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On 31st January 2020, the Italian cabinet declared a 6-month national emergency after the detection of the first two COVID-19 positive cases in Rome, two Chinese tourists travelling from Wuhan. Between then and the total lockdown introduced on 22nd March 2020 Italy was hit by an unprecedented crisis. In addition to being the first European country to be heavily swept by the COVID-19 pandemic, Italy was the first to introduce stringent lockdown measures. The SARS-CoV-2 outbreak and related COVID-19 pandemic have been the worst public health challenge endured in recent history by Italy. Two months since the beginning of the first wave, the estimated excess deaths in Lombardy, the hardest hit region in the country, reached a peak of more than 23,000 deaths. The extraordinary pressures exerted on the Italian Servizio Sanitario Nazionale (SSN) inevitably leads to questions about its preparedness and the appropriateness and effectiveness of responses implemented at both national and regional levels. The aim of the paper is to critically review the Italian response to the COVID-19 crisis spanning from the first early acute phases of the emergency (March–May 2020) to the relative stability of the epidemiological situation just before the second outbreak in October 2020.

High and sustained healthcare quality is important worldwide, though health policy may prioritise the achievement of certain aspects of quality over others. This study determines the relative importance of different aspects of mental healthcare quality to different stakeholders by eliciting preferences in a UK sample using a discrete choice experiment (DCE). DCE attributes were generated using triangulation between policy documents and mental healthcare service user and mental healthcare professional views, whilst ensuring attributes were measurable using available data. Ten attributes were selected: waiting times; ease of access; person-centred care; co-ordinated approach; continuity; communication, capacity and resources; treated with dignity and respect; recovery focus; inappropriate discharge; quality of life (QoL). The DCE was conducted online (December 2018 to February 2019) with mental healthcare service users (n = 331), mental healthcare professionals (n = 510), and members of the general population (n = 1018). Respondents' choices were analysed using conditional logistic regression. Relative preferences for each attribute were generated using the marginal rate of substitution (MRS) with QoL as numeraire. Across all stakeholders, being treated with dignity and respect was of high importance. A coordinated approach was important across all stakeholders, whereas communication had higher relative importance for healthcare professionals and service users and ease of access had higher relative importance for the general population. This implies that policy could be affected by the choice of whose preferences (service users, healthcare professionals or general population) to use, since this impacts on the relative value and implied ranking of different aspects of mental healthcare quality.

Health‐care systems around the world face limited financial resources, and England is no exception. The ability of the health‐care system in England to operate within its financial resources depends in part on continually increasing its productivity. One means of achieving this is to identify and disseminate throughout the system the most efficient processes. We examine the annual productivity growth achieved by 151 hospitals over five financial years, using the same methods developed to measure productivity of the National Health Service as a whole. We consider whether there are hospitals that consistently achieve higher than average productivity growth. These could act as examples of good practice for others to follow and provide a means of increasing system performance. We find that the productivity growth of some hospitals over the whole period exhibits better than average performance, but there is little or no evidence of consistency in the performance of these hospitals over adjacent years. Even the best performers exhibit periods of very poor performance and vice versa. We therefore conclude that accepted methods of measuring productivity growth for the health system as a whole do not appear suitable for identifying good performance at the hospital level.

The paper discusses the responses to the COVID-19 crisis in the acute phase of the first wave of the pandemic (February-May 2020) by different Italian regions in Italy, which has a decentralised healthcare system. We consider five regions (Lombardy, Veneto, Emilia-Romagna, Umbria, Apulia) which are located in the north, centre and south of Italy. These five regions differ both in their healthcare systems and in the extent to which they were hit by the first wave of COVID-19 pandemic. We investigate their different responses to COVID-19 reflecting on seven management factors: (1) monitoring, (2) learning, (3) decision-making, (4) coordinating, (5) communicating, (6) leading, and (7) recovering capacity. In light of these factors, we discuss the analogies and differences among the regions and their different institutional choices.

SummaryIn this editorial we define ‘productivity’ and ‘efficiency’ in a mental health service context, outlining the key challenges to measuring these in practice. We attempt to bring clarity of thought to this often debated, but rarely researched area.

Following the global trend of moving towards Universal Health Coverage, China has implemented a new round of health system reform, to achieve universal “safe, effective and affordable basic healthcare services” by 2020. We review the latest reforms using the World Health Organization framework developed by Murray and Frenk. In particular, we diagrammatically describe the structure of the current Chinese health system using the dimensions of Stewardship, Resource Generation, Financing and Provision, and assess the variability of access, levels of benefits, and quality of service across populations. We identified several areas of inequity and inefficiency. First, the fragmented institutional arrangements, with distinct objectives and responsibilities across agencies, create potential nonalignment of incentives. Second, there is a marked scarcity of qualified general practitioners and infrastructures despite the continuing effort to improve the gatekeeping function of primary care providers. Third, as risks are pooled only at the local level within different insurance schemes, the considerable income heterogeneity across geographic territories and resident types can generate significant inequality in access and funding. Fourth, persistent patient preference for higher quality healthcare at hospitals prevents the integration of care across tiers. We believe our comprehensive analysis will be informative for both health policymakers and researchers, in identifying and investigating the inefficiencies of the health system and the potentials for structural integration to achieve healthcare equity.

