Non-parametric and parametric applications measuring efficiency in health care

Longitudinal bi-criteria framework for assessing national healthcare responses to pandemic outbreaks

Pandemics like COVID-19 have illuminated the significant disparities in the performance of national healthcare systems (NHCSs) during rapidly evolving crises. The challenge of comparing NHCS performance has been a difficult topic in the literature. To address this gap, our study introduces a bi-criteria longitudinal algorithm that merges fuzzy clustering with Data Envelopment Analysis (DEA). This new approach provides a comprehensive and dynamic assessment of NHCS performance and efficiency during the early phase of the pandemic. By categorizing each NHCS as an efficient performer, inefficient performer, efficient underperformer, or inefficient underperformer, our analysis vividly represents performance dynamics, clearly identifying the top and bottom performers within each cluster of countries. Our methodology offers valuable insights for performance evaluation and benchmarking, with significant implications for enhancing pandemic response strategies. The study's findings are discussed from theoretical and practical perspectives, offering guidance for future health system assessments and policy-making.

Assessing Similarity in Production from Organizational and Department-Level Financial Accounting Statements

Hospitals are complex organizations which provide a wide array of health care services. This complexity creates challenges for stakeholders who wish to use financial accounting statements to make inferences about the productive choices made by a hospital's management. These challenges are especially salient when using data reported at the department (or cost center) level, or where the provision of care is coordinated across hospital departments. This study applies information entropy-based comparability analysis techniques to overall and department-level hospital financial data to identify hospital peer groups. Hospitals peer groups not only exhibit similar financial positions overall, but are also likely to exhibit operational similarities at the department level. Data for this analysis are drawn from the financial statements of Washington State critical access hospitals in the fiscal year 2019. The medical laboratory and pharmacy departments were specifically assessed because their services impact or support virtually every other revenue-producing department in the hospital. Findings suggest both departments significantly impact the formation of peer groups, with the pharmacy department contributing the largest impact.

Bakar AF. Approach in inputs & outputs selection of Data Envelopment Analysis (DEA) efficiency measurement in hospitals: A systematic review

The efficiency and productivity evaluation process commonly employs Data Envelopment Analysis (DEA) as a performance tool in numerous fields, such as the healthcare industry (hospitals). Therefore, this review examined various hospital-based DEA articles involving input and output variable selection approaches and the recent DEA developments. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was utilised to extract 89 English articles containing empirical data between 2014 and 2022 from various databases (Web of Science, Scopus, PubMed, ScienceDirect, Springer Link, and Google Scholar). Furthermore, the DEA model parameters were determined using information from previous studies, while the approaches were identified narratively. This review grouped the approaches into four sections: literature review, data availability, systematic method, and expert judgement. An independent single strategy or a combination with other methods was then applied to these approaches. Consequently, the focus of this review on various methodologies employed in hospitals could limit its findings. Alternative approaches or techniques could be utilised to determine the input and output variables for a DEA analysis in a distinct area or based on different perspectives. The DEA application trend was also significantly similar to that of previous studies. Meanwhile, insufficient data was observed to support the usability of any DEA model in terms of fitting all model parameters. Therefore, several recommendations and methodological principles for DEA were proposed after analysing the existing literature.

Efficiency evaluation of 28 health systems by MCDA and DEA

BACKGROUND

Policymakers, who are constantly discussing growing health expenditures, should know whether the health system is efficient. We can provide them with such information through international health system efficiency evaluations. The main objectives of this study are: (a) to evaluate the efficiency of health systems in 28 developed countries by multiple-criteria decision analysis (MCDA) and data envelopment analysis (DEA) and (b) to identify reasonable benchmark countries for the Czech Republic, for which we collect information on the relative importance of health system inputs and outputs.

METHODS

We used MCDA and DEA to evaluate the efficiency of the health systems of 28 developed countries. The models included four health system inputs (health expenditure as a relative share of GDP, the number of physicians, nurses, and hospital beds) and three health system outputs (life expectancy at birth, healthy life expectancy, and infant mortality rate). The sample covers 27 OECD countries and Russia, which is also included in the OECD database. To determine the input and output weights, we used a questionnaire sent to health policy experts in the Czech Republic.

RESULTS

We obtained subjective information on the relative importance of the health system inputs and outputs from 27 Czech health policy experts. We evaluated health system efficiency using four MCDA and two DEA models. According to the MCDA models, Turkey, Poland, and Israel were found to have efficient health systems. The Czech Republic ranked 16th, 19th, 15th, and 17th. The benchmark countries for the Czech Republic's health system were Israel, Estonia, Luxembourg, Italy, the UK, Spain, Slovenia, and Canada. The DEA model with the constant returns to scale identified four technically efficient health systems: Turkey, the UK, Canada, and Sweden. The Czech Republic was found to be one of the worst-performing health systems. The DEA model with the variable returns to scale identified 15 technically efficient health systems. We found that efficiency results are quite robust. With two exceptions, the Spearman rank correlations between each pair of models were statistically significant at the 0.05 level.

CONCLUSIONS

During the model formulation, we investigated the pitfalls of efficiency measurement in health care and used several practical solutions. We consider MCDA and DEA, above all, as exploratory methods, not methods providing definitive answers.

Evaluating the Effectiveness of Maternal, Neonatal, and Child Healthcare in Moroccan Hospitals and SDG 3: Using Two-Stage Data Envelopment Analysis and Tobit Regression

Maternal, neonatal, and child health play crucial roles in achieving the objectives of Sustainable Development Goal (SDG) 2030, particularly in promoting health and wellbeing. However, maternal, neonatal, and child services in Moroccan public hospitals face challenges, particularly concerning mortality rates and inefficient resource allocation, which hinder optimal outcomes. This study aimed to evaluate the operational effectiveness of 76 neonatal and child health services networks (MNCSN) within Moroccan public hospitals. Using Data Envelopment Analysis (DEA), we assessed technical efficiency (TE) employing both Variable Returns to Scale for inputs (VRS-I) and outputs (VRS-O) orientation. Additionally, the Tobit method (TM) was utilized to explore factors influencing inefficiency, with hospital, doctor, and paramedical staff considered as inputs, and admissions, cesarean interventions, functional capacity, and hospitalization days as outputs. Our findings revealed that VRS-I exhibited a higher average TE score of 0.76 compared to VRS-O (0.23). Notably, the Casablanca-Anfa MNCSN received the highest referrals (30) under VRS-I, followed by the Khemisset MNCSN (24). In contrast, under VRS-O, Ben Msick, Rabat, and Mediouna MNCSN each had three peers, with 71, 22, and 17 references, respectively. Moreover, the average Malmquist Index under VRS-I indicated a 7.7% increase in productivity over the 9-year study period, while under VRS-O, the average Malmquist Index decreased by 8.7%. Furthermore, doctors and functional bed capacity received the highest Tobit model score of 0.01, followed by hospitalization days and cesarean sections. This study underscores the imperative for policymakers to strategically prioritize input factors to enhance efficiency and ensure optimal maternal, neonatal, and child healthcare outcomes.

Applications of data envelopment analysis in acute care hospitals: a systematic literature review, 1984-2022

This study reviews scholarly publications on data envelopment analysis (DEA) studies on acute care hospital (ACH) efficiency published between 1984 and 2022 in scholarly peer-reviewed journals. We employ systematic literature review (SLR) method to identify and analyze pertinent past research using predetermined steps. The SLR offers a comprehensive resource that meticulously analyzes DEA methodology for practitioners and researchers focusing on ACH efficiency measurement. The articles reviewed in the SLR are analyzed and synthesized based on the nature of the DEA modelling process and the key findings from the DEA models. The key findings from the DEA models are presented under the following sections: effects of different ownership structures; impacts of specific healthcare reforms or other policy interventions; international and multi-state comparisons; effects of changes in competitive environment; impacts of new technology implementations; effects of hospital location; impacts of quality management interventions; impact of COVID-19 on hospital performance; impact of teaching status, and impact of merger. Furthermore, the nature of DEA modelling process focuses on use of sensitivity analysis; choice of inputs and outputs; comparison with Stochastic Frontier Analysis; use of congestion analysis; use of bootstrapping; imposition of weight restrictions; use of DEA window analysis; and exogenous factors. The findings demonstrate that, despite several innovative DEA extensions and hospital applications, over half of the research used the conventional DEA models. The findings also show that the most often used inputs in the DEA models were labor-oriented inputs and hospital beds, whereas the most frequently used outputs were outpatient visits, followed by surgeries, admissions, and inpatient days. Further research on the impact of healthcare reforms and health information technology (HIT) on hospital performance is required, given the number of reforms being implemented in many countries and the role HIT plays in enhancing care quality and lowering costs. We conclude by offering several new research directions for future studies.

