Measuring the operational and financial efficiency using a Malmquist data envelopment analysis: a case of Egyptian hospitals

Evaluating China's primary healthcare services' efficiency and spatial correlation: a three-stage DEA-Malmquist model

Introduction

Enhancing the efficiency of primary healthcare services is essential for a populous and developing nation like China. This study offers a systematic analysis of the efficiency and spatial distribution of primary healthcare services in China. It elucidates the fundamental landscape and regional variances in efficiency, thereby furnishing a scientific foundation for enhancing service efficiency and fostering coordinated regional development.

Methods

Employs a three-stage DEA-Malmquist model to assess the efficiency of primary healthcare services across 31 provincial units in mainland China from 2012 to 2020. Additionally, it examines the spatial correlation of efficiency distribution using the Moran Index.

Results

The efficiency of primary healthcare services in China is generally suboptimal with a noticeable declining trend, highlighting significant potential for improvement in both pure technical efficiency and scale efficiency. There is a pronounced efficiency gap among provinces, yet a positive spatial correlation is evident. Regionally, efficiency ranks in the order of East > Central > West. Factors such as GDP per capita and population density positively influence efficiency enhancements, while urbanization levels and government health expenditures appear to have a detrimental impact.

Discussion

The application of the three-stage DEA-Malmquist model and the Moran Index not only expands the methodological framework for researching primary healthcare service efficiency but also provides scientifically valuable insights for enhancing the efficiency of primary healthcare services in China and other developing nations.

Survey and analysis on the resource situation of primary health care institutions in rural China

Background

China's rural population is immense, and to ensure the well-being of rural residents through healthcare services, it is essential to analyze the resources of rural grassroots healthcare institutions in China. The objective is to examine the discrepancies and deficiencies in resources between rural grassroots healthcare institutions and the national average, providing a basis for future improvements and supplementation of rural healthcare resources.

Methodology

The study analyzed data from 2020 to 2022 on the number of healthcare establishments, the capacity of hospital beds, the number of healthcare professionals, and the number of physicians in both rural and national settings. Additionally, it examined the medical service conditions and ratios of township health centers in rural areas to assess the resource gap between rural areas and the national average.

Results

Healthcare establishments: On average, there were 2.2 fewer healthcare institutions per 10,000 persons in rural areas compared to the national average over three years. Hospital beds: On average, there were approximately 36 fewer hospital beds per 10,000 persons in rural areas compared to the national average over three years. Healthcare professionals and physicians: On average, there were about 48 fewer healthcare technical personnel and 10 fewer practicing (including assistant) physicians per 10,000 persons in rural areas compared to the national average over three years.

Conclusion

Compared to the national average, there are significant discrepancies and deficiencies in grassroots healthcare resources in rural China. This underscores the necessity of increasing funding to progressively enhance the number of healthcare institutions in rural areas, expand the number of healthcare personnel, and elevate medical standards to better align with national benchmarks. Improving rural healthcare resources will strategically equip these institutions to cater to rural communities and effectively handle public health emergencies. Ensuring that the rural population in China has equal access to healthcare services as the rest of the country is crucial for promoting the well-being of rural residents and achieving health equity.

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.

Do business strategies and environmental, social, and governance (ESG) performance mitigate the likelihood of financial distress? A multiple mediation model

This study explores the connection between business strategies, ESG performance, and the probability of bankruptcy. Using a sample comprising 1970 U.S. firm-year observations from 2016 to 2020, this study adopts several techniques to achieve its goals, including the partial least squares structural equation modeling (PLS-SEM) algorithm and additional analyses. The results demonstrate that a firm with a better cost leadership strategy has higher ESG performance. A sound cost leadership strategy and ESG performance negatively influence a firm's likelihood of financial distress. Using a mediating analysis model, we also find that financial and ESG performance mediate and mitigate the probability of experiencing financial distress through a cost leadership strategy, indicating that these are essential factors that cannot be ignored when mitigating bankruptcy probability. Financial performance also mediates and mitigates the probability of experiencing financial distress through the ESG path. This study adds to the existing body of knowledge by revealing the role of sound business strategies and ESG performance in mitigating the likelihood of financial distress, an under-explored topic. It also analyzes the mediation roles of financial and ESG performance to provide significant insights to companies' decision-makers in order to support them in their endeavors toward performance improvement and achieving best practices.

