Mixed Methods EvAluation of the high-volume low-complexity Surgical hUb pRogrammE (MEASURE): a mixed methods study protocol.

INTRODUCTION: The waiting list for elective surgery in England recently reached over 7.8 million people and waiting time targets have been missed since 2010. The high-volume low complexity (HVLC) surgical hubs programme aims to tackle the backlog of patients awaiting elective surgery treatment in England. This study will evaluate the impact of HVLC surgical hubs on productivity, patient care and the workforce. METHODS AND ANALYSIS: This 4-year project consists of six interlinked work packages (WPs) and is informed by the Consolidated Framework for Implementation Research. WP1: Mapping current and future HVLC provision in England through document analysis, quantitative data sets (eg, Hospital Episodes Statistics) and interviews with national service leaders. WP2: Exploring the effects of HVLC hubs on key performance outcomes, primarily the volume of low-complexity patients treated, using quasi-experimental methods. WP3: Exploring the impact and implementation of HVLC hubs on patients, health professionals and the local NHS through approximately nine longitudinal, multimethod qualitative case studies. WP4: Assessing the productivity of HVLC surgical hubs using the Centre for Health Economics NHS productivity measure and Lord Carter's operational productivity measure. WP5: Conducting a mixed-methods appraisal will assess the influence of HVLC surgical hubs on the workforce using: qualitative data (WP3) and quantitative data (eg, National Health Service (NHS) England's workforce statistics and intelligence from WP2). WP6: Analysing the costs and consequences of HVLC surgical hubs will assess their achievements in relation to their resource use to establish value for money. A patient and public involvement group will contribute to the study design and materials. ETHICS AND DISSEMINATION: The study has been approved by the East Midlands-Nottingham Research Ethics Committee 23/EM/0231. Participants will provide informed consent for qualitative study components. Dissemination plans include multiple academic and non-academic outputs (eg, Peer-reviewed journals, conferences, social media) and a continuous, feedback-loop of findings to key stakeholders (eg, NHS England) to influence policy development. TRIAL REGISTRATION: Research registry: Researchregistry9364 (https://www.researchregistry.com/browse-the-registry%23home/registrationdetails/64cb6c795cbef8002a46f115/).

Measuring the overall performance of mental healthcare providers.

To date there have been no attempts to construct composite measures of healthcare provider performance which reflect preferences for health and non-health benefits, as well as costs. Health and non-health benefits matter to patients, healthcare providers and the general public. We develop a novel provider performance measurement framework that combines health gain, non-health benefit, and cost and illustrate it with an application to 54 English mental health providers. We apply estimates from a discrete choice experiment eliciting the UK general population's valuation of non-health benefits relative to health gains, to administrative and patient survey data for years 2013-2015 to calculate equivalent health benefit (eHB) for providers. We measure costs as forgone health and quantify the relative performance of providers in terms of equivalent net health benefit (eNHB): the value of the health and non-health benefits minus the forgone benefit equivalent of cost. We compare rankings of providers by eHB, eNHB, and by the rankings produced by the hospital sector regulator. We find that taking account of the non-health benefits in the eNHB measure makes a substantial difference to the evaluation of provider performance. Our study demonstrates that the provider performance evaluation space can be extended beyond measures of health gain and cost, and that this matters for comparison of providers.

The impact of COVID-19 on mental health service utilisation in England.

The COVID-19 pandemic has had a significant impact on population mental health and the need for mental health services in many countries, while also disrupting critical mental health services and capacity, as a response to the pandemic. Mental health providers were asked to reconfigure wards to accommodate patients with COVID-19, thereby reducing capacity to provide mental health services. This is likely to have widened the existing mismatch between demand and supply of mental health care in the English NHS. We quantify the impact of these rapid service reconfigurations on activity levels for mental health providers in England during the first thirteen months (March 2020-March 2021) of the COVID-19 pandemic. We use monthly mental health service utilisation data for a large subset of mental health providers in England from January 1, 2015 to March 31, 2021. We use multivariate regression to estimate the difference between observed and expected utilisation from the start of the pandemic in March 2020. Expected utilisation levels (i.e. the counterfactual) are estimated from trends in utilisation observed during the pre-pandemic period January 1, 2015 to February 31, 2020. We measure utilisation as the monthly number of inpatient admissions, discharges, net admissions (admissions less discharges), length of stay, bed days, number of occupied beds, patients with outpatient appointments, and total outpatient appointments. We also calculate the accumulated difference in utilisation from the start of the pandemic period. There was a sharp reduction in total inpatient admissions and net admissions at the beginning of the pandemic, followed by a return to pre-pandemic levels from September 2020. Shorter inpatient stays are observed over the whole period and bed days and occupied bed counts had not recovered to pre-pandemic levels by March 2021. There is also evidence of greater use of outpatient appointments, potentially as a substitute for inpatient care.

Integrated Care for Multimorbidity Population in Asian Countries: A Scoping Review.

Background: The complex needs of patients with multiple chronic diseases call for integrated care (IC). This scoping review examines several published Asian IC programmes and their relevant components and elements in managing multimorbidity patients. Method: A scoping review was conducted by searching electronic databases encompassing Medline, Embase, Scopus, and Web of Science. Three key concepts - 1) integrated care, 2) multimorbidity, and 3) Asian countries - were used to define searching strategies. Studies were included if an IC programme in Asia for multimorbidity was described or evaluated. Data extraction for IC components and elements was carried out by adopting the SELFIE framework. Results: This review yielded 1,112 articles, of which 156 remained after the title and abstract screening and 27 studies after the full-text screening - with 23 IC programmes identified from seven Asian countries. The top 5 mentioned IC components were service delivery (n = 23), workforce (n = 23), leadership and governance (n = 23), monitoring (n = 15), and environment (n = 14); whist financing (n = 9) was least mentioned. Compared to EU/US countries, technology and medical products (Asia: 40%, EU/US: 43%-100%) and multidisciplinary teams (Asia: 26%, EU/US: 50%-81%) were reported less in Asia. Most programmes involved more micro-level elements that coordinate services at the individual level (n = 20) than meso- and macro-level elements, and programmes generally incorporated horizontal and vertical integration (n = 14). Conclusion: In the IC programmes for patients with multimorbidity in Asia, service delivery, leadership, and workforce were most frequently mentioned, while the financing component was least mentioned. There appears to be considerable scope for development. Highlights: First scoping review to synthesise the key components and elements of integrated care programmes for multimorbidity in Asia.All programmes emphasized 'distinctive service delivery', 'leadership', and 'workforce' components.'Financing' component was least mentioned in identified integrated care programmes.

Valuing quality in mental healthcare: A discrete choice experiment eliciting preferences from mental healthcare service users, mental healthcare professionals and the general population.

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.

Response to COVID-19: was Italy (un)prepared?

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.

Corona-regionalism? Differences in regional responses to COVID-19 in Italy.

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.

Unravelling the mental health productivity puzzle: where do we start?

In 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.

Assessing the design of China's complex health system - Concerns on equity and efficiency.

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.

Can productivity growth measures identify best performing hospitals? Evidence from the English National Health Service.

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.

How health policy shapes 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.

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.

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.

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.

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.

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.

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.

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.

Improving the measurement of health system output growth.

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.