Optimizing immunization services: A Data Envelopment Analysis (DEA) of child immunization facilities in Pakistan

INTRODUCTION

Child immunization, though cost-beneficial, experiences varying costs influenced by individual facility-level factors. A real-time solution is to optimize resources and enhance vaccination services through proper method to measure immunization facility efficiency using existing data. Additionally, examine the impact of COVID-19 on facility efficiency, with the primary goal of comprehensively assessing child immunization facility efficiency in Pakistan.

METHODS

Utilizing survey data collected in four rounds from May 2018 to December 2020, the research focuses on doses administered and stock records for the preceding six months in each phase. In the initial stage, Data Envelopment Analysis (DEA) is utilized to compute facility efficiency, employing two models with varied outputs while maintaining consistent inputs. Model 1 assesses doses administered, encompassing three outputs (pentavalent vaccine 1, 2, and 3). Meanwhile, Model 2, focuses on stock used featuring a single output (total doses used). The inputs considered in both models include stock availability, staff members, cold chain equipment, vaccine carriers, and vaccine sessions. The second stage involves the application of two competing regression specifications (Tobit and Simar-Wilson) to explore the impact of the COVID-19 pandemic and external factors on the efficiency of these facilities.

RESULTS

In 12 districts across Punjab and Sindh, we assess 466 facilities in Model 1 and 455 in Model 2. Model 1 shows 59% efficiency, and Model 2 shows 70%, indicating excess stock. Stock of vaccines need to be reduced by from 36% to 43%. In the stage, COVID-19 period reduced efficiency in Model 1 by 10%, however, insignificant in Model 2.

CONCLUSIONS

The proposed methodology, utilizing DEA, emerges as a valuable tool for immunization facilities seeking to improve resource utilization and overall efficiency. Model 1, focusing on doses administered indicates facilities low efficiency at average 59% and proves more pertinent for efficiency analysis as it directly correlates with the number of children vaccinated. The prevalent issue of overstocking across all facilities significantly impacts efficiency. This study underscores the critical importance of optimizing resources through the redistribution of excess stock with low efficiency.

Non-homogeneity in the efficiency evaluation of health systems

BACKGROUND

An international comparison of health system performance is a popular tool of health policy analysis. However, the efficiency evaluation of health systems is a practical example of an international comparison in which non-homogeneity is expected. The objective of this paper is to evaluate the efficiency of health systems by models in which a degree of non-homogeneity among countries is considered.

METHODS

We study the problem of non-homogeneity of health systems in the theoretical framework of the data envelopment analysis (DEA), which is a popular method of efficiency evaluation with hundreds of applications from various fields. DEA assume the homogeneity of production units and the homogeneity of the environment in which the production units operate. Hence, we compiled a summary of 14 recommendations on how to deal with the non-homogeneity in the DEA models. The analysed sample includes 38 OECD member countries. The data are from the year 2019.

RESULTS

As an example, we evaluated the health system efficiency of the Czech Republic. We used the DEA models with the neighbourhood measure of distance and the constraint limiting the comparison of countries with different levels of economic development. The health system inputs were the numbers of physicians, nurses, and hospital beds. In the production of the intermediate outputs (doctor consultations, inpatient care discharges), the Czech Republic should look at Poland, Slovakia and Slovenia. In the production of health outcomes (life expectancy), the peer countries are France, Italy and Switzerland.

CONCLUSIONS

The results of the DEA analysis are only indicative because no single analytical method can determine whether a health system is better or worse than others. We need to combine different methods, and DEA is one of them. We consider DEA as an exploratory method, not a method providing definitive answers.

Efficiency and productivity analysis of maternal and infant healthcare services in Sub-Saharan Africa

The paper examines the efficiency and productivity of Sub-Saharan African (SSA) countries towards maternal and infant healthcare services between 2015 and 2019. Data envelopment analysis is utilised to evaluate efficiency, and Malmquist-Luenberger's (ML) productivity estimation is employed for productivity analysis. The results indicate inefficiency in SSA maternal and infant healthcare services. Average efficiency is pegged at 85%, and 60% of the countries evaluated had below-average efficiency. Effects of socioeconomic dynamics of countries were analysed. Preliminary estimations on the impact of Gross domestic product (GDP), education, urban population, and total population on efficiency are not significant. Although GDP and education sometimes show that they influence efficiency positively. Sensitivity analysis indicates efficiency to be more responsive to health expenditure, as well as to nurses and midwives. ML Productivity decomposition into technical efficiency change and technological change indicates improvement in technical efficiency as the principal driver of efficiency and productivity. Policy recommendations are made in line with the findings, requirements, and constraints of SSA countries.

Analyzing the effect of health reforms on the efficiency of Ecuadorian public hospitals

This study aims to assess whether Ecuadorian health reforms carried out since 2008 have affected the efficiency performance of public hospitals in the country. We contribute to the literature by shedding new light on the effects on public healthcare efficiency for developing countries when policies move toward health equity and universal coverage. We follow a two-stage approach, wherein the first stage we make use of factor and cluster analysis to obtain three clusters of public hospitals based on their technological endowment; we exploit Data Envelopment Analysis for panel data in the second stage to estimate robust efficiency measures over time. Our innovative empirical strategy considers the heterogeneity of healthcare institutions in the analysis of their efficiency performance. The results show a significant decrease in the average efficiency of low and intermediate technology hospitals after the new constitution was adopted in 2008. The decline in efficiency coincides with the two reforms of 2010 and 2011 that brought on higher social security coverage.

Hospital cost efficiency: an examination of US acute care inpatient hospitals

The use of stochastic frontier models for inference on hospital efficiency is complicated by the inability to fully control for quality differences across hospitals. Additionally, the potential existence of cross-sectional dependence due to the presence of unobserved common factors leads to endogeneity problems that can bias both cost function and efficiency estimates. Using a panel consisting of 1518 hospitals for the years 1996-2013 (T = 18), I adopt techniques for dealing with long, cross-sectionally dependent panel data in order to estimate cost parameters and hospital specific efficiency. In particular, I employ the estimation technique proposed by Bai (Econometrica 77(4):1229-1279, 2009), which assumes that the unobservable heterogenous effects have a factor structure. I find evidence of considerable scale economies and that hospital cost inefficiencies have been increasing during the period of 1996-2013, and that the growth in expenditures is, in part, driven by spending that increases patient satisfaction, but that does not significantly contribute to improved patient health outcomes.

Data envelopment analysis on the efficiency of vaccination services and its influencing factors in Beijing, China

OBJECTIVES

Vaccination is an important part of public health services. We aim to assess the efficiency of vaccination services in Beijing, the capital of China, and to further study the influencing factors of efficiency.

METHODS

Using the immunization service data of Beijing, China in 2020, we firstly developed a data envelopment analysis (DEA) model to calculate the score of vaccination efficiency. Secondly, we used DEA model scenario simulations with different combinations of input-output factors to derive the magnitude of the effect of each input factor on the efficiency. Finally, combined with the data from the Beijing Regional Statistical Yearbook 2021, we developed the Tobit model to examine the effect of external social environmental factors on efficiency.

RESULTS

The average scores of efficiency of POVs (Point of Vaccination) in different areas of Beijing vary greatly. Different input factors had different degrees of positive effects on the efficiency score. In addition, the number of populations served by POV was positively associated with efficiency, the GDP and financial allocation of the POVs' district was also positively associated with efficiency score, while the total dependency ratio of the POVs' district was negatively associated with efficiency score.