Does the Efficiency of a Firm’s Intellectual Capital and Working Capital Management Affect Its Performance?

This study explores the efficiency of intellectual capital (ICE) and working capital management (WCME) in the GCC industrial sector and its potential impact on firm performance. The data were gathered from Standard & Poor’s database from 2015 to 2019. This study uses data envelopment analysis (DEA), regression analysis, and robustness tests to accomplish its aims. The results indicate that most firms do not employ their intellectual and working capital investments well and need improvement actions to achieve the best practices. The regression model results reveal that ICE and WCME significantly and positively influence firms’ performance. The results of this study support the resource-based, trade-off, and pecking order theories. The study findings have important implications for many stakeholders; for example, they would be helpful for firm decision-makers in managing their investments in intellectual and working capital to achieve the best practices and improve a firm's performance. In addition, the findings would be helpful for financiers, because high-performance firms are likely to have more reasonable valuations that facilitate debt financing. Moreover, the findings have noteworthy implications for trading procedures as investors aspire to attractive economic returns for their investments in corporations that pasture ICE and WCME issues. Additionally, these findings have important implications for employee job satisfaction and retention by improving IC management.

The Influence of Environmental, Social, and Governance (ESG) Practices on US Firms’ Performance: Evidence from the Coronavirus Crisis

This study explores the influence of total and individual ESG practices and the coronavirus crisis on US firm performance (FP). A large and recent sample of 406 US firms that adopted ESG issues during 2016–2020 was used. This study uses the generalized least-squares (GLS) regression estimator, the dynamic analysis technique, and robustness tests. The results indicate that firms with heightened ESG practices have better performance measures. In most cases, the results suggest that firms with heightened environmental, social, and governance performances have better performance measures. The results suggest that the coronavirus crisis negatively affected FP measures. In addition, the analyses of the differences suggest significant distinctions in FP due to the coronavirus crisis. This study’s findings have important implications for stakeholders. Managers could benefit from the results of this examination by recognizing the status of ESG practices and FP before and during the coronavirus crisis and identifying the linkage between the fulfillment of ESG responsibilities and FP. This study provides noteworthy practical implications that could enable managers to develop strategies and policies for adopting and enhancing ESG practices to achieve the best performance. Furthermore, the results could influence trading processes as investors and financiers pursue attractive financial returns from investments in businesses concerned with ESG issues.

Analyzing the efficiency of Chinese primary healthcare institutions using the Malmquist-DEA approach: Evidence from urban and rural areas

Background

China has been increasing the investment in Primary Health Care Institutions (PHCIs) since the launch of the New Health Care System Reform in 2009. It is a crucial concern whether the PHCIs can meet residents' need both in urban and rural with the limited government finance, especially encountering the challenge of the COVID-19. This study aimed to reveal the trend of the primary health service efficiency in the past decade, compare the urban-rural differences, and explore relevant factors.

Methods

DEA and Malmquist models were applied to calculate the health service efficiency of PHCIs among 28 provinces in China, with the input variables including the number of institutions, number of beds, number of health technicians, and the outputs variables including the number of outpatients and emergency visits, number of discharged patients. And the Tobit model was used to analyze the factors on the efficiency in urban and rural. A sensitivity analysis for model validations was also carried out.