The English (NHS) and the Italian (SSN) healthcare systems share many similar features: basic founding principles, financing, organization, management, and size. Yet the two systems have faced diverging policy objectives since 2000, which may have affected differently healthcare sector productivity in the two countries. In order to understand how different healthcare policies shape the productivity of the systems, we assess, using the same methodology, the productivity growth of the English and Italian healthcare systems over the period from 2004 to 2011. Productivity growth is measured 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. Our results suggest that different policy objectives are reflected in differential growth rates for the two countries. 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.

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 Blair's 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 Blair's 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. © 2016 The Authors Health Economics Published by John Wiley & Sons Ltd.

This chapter introduces the political focus on the productivity of the NHS. Productivity is a conceptually simple construct, relating the amount of output produced to the amount of inputs used in the production process. Productivity growth can also be calculated by comparing the change in outputs produced to the change in inputs utilised from one period to the next. Political interest is focused primarily on productivity growth. However, assessing productivity or efficiency is somewhat problematic in the context of a free-at-the-point-of-use health system service such as the NHS. The authors discuss some of the issues they face in attempting such an evaluation. With these issues in mind, the authors describe the growth in outputs, inputs and productivity between 2004/05 and 2013/14. The chapter concludes by discussing whether or not the key political measures highlighted in the first section appear to have had an influence on NHS productivity growth.

In 2009, the NHS Chief Executive 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 years 2010/11-2012/13. Productivity is measured as Outputs/Inputs. We extend previous productivity work at Trust level by including a fuller range of care settings, including Inpatient, A&E and Community Care, in our output measure. Inputs include staff, equipment, and capital resources. We analyse variation in Total Factor and Labour Productivity with ordinary least squares regressions. Explanatory variables include efficiency in resource use measures, Trust and patient characteristics. We find productivity varies substantially across Trusts but is consistent across time. Larger Trusts are associated with lower productivity. Patient age groups treated is also found to be important. Foundation Trust status is associated with lower Total Factor Productivity, while treating more patients in their last year of life is surprisingly associated with higher Labour Productivity. Variation in productivity is persistent across years, and not fully explained by case-mix adjustment. A lack of convergence in productivity may indicate outstanding scope to improve Trust productivity based on mimicking the practises of the most productive providers.

ObjectivesThe effective exploitation of what are often called big data is increasingly important. They provide the evidence in evidence-based health care and underpin scientific progress in many domains including social/economic policy. Typically, an optimal analysis involves working directly with microdata; i.e. the detailed data relating to each individual in the dataset. But there are many ethico-legal and other governance restrictions on physically sharing microdata. Furthermore, researchers or institutions may have an extensive intellectual property investment in complex microdata and although keen for other researchers to analyse their data they may not wish to give them a physical copy. These restrictions can discourage the use of optimum approaches to analysing pivotal data and slow scientific progress. Data science groups across the world are exploring privacy-protected approaches to analysing microdata without having to physically share the data. ApproachA two day international workshop was arranged focussing on privacy protected approaches to data analysis – particularly federated analysis where raw data remain at their original site of collection. The workshop considered the range of approaches that exist, and those that are currently being developed. It explored the strengths, weaknesses, opportunities and challenges associated with these methods and identified situations where specific approaches have a particularly important role. The workshop included a number of practical sessions where potential users could watch demonstrations of the various approaches in action and run analyses themselves. ResultsThe Data Analysis with Privacy Protection for Epidemiological Research (DAPPER) workshop was held 22-23rd August 2016, Bristol. We report back to the broader community on the outcomes of this workshop that focussed on exploring current approaches, tools and technical solutions that facilitate sensitive data to be shared and analysed. ConclusionsThe workshop has helped map out key opportunities and challenges and assisted potential users, developers and other stakeholders (e.g. funders/journals) to recognise the strengths and weaknesses of different privacy protected analytic approaches. The workshop will encourage further methodological work in this field and better informed application of existing methods.

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.

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. Electronic supplementary material The online version of this article (doi:10.1007/s10198-014-0569-5) contains supplementary material, which is available to authorized users.

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.

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.

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 Blair's 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 Blair's 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. © 2016 The Authors Health Economics Published by John Wiley & Sons Ltd.