CONCLUSION

The efficiency of vaccination services varied considerably across POVs. Constrained by limited resources, efficiency scores can be increased by increasing input factors that have a larger impact on efficiency score and reducing those that have a smaller impact on efficiency. In addition, the social environment should be considered in allocating vaccination resources, and more resources should be invested in areas with low levels of economic development, low financial allocation, and high population.

Estimating the efficiency of primary health care services and its determinants: evidence from provincial panel data in China

Background

The efficiency of primary health care services is drawing increased attention worldwide, especially in developing countries. Health care reform in China has moved into the 'deep water zone' phase and is facing the dilemma of inefficiency in primary health care services, which is a critical challenge for universal health coverage.

Methods

In this study, we estimate the efficiency of primary health care services in China and its determinants. A combination of a super-SBM (Slack-Based Measure) model, a Malmquist productivity index model and a Tobit model is used to study provincial panel data, and the results demonstrate the inefficiency of primary health care services in China and the variations in efficiency values between regions.

Results

Over time, the productivity of primary health care services shows a decreasing trend, mainly due to slowing technology change. Financial support is needed to improve the efficiency of primary health care services, but it is worth noting that existing social health insurance coverage decreases efficiency, while economic development, urbanization and education also have a significant impact.

Conclusion

The findings suggest that increasing financial support should remain a priority in developing countries but that reasonable reimbursement design, appropriate payment methods and comprehensive supporting social health insurance policies are key to the next step of reform.

Technical Efficiency Evaluation of Primary Health Care Institutions in Shenzhen, China, and Its Policy Implications under the COVID-19 Pandemic

(1) Background: Primary health care institutions (PHCI) play an important role in reducing health inequities and achieving universal health coverage. However, despite the increasing inputs of healthcare resources in China, the proportion of patient visits in PHCI keeps declining. In 2020, the advent of the COVID-19 pandemic further exerted a severe stress on the operation of PHCI due to administrative orders. This study aims to evaluate the efficiency change in PHCI and provide policy recommendations for the transformation of PHCI in the post-pandemic era. (2) Methods: Data envelope analysis (DEA) and the Malmquist index model were applied to estimate the technical efficiency of PHCI in Shenzhen, China, from 2016 to 2020. The Tobit regression model was then used to analyze the influencing factors of efficiency of PHCI. (3) Results: The results of our analysis reflect considerable low levels of technical efficiency, pure technical efficiency, and scale efficiency of PHCI in Shenzhen, China, in 2017 and 2020. Compared to years before the epidemic, the productivity of PHCI decreased by 24.6% in 2020, which reached the nadir, during the COVID-19 pandemic along with the considerable reduction of technological efficiency, despite the significant inputs of health personnel and volume of health services. The growth of technical efficiency of PHCI is significantly affected by the revenue from operation, percentage of doctors and nurses in health technicians, ratio of doctors and nurses, service population, proportion of children in the service population, and numbers of PHCI within one kilometer. (4) Conclusion: The technical efficiency significantly declines along with the COVID-19 outbreak in Shenzhen, China, with the deterioration of underlying technical efficiency change and technological efficiency change, regardless of the immense inputs of health resources. Transformation of PHCI such as adopting tele-health technologies to maximize primary care delivery is needed to optimize utilization of health resource inputs. This study brings insights to improve the performances of PHCI in China in response to the current epidemiologic transition and future epidemic outbreaks more effectively, and to promote the national strategy of Healthy China 2030.

Analysis of the effect of patients' health status on efficiency: Application of data envelopment analysis in healthcare

The effective and efficient operation of public healthcare systems is more and more important as a consequence of the increasing amount of money spent on their operation. For this reason, accurate and science-based efficiency information is needed for managers and healthcare policy makers. The evaluation of the efficiency in this sector is particularly difficult because several measures and indicators are used. Data envelopment analysis (DEA) can provide aggregate and overall measures of efficiency. The purpose of this paper is the examination of the efficiency of in-patient rehabilitation units curing musculoskeletal disorders in Hungary. The research presented focuses on rehabilitation units which attend to patients recovering from stroke or other acquired brain injuries. Output-oriented, slack-based DEA models are applied for the evaluation of in-patient rehabilitation units using data of a national survey. The novelty of the method presented is the consideration of the change in patients' functional status when efficiency is evaluated using DEA.

A network Data Envelopment Analysis to estimate nations' efficiency in the fight against SARS-CoV-2

The ongoing outbreak of SARS-CoV-2 has been deeply impacting health systems worldwide. In this context, it is pivotal to measure the efficiency of different nations' response to the pandemic, whose insights can be used by governments and health authorities worldwide to improve their national COVID-19 strategies. Hence, we propose a network Data Envelopment Analysis (DEA) to estimate the efficiencies of fifty-five countries in the current crisis, including the thirty-seven Organisation for Economic Co-operation and Development (OECD) member countries, six OECD prospective members, four OECD key partners, and eight other countries. The network DEA model is designed as a general series structure with five single-division stages - population, contagion, triage, hospitalisation, and intensive care unit admission -, and considers an output maximisation orientation, denoting a social perspective, and an input minimisation orientation, denoting a financial perspective. It includes inputs related to health costs, desirable and undesirable intermediate products related to the use of personal protective equipment and infected population, respectively, and desirable and undesirable outputs regarding COVID-19 recoveries and deaths, respectively. To the best of the authors' knowledge, this is the first study proposing a cross-country efficiency measurement using a network DEA within the context of the COVID-19 crisis. The study concludes that Estonia, Iceland, Latvia, Luxembourg, the Netherlands, and New Zealand are the countries exhibiting higher mean system efficiencies. Their national COVID-19 strategies should be studied, adapted, and used by countries exhibiting worse performances. In addition, the observation of countries with large populations presenting worse mean efficiency scores is statistically significant.

Efficiency and Productivity Differences in Healthcare Systems: The Case of the European Union

This study aims to identify significant differences between the countries of the European Union, follow the course of achievement of the convergence objectives, assess developments against specific common characteristics of the countries, and propose possible measures that could improve the state of health in the EU as a whole by implementing standard cohesion policies. To compare efficiency and productivity among the states of the European Union, we used data envelopment analysis (DEA) and the Malmquist productivity index (MPI). On the basis of our findings, even countries that joined the EU later achieve high technical efficiency values. However, it should be noted that it is in these countries that technical efficiency values tend to decline. The values of the Malmquist productivity index broadly indicate stagnation in western countries and productivity decline in central and eastern European countries. This decline is mainly due to a negative shift in the technological frontier in these countries.

Assessing the Efficiency and Productivity of the Hospital Clinics on the Island of Rhodes during the COVID-19 Pandemic

(1) Background: The aim was to measure the efficiency and productivity of 15 specialty clinics during the COVID-19 pandemic period 2020-2021 in the General Hospital of Rhodes. (2) Methods: An input-oriented data envelopment analysis and the Malmquist productivity index are used. Labor and capital were used as inputs, and in-patient discharges and days were used as outputs. (3) Results: Five out of the seven clinics in the pathology sector appeared fully efficient with an optimal productivity, and the rest showed progress in 2021. In 2020 the COVID-19 pathology clinic appeared to be inefficient and less productive, while in 2021, it showed a positive performance change. The surgical sector showed very high efficiency rates or even reached an optimal efficiency in both years. The productivity measurement, in most of the surgical clinics, was satisfactory to very high. In 2020 the COVID-19 surgical clinic appeared to be more efficient and productive than in 2021 when its performance declined. (4) Conclusions: The hospital responded to the pressure during the pandemic, by increasing its efficiency and productivity from 2020 to 2021. This was due to the accomplishment of the appropriate organizational changes in the infrastructure, human resources, and technology. The efficiency and productivity assessments should be incorporated in the hospitals' decision making.