Results

The average technical efficiency (TE) of urban PHCIs fluctuated from 63.3% to 67.1%, which was lower than that in rural (75.8-82.2%) from 2009 to 2019. In terms of dynamic efficiency, the urban PHCIs performed better than the rural, and the trends in the total factor productivity change were associated with favorable technology advancement. The population density and dependency ratio were the key factors on TE in both of the urban and rural PHCIs, and these two factors were positively correlated to TE. In terms of TE, it was negatively correlated with the proportion of total health expenditure as a percentage of GDP in urban PHCIs, while in rural it was positively correlated with the urbanization rate and negatively correlated with GDP per capita. Besides, the tests of Mann-Whitney U, and Kruskal-Wallis H indicated the internal validity and robustness of the chosen DEA and Malmquist models.

Conclusions

It needs to reduce the health resource wastes and increase service provision in urban PHCIs. Meanwhile, it is necessary to strengthen medical technology and gaining greater efficiency in rural PHCIs by technology renovation.

Evaluating the decentralization of public hospitals in Turkey in terms of technical efficiency: data envelopment analysis and Malmquist index

Purpose

Decree Law No. 663 introduced a decentralized organizational structure and administration pertaining to Turkish public hospitals in November 2011. This study aims to explore the effects of the public hospital unions (PHUs), which were a result of Decree Law No. 663, on the efficiency and productivity of public hospitals.

Design/methodology/approach

Data envelopment analysis (DEA) and DEA-based Malmquist total factor productivity (TFP) index were used from 2011 to 2016. Raw materials and supply expenses, salaries and fringe benefits, other service costs, general administrative expenses, total number of beds, number of specialists, number of residents, number of general practitioners, number of nurses and midwives and other medical officials were used as input variables. Working capital turnover, number of inpatients, number of outpatients and number of surgical operations for Groups A, B and C were used as output variables.

Findings

According to the DEA scores, the percentage of efficient hospitals showed a declining trend from 2011 to 2016. The TFP results also showed a decreasing trend from 2011 to 2016.

Practical implications

Providing administrative and financial autonomy to public hospital managers may cause efficiency and productivity losses, which is contrary to expectations.

Originality/value

This study is the first to reveal the impact of decentralization of public healthcare providers on their performance levels in Turkey.

Evaluating the Super-Efficiency of Working Capital Management Using Data Envelopment Analysis: Does COVID-19 Matter?

This study evaluates the working capital management (WCM) super-efficiency in UAE firms and determines how the COVID-19 crisis may affect WCM super-efficiency from a statistical perspective. Data envelopment analysis (DEA), difference tests, and Tobit regression techniques are used. Integration of DEA with Tobit regression is required to recognize the potential statistical consequence of COVID-19 on the super-efficiency of WCM. The results show that WCM super-efficiency is moderate and requires decision-makers' corrective measures to attain best practices. The study results show no significant difference in WCM super-efficiency, and COVID-19 does not significantly influence WCM super-efficiency. The results would rouse decision-makers' interests, who would look into the firm's efficiency and weak spots. They would then take major steps to improve efficiency and achieve the best practices. Stakeholders such as managers, financiers, and investors may need these results to monitor how firms manage their assets and liabilities. We contribute to the literature by investigating and evaluating firms' WCM super-efficiency pre and post the COVID-19 pandemic employing a unique technique based on DEA super-efficiency and examining whether COVID-19 has affected WCM super-efficiency to extend the scope of knowledge and stressing the importance of sound WCM, especially in periods of economic distress.

Analyzing the Efficiency of Working Capital Management: a New Approach Based on DEA-Malmquist Technology