Productivity is one of the key measures against which NHS achievements can be judged and is the focus of this report. We update our previous analyses of NHS productivity growth since 2004/05, focussing on the change in NHS productivity between 2011/12 and 2012/13, the latter financial year being the latest for which data have been made available. NHS productivity growth is measured as the rate of change in outputs over the rate of change of inputs. Positive productivity growth occurs when the relative growth in outputs exceeds the relative growth in inputs. NHS output captures all activity for NHS patients using data from the Hospital Episode Statistics (HES), Reference Cost returns and primary care use survey data. Quality is captured by waiting times, 30-day survival rates, and blood pressure management in primary care. Output growth amounted to 2.34% between 2011/12 and 2012/13, this being the lowest year-on-year growth rate over the full period since 2004/05. This is the first time over the full series in which quality-adjusted output growth has been lower than cost-weighted growth, which amounted to 2.58%. This is because some aspects of quality deteriorated between 2011/12 and 2012/13, with a reduction in survival rates for non-elective patients and further increases in waiting times. NHS inputs include of NHS and agency staff, intermediates and capital. NHS staff input is measured using staff numbers as recorded in the Electronic Staff Record and also from expenditure data. All other inputs are measured by deflating expenditure data by relevant price indices to capture changes in the volume of resource use. We construct two overall measures of NHS inputs, with our preferred mixed index using NHS staff numbers and an indirect index, which uses expenditure data to calculate NHS staff input. NHS input growth between 2011/12 and 2012/13 was 1.98% if labour input is calculated using NHS staff numbers or 2.63% if using expenditure data. This rate of input growth is relatively low for the series as a whole but it is the largest year-on-year increase since 2009/10. Productivity growth between 2011/12 and 2012/13 is estimated to have been 0.36% based on the mixed input index but -0.28% if based on the indirect input index. If measured using the preferred mixed index, the NHS has delivered overall total factor productivity growth of 10.4% since 2004/05, with 2011/12-2012/13 being the third consecutive period of year-on-year productivity growth.

Background and purposeAn 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 methodsWe 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.ResultsOlder 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.InterpretationPatient 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.

We measure the productivity of the health care sector over time by comparing the total amount of health care ‘output’ produced to the total amount of ‘input’ used to produce this output in accordance with Eurostat conventions (Eurostat, 2001). To construct a time series, we need to account for changes in routine data collection procedures, such as data coverage and changing activity definitions. To do this we construct a series of chained indices for both output and input growth in consecutive years. This allows us to calculate a like-with-like productivity growth series for the English National Health Service for the time period from 2004/5 to 2011/12.

Overview - 2010/11 was the first full financial year of a Coalition government committed to meeting the so-called Nicholson challenge of making £20bn efficiency savings in projected NHS expenditure by 2015. Securing improvements in NHS productivity is seen as a key element in meeting this challenge. In what follows we report year-on-year changes in productivity from 2004/5 to 2010/11.

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.

A new research study reveals that the productivity of the NHS in England has been broadly constant over the last seven years, increasing by an average of 0.1 per cent per year. The most detailed and comprehensive information available was used to compare growth in the total amount of resources (input) used to produce health care provided to NHS patients (output). The research shows that between 2003/4 to 2009/10 the number of staff has increased by 18 per cent, buildings and equipment by 24 per cent and all other inputs, such as clinical supplies and energy costs, by 76 per cent. There has also been a corresponding increase in both the quantity and quality of output. The number of patients treated in hospital increased from 12.1m to 15.6m; outpatient attendances from 50m to 77m; community care contacts from 76m to 92m; and primary care consultations from 262m to 300m. Over the same period, hospital survival rates improved from 99.4 per cent to 99.8 per cent for elective patients and from 95 per cent to 96 per cent for non-electives. Average inpatient waiting times fell from 78 to 57 days, reaching a low of 51 days in 2008/9. Outpatient waiting times fell from 58 days to 24 days. All in all, growth in activity and changes in quality have tracked the growth in inputs, implying that productivity has been flat over the seven year period.

We explore in this research paper the concept of avoidable mortality and how the way it is measured has evolved over time. Starting from an earlier review by Nolte and McKee (2004), we review the empirical studies which have been produced since then. Finally we appraise the empirical applications of the most recent literature. The concept of “avoidable mortality” refers, broadly speaking, to all those deaths that, given current medical knowledge and technology, could be avoided by the healthcare system through either prevention and/or treatment. It originates from the pioneering work by Rutstein, Berenberg et al. (1976) which introduced the notion of 'unnecessary untimely deaths' as a new way to measuring the quality of medical care. The most recent empirical literature shows that the notion of avoidable mortality continues to be used to establish the extent to which people are dying from amenable conditions within and/or across countries and over time, and whether socio-economic status and ethnicity are related to mortality from amenable conditions. Most studies use data taken from national death registries, with only two which link the concept of avoidable mortality to routinely collected administrative data of healthcare provision, such as hospitals. A number of criticisms are raised, with probably the most remarkable being the lack of association found between avoidable mortality and healthcare inputs. No study has actually attempted to use the concept of avoidable mortality within the original aim envisaged by Rutstein, i.e. as a quality indicator of healthcare provision. We recommend for future work in this area to focus on investigating the link between the provision of healthcare and the concept of avoidable mortality, with a particular emphasis on using routinely collected administrative data, such as hospital discharge data.