An observational study of health care provider collaboration networks and heterogenous hospital cost efficiency and quality outcomes

Provider network structure has been linked to hospital cost, utilization, and to a lesser degree quality, outcomes; however, it remains unknown whether these relationships are heterogeneous across different acute care hospital characteristics and US states. The objective of this study is to evaluate whether there are heterogeneous relationships between hospital provider network structure and hospital outcomes (cost efficiency and quality); and to assess the sources of measured heterogeneous effects. We use recent causal random forest techniques to estimate (hospital specific) heterogeneous treatment effects between hospitals' provider network structures and their performance (across cost efficiency and quality). Using Medicare cost report, hospital quality and provider patient sharing data, we study a population of 3061 acute care hospitals in 2016. Our results show that provider networks are significantly associated with costs efficiency (P < .001 for 7/8 network measures), patient rating of their care (P < .1 in 5/8 network measures), heart failure readmissions (P < .01 for 3/8 network measures), and mortality rates (P < .02 in 5/8 cases). We find that fragmented provider structures are associated with higher costs efficiency and patient satisfaction, but also with higher heart failure readmission and mortality rates. These effects are further found to vary systematically with hospital characteristics such as capacity, case mix, ownership, and teaching status. This study used an observational design. In summary, we find that hospital treatment responses to different network structures vary systematically with hospital characteristics..

How is the medical service efficiency in China? An empirical analysis using stochastic frontier approach and gravity models

The COVID-19 has heightened the focus of medical services. Scientifically evaluating the efficiency of medical services and defining their spatial transmission relationship is crucial for the rational allocation of health resources and the accomplishment of balanced regional medical service growth. We used a Stochastic Frontier Model to calculate medical service performance in Chinese provinces and the Gravity Model to study the spatial relationship of medical service performance across provinces using data from 2009 to 2018. We discover that: (1) population density and proportion of technical personnel are significantly positively correlated with the efficiency of regional medical services, whereas health institution density has a significantly negative influence. Their respective influence coefficients were 1.717, 0.647, and 0.407. (2) In China, the regional development of medical service efficiency is unbalanced. The east, middle, and west multi-year average medical service efficiency were 0.65, 0.46, and 0.53, respectively, and their gaps were narrowing; the south and north average efficiency were 0.591, 0.516, respectively, and their gaps were widening. (3) Our medical efficiency network is not yet widespread in the country. Hubei, Henan, Shandong, Jiangsu, Zhejiang, and Beijing were at the centre of the medical efficiency network, driving and connecting the nation's medical service. Our findings offer specific recommendations for better understanding and improving the efficiency of medical services.

Estimating health care delivery system value for each US state and testing key associations

OBJECTIVE

To estimate health care systems' value in treating major illnesses for each US state and identify system characteristics associated with value.

DATA SOURCES

Annual condition-specific death and incidence estimates for each US state from the Global Burden Disease 2019 Study and annual health care spending per person for each state from the National Health Expenditure Accounts.

STUDY DESIGN

Using non-linear meta-stochastic frontier analysis, mortality incidence ratios for 136 major treatable illnesses were regressed separately on per capita health care spending and key covariates such as age, obesity, smoking, and educational attainment. State- and year-specific inefficiency estimates were extracted for each health condition and combined to create a single estimate of health care delivery system value for each US state for each year, 1991-2014. The association between changes in health care value and changes in 23 key health care system characteristics and state policies was measured.

DATA COLLECTION/EXTRACTION METHODS

Not applicable.

PRINCIPAL FINDINGS

US state with relatively high spending per person or relatively poor health-outcomes were shown to have low health care delivery system value. New Jersey, Maryland, Florida, Arizona, and New York attained the highest value scores in 2014 (81 [95% uncertainty interval 72-88], 80 [72-87], 80 [71-86], 77 [69-84], and 77 [66-85], respectively), after controlling for health care spending, age, obesity, smoking, physical activity, race, and educational attainment. Greater market concentration of hospitals and of insurers were associated with worse health care value (p-value ranging from <0.01 to 0.02). Higher hospital geographic density and use were also associated with worse health care value (p-value ranging from 0.03 to 0.05). Enrollment in Medicare Advantage HMOs was associated with better value, as was more generous Medicaid income eligibility (p-value 0.04 and 0.01).

CONCLUSIONS

Substantial variation in the value of health care exists across states. Key health system characteristics such as market concentration and provider density were associated with value.

Evaluation of Unified Healthcare Efficiency in China: A Meta-Frontier Non-radial Directional Distance Function Analysis During 2009–2019

In this study, we analyze the unified healthcare efficiency in China at the regional level from 2009 to 2019. To accurately evaluate the evolution of unified efficiency from both static and dynamic perspectives, we combine the non-radial directional distance function and the meta-frontier method to evaluate the unified healthcare efficiency and its dynamic changes. This new approach allows for regional heterogeneity and non-radial slack simultaneously. The decomposition of the meta-frontier non-radial Malmquist unified healthcare efficiency index (MNMHEI) can be used to identify the driving factors of dynamic changes. The results show that the unified healthcare efficiency in eastern China is generally higher than that in non-eastern China from the static perspective, implying significant regional differences. Moreover, the unified efficiency in both eastern and non-eastern regions shows similar time trends and reaches the maximum in 2012. From the dynamic perspective, the unified healthcare efficiency increases annually by 2.68% during the study period. This increase in eastern China as a technology leader is mainly driven by technological progress, whereas the increase in non-eastern China is mainly driven by a better catch-up effect. In addition, the impact of the reform on the non-eastern region is more significant for the decreasing technology gap, the stronger growth momentum of technological progress, and global innovative provinces.

Production function for modeling hospital activities. The case of Polish county hospitals

The aim of the article is to present the use of production function as a source of knowledge for managers of county hospitals to make rational decisions so as to achieve economic efficiency, including naturally the financial efficiency. The healthcare sector in each country differs from other sectors of the economy. The economically effective operation of county hospitals in Poland is very difficult due to all their determinants. Therefore, all economic analyses should be used to help hospital managers achieve this goal, and production function remains underestimated as a source of knowledge. The Cobb-Douglas and translog production functions were used as sources of knowledge for decision-making by county hospitals. Total number of patient-days was a dependent variable; and the total number of beds, the number of doctors and nurses (in full time equivalents, FTEs) and costs (of materials, electricity, services) were a set of explanatory variables. The significance of explanatory variables most often appeared in models accounting for the workload of nurses. On the other hand, the greatest fit measured with the residual standard error was characterised by models accounting for the number of beds. For each type of production function, the diversified results obtained show the properties of production function. This kind of knowledge is not provided by analyses which are not based on production functions.

Evaluating the comparative efficiency of medical centers in Taiwan: a dynamic data envelopment analysis application

Background

People in Taiwan enjoy comprehensive National Health Insurance coverage. However, under the global budget constraint, hospitals encounter enormous challenges. This study was designed to examine Taiwan medical centers' efficiency and factors that influence it.

Methods

We obtained data from open sources of government routine publications and hospitals disclosed by law to the National Health Insurance Administration, Ministry of Health and Welfare, Taiwan. The dynamic data envelopment analysis (DDEA) model was adopted to estimate all medical centers' efficiencies during 2015–2018. Beta regression models were used to model the efficiency level obtained from the DDEA model. We applied an input-oriented approach under both the constant returns-to-scale (CRS) and variable returns-to-scale (VRS) assumptions to estimate efficiency.

Results

The findings indicated that 68.4% (13 of 19) of medical centers were inefficient according to scale efficiency. The mean efficiency scores of all medical centers during 2015–2018 under the CRS, VRS, and Scale were 0.85, 0.930, and 0.95,respectively. Regression results showed that an increase in the population less than 14 years of age, assets, nurse-patient ratio and bed occupancy rate could increase medical centers' efficiency. The rate of emergency return within 3-day and patient self-pay revenues were associated significantly with reduced hospital efficiency (p < 0.05). The result also showed that the foundation owns medical center has the highest efficiency than other ownership hospitals.

Conclusions

The study results provide information for hospital managers to consider ways they could adjust available resources to achieve high efficiency.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-07869-8.

Key Indicators Affecting Hospital Efficiency: A Systematic Review

Background

Measuring hospital efficiency is a systematic process to optimizing performance and resource allocation. The current review study has investigated the key input, process, and output indicators that are commonly used in measuring the technical efficiency of the hospital to promote the accuracy of the results.