In this study, we analyze the efficiency of working capital management (WCME) for Gulf companies before and during the coronavirus crisis, then explore the influence of the coronavirus crisis on WCME. This study uses several techniques to achieve its goals, including the Malmquist index (MI), data envelopment analysis (DEA), and Tobit regression. The results demonstrate that most firms (approximately 84%) adopt a conservative strategy for their WCM. The WCME results revealed a statistical difference in the technological and pure efficiency scores for companies before and during the coronavirus crisis, while the results revealed no statistical difference in the technical, scale, and total factor productivity scores. Tobit’s results show that the coronavirus crisis had no significant influence on companies’ WCM performance. Finally, our results indicate that firms that are efficient in terms of WCM have higher sales returns and net income. The findings of this study have important implications for stakeholders to increase their awareness of companies’ WCM performance before and during a crisis. In addition, the results could have implications for trading strategies as investors and financiers seek to invest in companies with good WCM. The implications of WCM performance on social interests would cause decision-makers to use the best strategies and procedures to enhance WCM activities to improve their investments and image in the community in which it operates. We advance a novel contribution to the literature by analyzing and appraising the WCME for companies before and during the coronavirus crisis using a new approach based on DEA-Malmquist technology and then examining whether the coronavirus crisis has affected the WCME.

Configurational Paths to Higher Efficiency in County Hospital: Evidence From Qualitative Comparative Analysis

Background

The efficient operation of county-level medical institutions is a significant guarantee in constructing Chinese rural tertiary care service networks. However, it is still unclear how to increase the efficiency of county hospitals under the interaction of multiple factors. In this study, 35 county general hospitals in China were selected to explore the configuration paths of county hospitals' high and poor efficiency status under the Environment-Structure-Behavior (ESB) framework and provide evidence-based recommendations for measures to enhance its efficiency.

Methods

Data envelopment analysis with the bootstrapping procedure was used to estimate the technical efficiency value of case hospitals. A fuzzy-set qualitative comparative analysis approach was carried out to explore the configuration of conditions to the efficiency status.

Results

Antecedent configurations affecting the efficiency status of county hospitals were identified based on the ESB analytical framework. Three high-efficiency configuration paths can be summarized as structural optimization, capacity enhancement, and government support. Another three types of paths, namely insufficient capacity, aggressive expansion, and poor decision-making, will lead to inefficient configurations.

Conclusion

Qualitative comparative analysis is necessary when exploring complex causality. The efficiency situation of county hospitals results from a combination of influencing factors instead of the effect of a single one. There is no solitary configuration for high efficiency that applies to all healthcare units. Any measures aimed at efficiency promotion should be discussed within the framework of a case-specific analysis.

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.

Measuring operational efficiency of isolation hospitals during COVID-19 pandemic using data envelopment analysis: a case of Egypt

Purpose

This study used Data Envelopment Analysis (DEA) to measure and evaluate the operational efficiency of 26 isolation hospitals in Egypt during the COVID-19 pandemic, as well as identifying the most important inputs affecting their efficiency.

Design/methodology/approach

To measure the operational efficiency of isolation hospitals, this paper combined three interrelated methodologies including DEA, sensitivity analysis and Tobit regression, as well as three inputs (number of physicians, number of nurses and number of beds) and three outputs (number of infections, number of recoveries and number of deaths). Available data were analyzed through R v.4.0.1 software to achieve the study purpose.

Findings

Based on DEA analysis, out of 26 isolation hospitals, only 4 were found efficient according to CCR model and 12 out of 26 hospitals achieved efficiency under the BCC model, Tobit regression results confirmed that the number of nurses and the number of beds are common factors impacted the operational efficiency of isolation hospitals, while the number of physicians had no significant effect on efficiency.

Research limitations/implications

The limits of this study related to measuring the operational efficiency of isolation hospitals in Egypt considering the available data for the period from February to August 2020. DEA analysis can also be an important benchmarking tool for measuring the operational efficiency of isolation hospitals, for identifying their ability to utilize and allocate their resources in an optimal manner (Demand vs Capacity Dilemma), which in turn, encountering this pandemic and protect citizens' health.

Originality/value

Despite the intensity of studies that dealt with measuring hospital efficiency, this study to the best of our knowledge is one of the first attempts to measure the efficiency of hospitals in Egypt in times of health' crisis, especially, during the COVID-19 pandemic, to identify the best allocation of resources to achieve the highest level of efficiency during this pandemic.