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

Abstract

PurposeThe efficient use of organizational resources is integral to the existence of prime firms. This study, using Malmquist data envelopment analysis (DEA), aims to assess in the level of operational and financial efficiency and its determinants for ensuring and sustaining excellent performance in 33 Egyptian private hospitals.Design/methodology/approachThis study adopted a Malmquist DEA approach to assess the changes in operational and financial efficiency in Egyptian hospitals. Tobit regression was also used to identify the significant variables affecting their efficiency. In addition, a sensitivity analysis is carried out for model validations.FindingsOut of 33 hospitals, 17 were found inefficient due to the decline in their technical efficiency. Moreover, the total value of the software programs and operational expenses and the total number of employees are common factors affecting both operational and financial efficiency. In addition, the number of physicians significantly affects the hospital's financial efficiency.Practical implicationsThe study sheds light on the value of using DEA to assess efficiency. DEA in the context of emerging economy such as Egypt's can be a useful tool for decision-makers and practitioners in identifying and addressing performance weaknesses and thus supports continuous improvement in performance.Originality/valueSeveral studies have adopted the DEA approach to assess the overall efficiency of hospitals in Europe and the United States. However, in the MENA region, these studies are uncommon. This study is thought to be one of the earliest attempts to assess hospitals' efficiency in Egypt.

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

“…DEA has been extensively used by researchers and practitioners to evaluate hospital performance and in measuring the efficiency of various types of healthcare organizations including public hospitals (Alsabah et al, 2020;Ngobeni et al, 2020;Najadat et al, 2020;Kocisova et al, 2018;Almiman, 2018;Mitropoulos et al, 2015;Mogha et al, 2015;Najadat and Alsmadi, 2011;Akazili et al, 2008), university and research hospitals (Akkan et al, 2020;Ilgun and Knoca, 2019;Bahrami et al, 2018;Chowdhury and Zelenyuk, 2016) and private hospitals (Habib and Shahwan, 2020;Gandhi and Sharma, 2018;Mogha et al, 2012). Ngobeni et al (2020) measured the technical efficiency of public hospitals in nine provinces in South Africa based on CRS and VRS-DEA during the period 2017-2018.…”

“…The results showed that the better equipped and larger departments support the less equipped one, and the interoperability between them must be considered to measure their level of efficiency better. In the same context Habib and Shahwan (2020) used DEA to analyze the operational and financial efficiency of 33 Egyptian private hospitals to identify the most important variables affecting the efficiency of these hospitals. The results confirmed that 17 out of 33 hospitals were found to be inefficient, due to a decrease in their technical efficiency during the analysis period 2014-2016, and that the most important factors affecting both the operational and financial efficiency of these hospitals are the value of health software, operational expenses and the total number of employees.…”

“…DEA has been widely employed for evaluating the relative efficiency of hospitals (Top et al, 2020). Unlike other methods, such as financial ratios, stochastic frontier analysis (SFA) and regression analysis, DEA has been adopted for several reasons: it is one of the multicriteria decision-making methods that DMUs use in determining the relationship between inputs and outputs, it does not require prior assumptions regarding the chosen variables, it does not require any prior information about the weights that must be given to the selected inputs and outputs, it is considered a powerful and useful method in benchmarking, it assists managers and policymakers in the process of continuous improvement (Habib and Shahwan, 2020;Asanduluia et al, 2014) and it is more realistic as it targets best practices among DMUs rather than relying on average practices.…”

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

“…The control variables can take place as institutional and environmental factors, although they differ according to the purpose of the research (Bobo et al, 2018). Numbers of hospitals, beds, physicians, nurses þ midwives and other health personnel used as inputs in this study were also used as inputs in the studies conducted by Afonso and Aubyn (2011), Mutter et al (2008), Bilsel and Davutyan (2014), Lobo et al (2014), Karagiannis (2015), Samut and Cafri (2016), Chowdury and Zelenyuk (2016), Nistor et al (2017), Guo et al (2017), Du (2018), Guerrini et al (2018), Bobo et al (2018) and Habib and Shahwan (2020). Similarly, it has been observed that the variables of number of outpatients, proportion of inpatients and surgeries and risk-adjusted crude mortality rate are used in most studies in the literature (Mutter et al, 2008;Bilsel and Davutyan, 2014;Lobo et al, 2014;Chowdury and Zelenyuk, 2016;Nistor et al, 2017;Guo et al, 2017;Du, 2018;Bobo et al, 2018;Habib and Shahwan, 2020).…”

“…Numbers of hospitals, beds, physicians, nurses þ midwives and other health personnel used as inputs in this study were also used as inputs in the studies conducted by Afonso and Aubyn (2011), Mutter et al (2008), Bilsel and Davutyan (2014), Lobo et al (2014), Karagiannis (2015), Samut and Cafri (2016), Chowdury and Zelenyuk (2016), Nistor et al (2017), Guo et al (2017), Du (2018), Guerrini et al (2018), Bobo et al (2018) and Habib and Shahwan (2020). Similarly, it has been observed that the variables of number of outpatients, proportion of inpatients and surgeries and risk-adjusted crude mortality rate are used in most studies in the literature (Mutter et al, 2008;Bilsel and Davutyan, 2014;Lobo et al, 2014;Chowdury and Zelenyuk, 2016;Nistor et al, 2017;Guo et al, 2017;Du, 2018;Bobo et al, 2018;Habib and Shahwan, 2020). The control variables used in this study (proportion of university graduates, GDP per capita, metropolitan status and population) were found to have an effect on the efficiency score in the studies conducted by Afonso and Aubyn (2011), Bilsel and Davutyan (2014), Karagiannis (2015), Samut and Cafri (2016), Guo et al (2017), Du (2018) and Bobo et al (2018).…”