Methods

To conduct this systematic review, the electronic resources and databases MEDLINE (via PubMed), Scopus, Ovid, Proquest, Google Scholar, and reference lists of the selected articles were used for searching articles between 2010 and 2019. After in-depth reviews based on the inclusion and exclusion criteria, among 1,537 studies, 144 articles were selected for the final assessment. Critical Appraisal Skills Programme (CASP) Checklist was used for evaluating the quality of the articles. The main findings of studies have been extracted using content analysis.

Results

After the final analysis, the Context/Input indicators that were commonly considered by studies in analyzing hospital technical efficiency include different variables related to Hospital Capacity, Structure, Characteristics, Market concentration, and Costs. The Process/Throughput indicators include different variables related to Hospital Activity or services-oriented process Indicators, Hospital Quality-oriented process indicators, and Hospital Educational processes. Finally, the Output/Outcome indicators include different variables related to Hospital Activity-related output variables and Quality-related output/outcomes variables.

Conclusion

This study has identified that it is necessary to mix and assess a set of input, process, and output indicators of the hospital with both quantitative and qualitative indicators for measuring the technical efficiency of hospitals comprehensively.

Evaluation of the Efficiency in Public Health Centers in Greece Regarding the Human Resources Occupied: A Bootstrap Data Envelopment Analysis Application

In this paper, the overall efficiency of health centers in Greece is measured by applying the input-oriented model of Data Envelopment Analysis. In addition, four different models were subjected to the input-oriented Data Envelopment Analysis to investigate the contribution of each category of human resources to the efficiency results of the health centers. The bootstrap technique was performed to generate confidence intervals for the models. Data for 155 health centers in Greece were provided by the Ministry of Health. The health centers submitted in the analysis obtained an average efficiency value of 0.932. The average results of the partial models in terms of each input show that the efficiency values achieved by the health centers are mostly influenced by the number of physicians and the number of managers employed. The second factor influencing the efficiency values of the health centers are the number of nursing staff occupied in the health centers. Non-medical staff employed in the health centers had the least contribution to the efficiencies measured. This paper provides important information for the stakeholders and the Government of Greece so as to better allocate the personnel employed in primary health care according to the efficiencies attained by the health centers.

The productive efficiency of community health service stations in China: Taking Shandong province as a case

The present study evaluated the efficiencies of community health service (CHS) stations based on a survey of 1246 CHS stations, covering nine subcategories in 16 cities in Shandong province. Data envelopment analysis (DEA) was applied to investigate the overall efficiency, the technical and the scale efficiency of community health care resources. The results are, the overall efficiency was 9.47%, and the overall efficiency was generally higher in the central-west region than in the east. There were 23.27% of CHS stations showing technically efficient. The technical efficiency was higher in the east (31.11%) relative to the central-west (19.72%), and 72.71% of CHS stations had a technical efficiency higher than the regional average efficiency. The scale efficiency was 9.31% for CHS stations in Shandong province, being the decisive factor for overall efficiency, and 68.96% of CHS stations showed a scale efficiency above the regional average. Stations held by enterprises and universities, and extended by tertiary hospitals had lower efficiencies than other types of CHS stations. In conclusion, the CHS stations had low efficiencies in general, and scale inefficiencies were the main cause. Related suggestions to improve the efficiency are provided accordingly.

Efficiency and Productivity of Public Hospitals in Serbia Using DEA-Malmquist Model and Tobit Regression Model, 2015-2019

Improving productivity within health systems using limited resources is a matter of great concern. The objectives of the paper were to evaluate the productivity, efficiency, and impact of environmental factors on efficiency in Serbian hospitals from 2015-2019. Data envelopment analysis, Malmquist index and Tobit regression were applied to hospital data from this period, and public hospitals in Serbia exhibited a great variation regarding their capacity and performance. Between five and eight hospitals ran efficiently from 2015 to 2019, and the productivity of public hospitals increased whereas technical efficiency decreased in the same period. Tobit regression indicated that the proportion of elderly patients and small hospital size (below 200 beds) had a negative correlation with technical efficiency, while large hospital size (between 400 and 600 beds), the ratio of outpatient episodes to inpatient days, bed turnover rate and the bed occupation rate had a positive correlation with technical efficiency. Serbian public hospitals have considerable space for technical efficiency improvement and public action must be taken to improve resource utilization.

Governmental Investments in Hospital Infrastructure Among Regions and Its Efficiency in China: An Assessment of Building Construction

Hospital infrastructure has been addressed as the prerequisite of healthcare delivery which intensively affects medical quality. Over the past decade, China has proposed a series of investment plans for hospital infrastructure in order to promote healthcare development in underdeveloped regions. Focusing on the construction of hospital buildings as the key component of hospital infrastructure, this study aims to examine whether the investment efficiency is lower where a government prioritizes equity and to explore what kind of geographical predispositions should be embedded in governmental investment plans for hospital infrastructures from the perspectives of both investment equity and efficiency. Relevant data from 330 governmental-invested hospital building construction projects in Sichuan province, China, from 2009 to 2018 were collected. Concentration index was used to evaluate the equity in the distribution of the investments. Tobit model was employed to explore the relationship between regional economic development and investment efficiency measured by an integrated approach of principal component analysis and data envelopment analysis. The results demonstrated a slight concentration of governmental investments in economically developed regions, while a negative association with regional economic development was identified with investment efficiency. Our study illustrated the investment efficiency was higher where a government prioritized equity and provided empirical evidences on switching governmental investment predisposition in the aspect of healthcare infrastructure construction toward less developed regions in China from the perspectives of both investment allocation equity and efficiency, which would further assist in the formulation of region-specific policies and strategies for underdeveloped regions.

Total productivity change of Health Centers in Greece in 2016-2018: a Malmquist index data envelopment analysis application for the primary health system of Greece

BACKGROUND

This paper attempts to evaluate the primary health care system by evaluating health centres in Greece.

METHODS

Malmquist Index Data Envelopment Analysis is applied to study the total productivity of 155 health centres in Greece during 2016-2018. The data were collected from the Ministry of Health and submitted to quality tests to ensure validity and avoid bias.

RESULTS

This paper measures the productivity of each of the 155 health centres in Greece and how it shifted during 2016-2018. In addition, the overall productivity change of the 155 health centres over time is calculated and analysed as being due to technical efficiency or technological efficiency. The analysis of the mean values showed a decrease of 0.9% in the overall productivity factor from 2016 to 2017 and a decrease of 5.2% from 2017 to 2018. The overall decrease in the productivity of the 155 health centres was 3.1%. From 2016 to 2018, 59 health centres changed their productivity mainly due to technological change and 91 changed mainly due to technical efficiency change. One health centre showed regression to its total productivity due to equal regression of its technical efficiency and technology.

CONCLUSIONS

The method used is nonparametric data envelopment analysis along with the Malmquist index to include panel data in the analysis. Meaningful results were extracted by indicating the number of health centres that improved their productivity, regressed in productivity, or remained constant through the period 2016-2018. This paper may contribute to improving health centres' efficiency and productivity. Furthermore, valuable results can be extracted for the National Health Care System to match available resources that correspond to each health centre's needs, as well as for manager planners and stakeholders in primary health care.

Efficiency of Wood-Processing Enterprises—Evaluation Based on DEA and MPI: A Comparison between Slovakia and Bulgaria for the Period 2014–2018

The ongoing transition to a low-carbon, sustainable forest-based economy, and the adoption of circular bioeconomy principles in the wood-processing industry is associated with the optimization of natural resources, application of environmentally sustainable production technologies, adoption of technological and organizational innovations, and increased economic efficiency and competitiveness. The implementation of all these measures can help to reach the biggest challenge of our time in the fight against climate change in a cost-effective and competitive way. The aim of this study was to estimate the technical efficiency of wood-processing companies in the Slovak Republic and the Republic of Bulgaria by applying data envelopment analysis (DEA) and the Malmquist productivity index (MPI), and to reveal some factors for efficiency improvements. The economic efficiency evaluation based on official data was performed using selected indices of four wood-processing companies in each country in the period 2014–2018. The study implemented an output-oriented DEA model with constant returns to scale as a nonparametric linear approach for measuring the efficiency of production decision-making units (DMUs). The results obtained revealed that the studied Slovak companies were more efficient with better management in terms of machinery planning and overhead utilization. Markedly, the Bulgarian companies achieved better materials management and current planning quality. Increased economic efficiency of wood-processing enterprises in both countries can be realized through investments in innovative technological improvements, and enhanced research and development activities.

