Objective Solutions to quality and safety problems exist within healthcare organisations, but to maximise the learning from these positive deviants, we first need to identify them. This study explores using routinely collected, publicly available data in England to identify positively deviant services in one region of the country. Design A mixed methods study undertaken July 2014 to February 2015, employing expert discussion, consensus and statistical modelling to identify indicators of quality and safety, establish a set of criteria to inform decisions about which indicators were robust and useful measures, and whether these could be used to identify positive deviants. Setting Yorkshire and Humber, England. Participants None - analysis based on routinely collected, administrative English hospital data. Main outcome measures We identified 49 indicators of quality and safety from acute care settings across eight data sources. Twenty-six indicators did not allow comparison of quality at the sub-hospital level. Of the 23 remaining indicators, 12 met all criteria and were possible candidates for identifying positive deviants. Results Four indicators (readmission and patient reported outcomes for hip and knee surgery) offered indicators of the same service. These were selected by an expert group as the basis for statistical modelling, which supported identification of one service in Yorkshire and Humber showing a 50% positive deviation from the national average. Conclusion Relatively few indicators of quality and safety relate to a service level, making meaningful comparisons and local improvement based on the measures difficult. It was possible, however, to identify a set of indicators that provided robust measurement of the quality and safety of services providing hip and knee surgery.

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Prospective payment arrangements are now the main form of hospital funding in most developed countries. An essential component of such arrangements is the classification system used to differentiate patients according to their expected resource requirements. In this article we describe the evolution and structure of Healthcare Resource Groups (HRGs) in England and the way in which costs are calculated for patients allocated to each HRG. We then describe how payments are made, how policy has evolved to incentivise improvements in quality, and how prospective payment is being applied outside hospital settings.

This paper discusses key challenges and opportunities that arise when using linked electronic health records (EHR) in health economics and outcomes research (HEOR), with a particular focus on estimating healthcare costs. These challenges and opportunities are framed in the context of a case study modelling the costs of stable coronary artery disease in England. The challenges and opportunities discussed fall broadly into the categories of (1) handling and organising data of this size and sensitivity; (2) extracting clinical endpoints from datasets that have not been designed and collected with such endpoints in mind; and (3) the principles and practice of costing resource use from routinely collected data. We find that there are a number of new challenges and opportunities that arise when working with EHR compared with more traditional sources of data for HEOR. These call for greater clinician involvement and intelligent use of sensitivity analysis.

Does health policy shape healthcare sector productivity? Evidence from Italy and UK. 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.