivers of productivity variations

Background Health care systems in OECD countries are increasingly facing economic challenges and funding pressures. These normally demand interventions (political, financial and organisational) aimed at improving the efficiency of the health system as a whole and its single components. In 2009, the English NHS Chief Executive, Sir David Nicholson, warned that a potential funding gap of £20 billion should be met by extensive efficiency savings by March 2015. Our study investigates possible drivers of differential Trust performance (productivity) for the financial years 2010/11-2012/13. Methods Following accounting practice, we define Productivity as the ratio of Outputs over Inputs. We analyse variation in both Total Factor and Labour Productivity using ordinary least squares regressions. We explicitly included in our analysis factors of differential performance highlighted in the Nicholson challenge as the sources were the efficiency savings should come from. Explanatory variables include efficiency in resource use measures, Trust and patient characteristics, and quality of care. Results We find that larger Trusts and Foundation Trusts are associated with lower productivity, as are those treating a greater proportion of both older and/or younger patients. Surprisingly treating more patients in their last year of life is associated with higher Labour Productivity.

Productivity Growth in the English National Health Service from 1998/1999 to 2013/2014

Summary Productivity growth is a key measure against which National Health Service (NHS) achievements are judged. We measure NHS productivity growth as a set of paired year‐on‐year comparisons from 1998/1999–1999/2000 through 2012/2013–2013/2014, which are converted into a chained index that summarises productivity growth over the entire period. Our measure is as comprehensive as data permit and accounts for the multitude of diverse outputs and inputs involved in the production process and for regular revisions to the data used to quantify outputs and inputs. Over the full‐time period, NHS output increased by 88.96% and inputs by 81.58%, delivering overall total factor productivity growth of 4.07%. Productivity growth was negative during the first two terms of Blairs government, with average yearly growth rate of −1.01% per annum (pa) during the first term (to 2000/2001) and −1.49% pa during the second term (2000/2001–2004/2005). Productivity growth was positive under Blairs third term (2004/2005–2007/2008) at 1.41% pa and under the Brown government (2007/2008–2010/2011), averaging 1.13% pa. Productivity growth remained positive under the Coalition (2010/2011–2013/2014), averaging 1.56% pa.

Against All Odds: The Contribution of the Healthcare Sector to Productivity. Evidence from Italy and UK from 2004 to 2011

We assess the productivity growth of the English and Italian healthcare systems over the period from 2004 to 2011. The English (NHS) and the Italian (SSN) healthcare systems share many similar features, facilitating comparison: basic founding principles, financing, organization, management, and size. We measure productivity growth as the rate of change in outputs over the rate of change in inputs. We find that the overall NHS productivity growth index increased by 10% over the whole period, at an average of 1.39% per year, while SSN productivity increased overall by 5%, at an average of 0.73% per year. Differential growth reflects different policy objectives. In England, the NHS focused on increasing activity, reducing waiting times and improving quality. Italy focused more on cost containment and rationalized provision, in the hope that this would reduce unjustified and inappropriate provision of services.