Health care system efficiency and life expectancy: A 140-country study

Despite the evidence of links between health expenditure and health care efficiency, it is still unclear why countries with similar levels of health expenditures experience different outputs in terms of life expectancy at birth. Health care system efficiency might shed some light on the question. Using output-oriented data envelopment analysis, we compared the health systems of 140 countries in terms of attained life expectancy. Efficiency is determined by the distance from the closest country on the best practice frontier, which identifies the highest attainable life expectancy observed for any given level of health care spending. By using national data form the Human Development Data, we built the efficiency frontier and computed the potential life expectancy increase for each country. The potential improvement was, on average, 5.47 years [95%CI: 4.71-6.27 years]. The least efficient countries (10th percentile of the efficiency score) could improve by 11.78 years, while the most efficient countries (90th percentile of the efficiency score) could only improve by 0.83 years. We then analyzed, with regression analysis stratified by average education level, and by the role of health-related variables in differentiating efficient and inefficient countries from each other. The results suggest that, among countries with lower levels of education, decreasing unemployment and income inequality increases average life expectancy, without increasing health expenditure levels.

Using COVID-19 mortality to select among hospital plant capacity models: An exploratory empirical application to Hubei province

This contribution defines short- and long-run output- and input-oriented plant capacity measures and evaluates them relative to convex and nonconvex technologies. By applying these different plant capacity concepts, the authors seek to measure the use of existing capacities, as well as the evolution and build-up of extra hospital capacity in the Chinese province of Hubei during the outbreak of the COVID-19 epidemic in early 2020. Furthermore, medical literature has established that mortality rates increase with high capacity utilization rates, an insight that this study leverages to select the most plausible of eight plant capacity concepts. The preliminary results indicate that a relatively new, input-oriented plant capacity concept correlates best with mortality.

Using data envelopment analysis and the bootstrap method to evaluate organ transplantation efficiency in Brazil

Brazil has the most extensive public program for organ transplantation in the world, and the Brazilian National Health System (SUS) provides full coverage of all costs involved in organ donation, transplants, and post-transplant. Despite the relevance of the subject and the shortage of organs for transplants, transplantation process efficiency assessments are still uncommon in Brazil and abroad. This study aims to evaluate the efficiency of the Brazilian states and the Federal District in transforming potential organ donors into actual donations. We applied data envelopment analysis (DEA) in conjunction with the bootstrap technique, using organ transplantation data from 2018. The bootstrap methods applied (bootstrap technique, the bootstrap-biased scores of efficiency, and the bootstrap bias-corrected scores of efficiency) allow to obtain a confidence interval for DEA scores and provide greater robustness to studies based on DEA methodology. The bootstrap bias-corrected model indicates that there is significant room for improvement in terms of converting potential donors into actual donors. The mean corrected score is 0.55, signalizing that altogether the Brazilian states could maximize in 45% the number of transplanted organs without necessarily increasing the pool of potential donors. The study provides insights into the Brazilian processes of organ donation and transplantation, helping to identify locations in need of resource allocation improvements. Given the scarcity of studies with a joint application of DEA and bootstrap techniques in this crucial health activity, we also intend to methodologically contribute to this type of benchmark analysis, emphasizing the importance of considering measurement errors, randomness, and bias at DEA models.

Determining the factors affecting the technical efficiency scores of public hospitals using different regression methods

Purpose

This study aims to identify the efficiency scores of hospitals affiliated to the Ministry of Health in Turkey between the years of 2010–2015 at provincial level and to reveal the factors that affect the efficiency scores.

Design/methodology/approach

The two-stage data envelopment analysis (DEA) method was used to achieve the study purpose. In the first stage, DEA method based on input-oriented Charnes–Cooper–Rhodes (CCR) model was performed to calculate the efficiency scores of public hospitals at the provincial level between 2010 and 2015, and in the second stage, Tobit regression and linear regression analyses were used to identify whether the efficiency scores of provinces are affected by the input, output and control variables.

Findings

Upon the analysis, the average efficiency scores of 81 provinces by years were found to vary between 0.79 and 0.89. According to both regression analyses, all of the input and output variables were found to have significant effects on the efficiency scores of provinces while only the population of province among the control variables was identified as the factor with an effect on the efficiency scores of provinces (p < 0.05).

Practical implications

The results of this study are thought to guide health policymakers and managers in terms of both determining efficient and inefficient hospitals at the provincial level and revealing which variables should be taken into account in order to increase efficiency.

Originality/value

The study differs from previous studies on the efficiency of hospitals. First, although previous studies were generally descriptive studies to determine the efficiency level of hospitals, this study is an analytical study that tries also to show the factors affecting the efficiency of hospitals. In addition, while examining the effect of input and output variables on efficiency scores, control variables were also included in the study.

Efficiency, quality, and management practices in health facilities providing outpatient HIV services in Kenya, Nigeria, Rwanda, South Africa and Zambia

Few studies have assessed the efficiency and quality of HIV services in low-resource settings or considered the factors that determine both performance dimensions. To provide insights on the performance of outpatient HIV prevention units, we used benchmarking methods to identify best-practices in terms of technical efficiency and process quality and uncover management practices with the potential to improve efficiency and quality. We used data collected in 338 facilities in Kenya, Nigeria, Rwanda, South Africa, and Zambia. Data envelopment analysis (DEA) was used to estimate technical efficiency. Process quality was estimated using data from medical vignettes. We mapped the relationship between efficiency and quality scores and studied the managerial determinants of best performance in terms of both efficiency and quality. We also explored the relationship between management factors and efficiency and quality independently. We found levels of both technical efficiency and process quality to be low, though there was substantial variation across countries. One third of facilities were mapped in the best-performing group with above-median efficiency and above-median quality. Several management practices were associated with best performance in terms of both efficiency and quality. When considering efficiency and quality independently, the patterns of associations between management practices and the two performance dimensions were not necessarily the same. One management characteristic was associated with best performance in terms of efficiency and quality and also positively associated with efficiency and quality independently: number of supervision visits to HIV units.

For-Profit Hospitals Have Thrived Because of Generous Public Reimbursement Schemes, Not Greater Efficiency: A Multi-Country Case Study

For-profit hospitals' market share has increased in many nations over recent decades. Previous studies suggest that their growth is not attributable to superior performance on access, quality of care, or efficiency. We analyzed other factors that we hypothesized may contribute to the increasing role of for-profit hospitals. We studied the historical development of the for-profit hospital sector across 4 nations with contrasting trends in for-profit hospital market share: the United States, the United Kingdom, Germany, and the Netherlands. We focused on 3 factors that we believed might help explain why the role of for-profits grew in some nations but not in others: (1) the treatment of for-profits by public reimbursement plans, (2) physicians' financial interests, and (3) the effect of the political environment. We conclude that access to subsidies and reimbursement under favorable terms from public health care payors is an important factor in the rise of for-profit hospitals. Arrangements that aligned financial incentives of physicians with the interests of for-profit hospitals were important in stimulating for-profit growth in an earlier era, but they play little role at present. Remarkably, the environment for for-profit ownership seems to have been largely immune to political shifts.

Pandemic response management framework based on efficiency of COVID-19 control and treatment

Aims: The existing response management system for pandemic disease fell short of controlling COVID-19. This study evaluates the response management relative efficiency of 58 countries in two stages, using two models. Materials & methods: Data envelopment analysis was applied for efficiency analysis. Results: 89.6% of countries were inefficient in pandemic control and 79% were inefficient in treatment measures. Sensitivity analysis underlines resources as a critical factor. Further examination points to absence of a robust and uniform mitigation measure against the pandemic in most countries. Conclusions: Preventing spread is not only the first line of defense; it is the only line of defense. The lack of a global public health database support system and uniform response compounded inefficiency. A robust pandemic response management framework is developed based on practices of key performers. Action plans are proposed, with a recommendation for a global public health pandemic database monitoring and support system as the nucleus.