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.

The fundamental aim of public services is to improve the quality of life of citizens. The main objective of this study was to investigate the influence of public service organisations (PSOs) on aspects of quality of life (broadly measured) of citizens at a local level. We assembled a rich database using 20 of the 45 quality of life measures developed by the Audit Commission. Those we selected covered broad areas of quality of life such as safety, housing, health, education, and transport and were available at ‘small area’ level. We used a range of advanced statistical methods to analyse the relationships between PSOs and quality of life measures at different hierarchical levels. The techniques were selected to be robust when making comparisons between levels and when looking at associations between quality of life measures. Our descriptive analyses (bivariate correlations, factor analysis and ANOVA) suggested overall some significant correlations between some of the quality of life variables. The SUR model results also indicated that the quality of life indicators are correlated, and therefore that we should look at these measures in a joint modelling approach such as MVML, as envisaged in the study objectives.

We report estimates of output growth for the National Health Service in England over the period 2003/4 to 2006/7. Our output index is virtually comprehensive, capturing as far as possible all the activities undertaken for NHS patients by both NHS and non-NHS providers across all care settings. We assess the quality of output by measuring the waiting times and survival status of every single patient treated in hospital, and we allow for improved disease management in primary care. We propose and apply a method that avoids the traditional requirement for consistent definition of output categories over time in construction of output indices. Use of our approach is critical: it would be not otherwise be possible to calculate output growth for the NHS over the years we consider in any meaningful way. After correcting for significant improvements in data collection in the early period, output growth for the NHS between 2003/4 to 2006/7 averages 5.1% per year, of which 1% is due to improvements in the quality of care.

National income accounting practice is to weight health service activities by their cost so that they can be aggregated into an output index. Quality changes are ignored. We propose an 'ideal' value weighted output index in which the value attached to each output reflects its contribution to health outcomes and other characteristics valued by patients. Calculation of the index for the health system as a whole is currently infeasible because of a lack of data, especially on health outcomes. We demonstrate alternative ways of combining health outcome data with existing information on post-treatment survival, life expectancy and waiting times to construct quality adjusted cost weighted and health outcome weighted indices for a small set of hospital activities for which there are health outcome data.

Following the publication of the Atkinson Review of the measurement of government outputs in the National Accounts, there has been great interest in measuring the productivity growth of the National Health Service. Such macro measures of productivity are important when deciding how much public money to devote to the NHS, and in holding the NHS to account. However, it is also important to gain an understanding of the productivity of individual programmes of care, so as to ensure that resources are allocated efficiently within the NHS. Hitherto, such information has not been available. This report is an exploratory study of the feasibility and usefulness of developing measures of growth in outputs, costs and productivity of a single programme of care within the NHS: hospital treatment of circulatory diseases.

The Centre for Health Economics and National Institute of Economic and Social Research have recently completed a project funded by the Department of Health to improve measurement of the productivity of the NHS. The researchers have suggested better ways of measuring both outputs and inputs to improve estimates of productivity growth. Past estimates of NHS output growth have not taken account of changes in quality. The CHE/NIESR team conclude that the routine collection of health outcome data on patients is vital to measure NHS quality. They also propose making better use of existing data to quality adjust output indices to capture improvements in hospital survival rates and reductions in waiting times. With these limited adjustments the team estimate that annual NHS output growth averaged 3.79% between 1998/99 and 2003/04.The research team has also developed improved ways of measuring NHS inputs, particularly by drawing on better information about how many people are employed in the NHS and by recognising that staff are becoming increasingly better qualified. There have been substantial increases in staffing levels, pharmaceutical use and investment in equipment and buildings since 1998/99. The net effect of this growth in both outputs and inputs is that, according to the research team’s estimates, NHS productivity declined by about 1.59% a year since 1998/99. This is not out of line with estimates of growth rates in other UK and US service sectors, including insurance and business services. Nor is it surprising that recent years have seen negative growth in the NHS. There are at least two reasons. First, there has been an unprecedented increase in NHS expenditure. The NHS has had to employ more staff to meet the requirements of the European Working Time Directive and hospital consultants and general practitioners, in particular, have benefited from new pay awards.Second, the NHS collects very little information about what actually happens to patients as a result of their contact with the health service. Until there is routine collection of health outcomes data, measurement of the quality of NHS output will remain partial and productivity growth is likely to be underestimated.