Cross-efficiency evaluation in the presence of flexible measures with an application to healthcare systems

Abolghasem, Sepideh; Toloo, Mehdi; Amézquita, Santiago

doi:10.1007/s10729-019-09478-0

Cited by 38 publications

(19 citation statements)

References 66 publications

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“…DEA starts from a series of variables, input and output, and calculates the efficiency of each DMU, elaborating a ranking of relative efficiency with attribution of weights. The efficiency frontier is made up of efficient DMUs, located on it, compared to the inefficient ones (the distance from the DMU to the border represents efficiency) [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

COVID-19 and the efficiency of health systems in Europe

Lupu

Ţigănaşu

2022

Health Econ Rev

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Background This study aims at analyzing the efficiency of the health systems of 31 European countries in treating COVID-19, for the period January 1, 2020 – January 1, 2021, by incorporating some factors from a multidimensional perspective. Methods The methodology used in the research was Data Envelopment Analysis (DEA), through which efficiency scores for health systems have been calculated. The research was performed considering three stages: the first wave (January 1–June 15), the relaxation period (June 15–October 1) and the second wave (October 1–December 31). In evaluating the determinants of the efficiency of health systems, six major fields of influence were taken into account: health care, health status, population, economic, cultural/societal and governmental issues, all covering 15 indicators. After measuring the efficiency, we used the Tobit type regression to establish the influencing elements on it. Results The results for the public health systems of European states were determined for each country and period. We evaluated the efficiency of health systems in Europe against COVID-19, starting from health inputs (COVID-19 cases, physicians, nurses, hospital beds, health expenditure) and output (COVID-19 deaths). The obtained outputs show that, especially in the first phase of the pandemic, the inefficiency of the health systems was quite high, mainly in Western countries (Italy, Belgium, Spain, UK). In the relaxation phase and in the second wave, the Western states, severely affected at the beginning of the pandemic, began to take adequate measures and improve the efficiency of their sanitary systems. Instead, Eastern European countries were hit hard by the inefficiency of health systems (Bulgaria, Greece, Hungary, Romania). After Tobit regression, results of the study show that the influencing elements are different for the three stages: concerning the first wave, comobirdities, population age, and population density are important; for relaxation period a great influence have government effectiveness and power distance; with respect to second wave, the relevant factors are education and population density. Conclusions The results obtained could serve as starting points for health policymakers to perform comparative analyzes in terms of good practices in the health system and to develop national plans to better deal with health crises. At the same time, they can be used internationally to achieve a coherent and effective community response to the pandemic.

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Section: Methodsmentioning

confidence: 99%

COVID-19 and the efficiency of health systems in Europe

Lupu

Ţigănaşu

2022

Health Econ Rev

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“…In our opinion, the lack of a systematic analysis approach is a significant contributory factor to the observed shortfall. Here, we note that some frameworks have been proposed (e.g., Abolghasem et al, 2019;Andreji c et al, 2016;Grosskopf et al, 2004), but they are found to be restricted to qualitative analysis.…”

Section: Literature Reviewmentioning

confidence: 96%

Hybrid model for a cross‐department efficiency evaluation in healthcare systems

et al. 2021

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This work investigates a new hybrid data envelopment analysis (DEA) and grey relational analysis (GRA) for a macro‐level evaluation of healthcare organizations. Once similar processes and decision‐making units (DMUs) are identified, the DEA is used to extract the efficient DMUs and introduce potential improvements. Then, the identified DMUs and inputs are scanned by the GRA to highlight their preference order and thus retrieve the benchmarked DMUs and the most influencing variables to prioritize. An empirical analysis evolving 63 DMUs across four departments of a Tunisian teaching hospital was conducted to provide useful guidance to policymakers to better allocate available resources.

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“…[6] introduced this method first and used the term Decision Making Unit (DMU) to refer to the units where efficiency values are calculated. DEA is specifically empowered by its ability to efficiently examine the efficiency of healthcare providers [22]. Sherman [23] was the first to apply the DEA methodology in order to measure the efficiency of seven US hospitals.…”

Section: Data Envelopment Analysis (Dea)mentioning

confidence: 99%

Benchmarking healthcare systems of OECD countries: A DEA – based Malmquist Productivity Index Approach

AYDIN

2022

Alphanumeric Journal

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Along with technological innovations and developments experienced in the second half of the twentieth century, very important changes have occurred in healthcare. Many different, complex and economically expensive services are being tried to be carried out together. For this reason, it is finally crucial that the health services delivered by providers to scarce resources are delivered effectively and efficiently to people without sacrificing quality. Today, the most important problem of the production of healthcare services is the resource shortage as it is in other sectors. Efficiency, quality and competition are important criteria in the production and delivery of health services. Reducing costs in the production of health services is one of the main health policies for many world countries. These policies have made it necessary for international competitiveness, product and service sectors to continually improve their performance. In this study, the efficiency evaluation of the health care services of OECD were examined using Data Envelopment Analysis and Malmquist Total Factor Productivity. Considering the input values of the countries, the results of the input-oriented model and Malmquist productivity index were analyzed with the fixed return assumption according to the scale.

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Cross-efficiency evaluation in the presence of flexible measures with an application to healthcare systems

Cited by 38 publications

References 66 publications

COVID-19 and the efficiency of health systems in Europe

COVID-19 and the efficiency of health systems in Europe

Hybrid model for a cross‐department efficiency evaluation in healthcare systems

Benchmarking healthcare systems of OECD countries: A DEA – based Malmquist Productivity Index Approach

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