The Effects of Operational and Financial Performance Failure on BI&A-Enabled Search Behaviors: A Theory of Performance-Driven Search

Abstract for Practice and Policy

Is it worth outsourcing essential public health services in China?-Evidence from Beilin District of Xi'an

BACKGROUND

Essential public health services (EPHS) is general welfare public health interventions led by the Chinese government and provided by the primary medical institutions to all residents. In Beilin District, Xi'an, EPHS producers can be divided into outsourced institutions and public institutions.

OBJECTIVE

Can outsourcing EPHS reduce costs and improve efficiency and quality? There is still no definite answer to this question. This paper compares the performance of outsourced institutions and public institutions in terms of efficiency and quality, explains the reasons for this phenomenon.

METHODS

This paper uses a theoretical and two-stage DEA model Based on a "triple subject" research framework.

RESULTS

The results show that the difference between public institutions and outsourced institutions is mainly reflected in service quality. When the quality is not measured, outsourced institutions' production efficiency is higher than that of public institutions. When there are quality measurements, the production efficiency of outsourced institutions is lower than that of public institutions.

CONCLUSIONS

Outsourced institutions perform worse than public institutions. The reason is that a bilateral monopolistic market structure has formed between local governments and outsourced institutions. This situation makes it difficult for the government to replace poor quality outsourced institutions under the constraints of a limited budget.

Multilevel Analysis of the Relationship between Ownership Structure and Technical Efficiency Frontier in the Spanish National Health System Hospitals

Objective: To evaluate the relationship between the ownership structure of hospitals and the possibility of their being positioned on the frontier of technical efficiency in the economic crisis period 2010–2012, adjusting for hospital variables and regional characteristics in the areas where the Spanish National Health System (SNHS) hospitals are located. Methods: 230 National Health System hospitals were studied over the two-year period 2010–2012 according to their ownership structure—public hospitals, private hospitals and public–private partnership (PPP)—data envelopment analysis orientated to inputs was used to measure the overall technical efficiency, pure efficiency and efficiency of scale. A generalised linear mixed model (GLMM) with binomial distribution and logit link function was used to analyse the hospital and regional variables associated with positioning on the frontier. Results: There are substantial differences between the average pure technical efficiency of public, private and PPP hospitals, as well as a greater number of PPP models being positioned on the efficiency frontier (91.67% in 2012). The odds of being positioned on the frontier are 41.7 times higher in PPP models than in public hospitals. The average annual household income per region is related to the greater odds of hospitals being positioned on the frontier of efficiency. Conclusions: During the most acute period of recession in the Spanish economy, PPP formulas favoured hospital efficiency, by increasing the odds of being positioned on the frontier of efficiency when compared to private and public hospitals. The position on the frontier of efficiency of a hospital is related to the wealth of its region.

Determinants of Technical Efficiency in Public Hospitals: The Case of Saudi Arabia

OBJECTIVE

In this study, we investigate the effect of the external environmental and institutional factors on the efficiency and the performance of the public hospitals affiliated to the Ministry of Health (MOH) in the Kingdom of Saudi Arabia (KSA). We estimate the demographic and socioeconomic characteristics of catchment populations that explain the demand for health services.

METHODS

We apply descriptive analysis to explore what external factors (demographic and socioeconomic factors) can explain the observed differences in technical efficiency scores. We use Spearman's rank correlation, multivariate Tobit regression and Two-part model to measure the impact of the explanatory variables (i.e. population density, nationality, gender, age groups, economic status, health status, medical interventions and geographic location) on the efficiency scores.

RESULTS

The analysis shows that the external factors had a significant influence on efficiency scores. We find significant associations between hospitals efficiency scores and number of populations in the catchment area, percentage of children (0-5 years old), the prevalence of infectious diseases, and the number of prescriptions dispensed from hospital's departments. Also, the scores significantly associate with the number of populations who faced financial hardships during medical treatments, and those received financial support from social administration. That indicates the hospitals that serve more patients in previous characteristics are relatively more technically efficient.

CONCLUSIONS

The environmental and institutional factors have a crucial effect on efficiency and performance in public hospitals. In these regards, we suggested improvement of health policies and planning in respect to hospital efficiency and resource allocation, which consider the different demographic, socioeconomic and health status of the catchment populations (e.g., population density, poverty, health indicators and services utilization). The MOH should pay more attention to ensure appropriate allocation mechanisms of health resources and improve utilization of health services among the target populations, for securing efficient and equitable health services.

How efficient are surgical treatments in Japan? The case of a high-volume Japanese hospital

Japan's healthcare expenditures, which are largely publicly funded, have been growing dramatically due to the rapid aging of the population as well as the innovation and diffusion of new medical technologies. Annual costs for surgical treatments are estimated to be approximately USD 20 billion. Using unique longitudinal clinical data at the individual surgeon level, this study aims to estimate the technical efficiency of surgical treatments across surgical specialties in a high-volume Japanese teaching hospital by employing stochastic frontier analysis (SFA) with production frontier models. We simultaneously examine the impacts of potential determinants that are likely to affect inefficiency in operating rooms. Our empirical results show a relatively high average technical efficiency of surgical production, with modest disparity across surgical specialties. We also demonstrate that an increase in the number of operations performed by a surgeon significantly reduces operating room inefficiency, whereas the revision of the fee-for-service schedule for surgical treatments does not have a significant impact on inefficiency. In addition, we find higher technical efficiency among surgeons who perform multiple daily surgeries than those who perform a single operation in a day. We suggest that it is important for hospital management to retain efficient surgeons and physicians and provide efficient healthcare services given the competitive Japanese healthcare market.

Efficiency evaluation of public hospitals in Saudi Arabia: an application of data envelopment analysis

OBJECTIVE

In this study, we assess the performance of public hospitals in Saudi Arabia. We detect the sources of inefficiency and estimate the optimal levels of the resources that provide the current level of health services. We enrich our analysis by employing locations and capacities of the hospitals.

DESIGN

We employ data envelopment analysis (DEA) to measure the technical efficiency of 91 public hospitals. We apply the input-oriented Charnes, Cooper and Rhodes, and Banker, Charne, Cooper models under Constant and Variable Returns-to-Scale. The assessment includes four inputs, and six output variables taken from the Ministry of Health databases for 2017. We conducted the assessment via PIM-DEA V.3.2 software.

SETTING

Ministry of health-affiliated hospitals in the Kingdom of Saudi Arabia.

RESULTS

Findings identified 75.8% (69 of 91) of public hospitals as technically inefficient. The average efficiency score was 0.76, indicating that hospitals could have reduced their inputs by 24% without reduction in health service provision. Small hospitals (efficiency score 0.79) were more efficient than medium-sized and large hospitals. Hospitals in the central region were more efficient (efficiency score 0.83), than those located in other geographical locations. More than half of the hospitals (62.6%) were operating suboptimally in terms of the scale efficiency, implying that to improve efficiency, they need to alter their production capacity. Performance analysis identified overuse of physician's numbers and shortage of health services production, as major causes of inefficiency.

CONCLUSION

Most hospitals were technically inefficient and operating at suboptimal scale size and indicate that many hospitals may improve their performance through efficient utilisation of health resources to provide the current level of health services. Changes in the production capacity are required, to facilitate optimal use of medical capacity. The inefficient hospitals could benefit from these findings to benchmarking their system and performance in light of the efficient hospital within their capacity and geographical location.

The efficiency of routine infant immunization services in six countries: a comparison of methods

BACKGROUND

Few studies have systematically examined the efficiency of routine infant immunization services. Using a representative sample of infant immunization sites in Benin, Ghana, Honduras, Moldova, Uganda and Zambia (316 total), we estimated average efficiency levels and variation in efficiency within each country, and investigated the properties of published efficiency estimation techniques.

METHODS

Using a dataset describing 316 immunization sites we estimated site-level efficiency using Data Envelopment Analysis (DEA), Stochastic Frontier Analysis (SFA), and a published ensemble method combining these two approaches. For these three methods we operationalized efficiency using the Sheppard input efficiency measure, which is bounded in (0, 1), with higher values indicating greater efficiency. We also compared these methods to a simple regression approach, which used residuals from a conventional production function as a simplified efficiency index. Inputs were site-level service delivery costs (excluding vaccines) and outputs were total clients receiving DTP3. We analyzed each country separately, and conducted sensitivity analysis for different input/output combinations.

RESULTS

Using DEA, average input efficiency ranged from 0.40 in Ghana and Moldova to 0.58 in Benin. Using SFA, average input efficiency ranged from 0.43 in Ghana to 0.69 in Moldova. Within each country scores varied widely, with standard deviation of 0.18-0.23 for DEA and 0.10-0.20 for SFA. Input efficiency estimates generated using SFA were systematically higher than for DEA, and the rank correlation between scores ranged between 0.56-0.79. Average input efficiency from the ensemble estimator ranged between 0.41-0.61 across countries, and was highly correlated with the simplified efficiency index (rank correlation 0.81-0.92) as well as the DEA and SFA estimates.

CONCLUSIONS

Results imply costs could be 30-60% lower for fully efficient sites. Such efficiency gains are unlikely to be achievable in practice - some of the apparent inefficiency may reflect measurement errors, or unmodifiable differences in the operating environment. However, adapted to work with routine reporting data and simplified methods, efficiency analysis could triage low performing sites for greater management attention, or identify more efficient sites as models for other facilities.

Measuring and optimising the efficiency of community hospital inpatient care for older people: the MoCHA mixed-methods study

Background

Community hospitals are small hospitals providing local inpatient and outpatient services. National surveys report that inpatient rehabilitation for older people is a core function but there are large differences in key performance measures. We have investigated these variations in community hospital ward performance.

Objectives

(1) To measure the relative performance of community hospital wards (studies 1 and 2); (2) to identify characteristics of community hospital wards that optimise performance (studies 1 and 3); (3) to develop a web-based interactive toolkit that supports operational changes to optimise ward performance (study 4); (4) to investigate the impact of community hospital wards on secondary care use (study 5); and (5) to investigate associations between short-term community (intermediate care) services and secondary care utilisation (study 5).

Methods

Study 1 – we used national data to conduct econometric estimations using stochastic frontier analysis in which a cost function was modelled using significant predictors of community hospital ward costs. Study 2 – a national postal survey was developed to collect data from a larger sample of community hospitals. Study 3 – three ethnographic case studies were performed to provide insight into less tangible aspects of community hospital ward care. Study 4 – a web-based interactive toolkit was developed by integrating the econometrics (study 1) and case study (study 3) findings. Study 5 – regression analyses were conducted using data from the Atlas of Variation Map 61 (rate of emergency admissions to hospital for people aged ≥ 75 years with a length of stay of < 24 hours) and the National Audit of Intermediate Care.

Results

Community hospital ward efficiency is comparable with the NHS acute hospital sector (mean cost efficiency 0.83, range 0.72–0.92). The rank order of community hospital ward efficiencies was distinguished to facilitate learning across the sector. On average, if all community hospital wards were operating in line with the highest cost efficiency, savings of 17% (or £47M per year) could be achieved (price year 2013/14) for our sample of 101 wards. Significant economies of scale were found: a 1% rise in output was associated with an average 0.85% increase in costs. We were unable to obtain a larger community hospital sample because of the low response rate to our national survey. The case studies identified how rehabilitation was delivered through collaborative, interdisciplinary working; interprofessional communication; and meaningful patient and family engagement. We also developed insight into patients’ recovery trajectories and care transitions. The web-based interactive toolkit was established [http://mocha.nhsbenchmarking.nhs.uk/ (accessed 9 September 2019)]. The crisis response team type of intermediate care, but not community hospitals, had a statistically significant negative association with emergency admissions.

Limitations

The econometric analyses were based on cross-sectional data and were also limited by missing data. The low response rate to our national survey means that we cannot extrapolate reliably from our community hospital sample.

Conclusions

The results suggest that significant community hospital ward savings may be realised by improving modifiable performance factors that might be augmented further by economies of scale.

Future work

How less efficient hospitals might reduce costs and sustain quality requires further research.

Funding

This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 8, No. 1. See the NIHR Journals Library website for further project information.

Technical Efficiency of Public and Private Hospitals in Beijing, China: A Comparative Study

Objective: With the participation of private hospitals in the health system, improving hospital efficiency becomes more important. This study aimed to evaluate the technical efficiency of public and private hospitals in Beijing, China, and analyze the influencing factors of hospitals’ technical efficiency, and thus provide policy implications to improve the efficiency of public and private hospitals. Method: This study used a data set of 154–232 hospitals from “Beijing’s Health and Family Planning Statistical Yearbooks” in 2012–2017. The data envelopment analysis (DEA) model was employed to measure technical efficiency. The propensity score matching (PSM) method was used for matching “post-randomization” to directly compare the efficiency of public and private hospitals, and the Tobit regression was conducted to analyze the influencing factors of technical efficiency in public and private hospitals. Results: The technical efficiency, pure technical efficiency and scale efficiency of public hospitals were higher than those of private hospitals during 2012–2017. After matching propensity scores, although the scale efficiency of public hospitals remained higher than that of their private counterparts, the pure technical efficiency of public hospitals was lower than that of private hospitals. Panel Tobit regression indicated that many hospital characteristics such as service type, level, and governance body affected public hospitals’ efficiency, while only the geographical location had an impact on private hospitals’ efficiency. For public hospitals in Beijing, those with lower average outpatient and inpatient costs per capita had better performance in technical efficiency, and bed occupancy rate, annual visits per doctor, and the ratio of doctors to nurses also showed a positive sign with technical efficiency. For private hospitals, the average length of stay was negatively associated with technical efficiency, but the bed occupancy rate, annual visits per doctor, and average outpatient cost were positively associated with technical efficiency. Conclusions: To improve technical efficiency, public hospitals should focus on improving the management standards, including the rational structure of doctors and nurses as well as appropriate reduction of hospitalization expenses. Private hospitals should expand their scale with proper restructuring, mergers, and acquisitions, and pay special attention to shortening the average length of stay and increasing the bed occupancy rate.

Assessing the effects of exogenous factors for benchmarking hospitals with double bootstrapping

Purpose

Data envelopment analysis (DEA) is based on the production possibility set that involves the process of converting resources or inputs to outputs. Accordingly, most DEA models include endogenous variables and need an additional step to find the influence of exogenous variables on the process. The purpose of this paper is to examine the relationship between the efficiency scores of DEA and the exogenous variables using truncated regression analysis with double bootstrapping along with two additional methods.

Design/methodology/approach

First, the authors employ DEA for benchmarking the comparative efficiency of the health care institutes. Next, the authors run and compare truncated, ordinary least square (OLS) and Tobit regression analysis using the double bootstrapping algorithm for finding the influence of exogenous variables on the efficiency of the health care institutes.

Findings

The authors confirmed the amount of bias for the Tobit and OLS regression models, which was caused by serially correlated errors. Accordingly, the authors chose results from the truncated regression model with double bootstrapping for examining the influence of exogenous or environment variables on the efficiency scores.

Research limitations/implications

The study includes cross-sectional data on health care institutes in the state of Washington, USA. Collecting data in various states or regions over time is left for future studies.

Practical implications

In this study, three exogenous variables such as Medicaid revenues, locations of health care institutes and ownership types are significant for explaining the relationship between the efficiency scores and a group of the exogenous variables. Managers and policy makers need to pay attention to these variables along with endogenous variables for promoting the sustainability of the health care institutes.

Originality/value

The study demonstrates the usefulness of the truncated regression analysis with double bootstrapping for confirming the relationship between the efficiency scores of DEA and a group of exogenous variables, which is rare in the DEA literature.