Measuring the Effect of Size on Technical Efficiency of the United Arab Emirates Hospitals

Mahate, Ashraf A.; Hamidi, Samer; Akinci, Fevzi

doi:10.5539/gjhs.v9n3p116

Cited by 11 publications

(9 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is hoped that this study will lead to new insights into the elimination of one of the disadvantages of traditional DEA analyses -sensitivity to input and output variables' selection (and outliers), a number of inputs and outputs, measurement errors (for more details in the studies of, e.g. [34][35][36]42], or [80]), as well as on measurement variations [37].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Health system efficiency in OECD countries: dynamic network DEA approach

2021

View full text Add to dashboard Cite

Background In recent years, measuring and evaluating the efficiency of health systems has been explored in the context of seeking resources to ensure the sustainability of ‘countries’ health and social systems and addressing various crises in the health sector. The study aims to quantify and compare the efficiency of OECD health systems in 2000, 2008, and 2016. The contribution to research in the field of efficiency in the healthcare system can be seen in the application of Dynamic Network Data Envelopment Analysis (DNDEA), which help us to analyse not only the overall efficiency of the healthcare system but analyse the overall efficiency as the result of the efficiencies of individual interconnected areas (public and medical care area). By applying the DNDEA model, we can realise the analysis not only within one year, but we can find out if the measures and improvements taken in the healthcare sector have a positive impact on its efficiency in a later period (eight-year interval). Methods The analysis focuses on assessing the efficiency of the health systems of OECD countries over three periods: 2000, 2008, and 2016. Data for this study were derived from the existing OECD database, which provides aggregated data on OECD countries on a comparable basis. In this way, it was possible to compare different countries whose national health statistics may have their characteristics. The input-oriented Dynamic Network Data Envelopment Analysis model was used for data processing. The efficiency of OECD health systems has been analysed and evaluated comprehensively and also separately in two divisions: public health sub-division and medical care sub-division. The analysis combines the application of conventional and unconventional methods of measuring efficiency in the health sector. Results The results for the public health sub-division, medical care sub-division and overall health system for OECD countries under the assumption of constant returns to scale indicate that the average overall efficiency was 0.8801 in 2000, 0.8807 in 2008 and 0.8472 in 2016. The results of the input-oriented model with the assumption of constant returns to scale point to the overall average efficiency of health systems at the level of 0.8693 during the period. According to the Malmquist Index results, the OECD countries improved the efficiency over the years, with performance improvements of 19% in the public health division and 8% in the medical care division. Conclusions The results of the study are beneficial for health policymakers to assess and compare health systems in countries and to develop strategic national and regional health plans. Similarly, the result will support the development of international benchmarks in this area. The issue of health efficiency is an intriguing one that could be usefully explored in further research. A greater focus on combining non-parametric and parametric models could produce interesting findings for further research. The consistency in the publication and updating of the data on health statistics would help us establish a greater degree of accuracy.

show abstract

Section: Discussionmentioning

confidence: 99%

“…These are commonly used outputs in the evaluation efficiency of the medical care system (e.g. [2,5,9,32,35,36], or [41,42]). One of the advantages of the DNDEA model, which can be considered a contribution of the paper, is that we can consider so-called carry-over activities affecting the efficiency with time shift.…”

Section: Methodology and Datamentioning

confidence: 99%

Health system efficiency in OECD countries: dynamic network DEA approach

2021

View full text Add to dashboard Cite

show abstract

“…The total number of medical personnel, other technical personnel, and the number of non-medical personnel was used as an input variable by Cheng et al [ 13 ] who used variable numbers of doctors, nurses, and the number of administrative and other staff. Czypionka et al [ 16 ] used the variable of medical and non-medical staff, Varabyova and Schreyögg [ 65 ], and Li and Dong [ 38 ] used the variable of employees for the DEA model, Mahate et al [ 45 ] distributed input variables to physicians, dentists, nurses, pharmacists, administrative and other workers to estimate technical efficiency. Fragkiadakis et al [ 22 ] used inputs such as clinical staff, nurses and administrative staff, Rezapour et al [ 57 ] used the total staff of hospitals in the aggregate variable: human resources.…”

Section: Methodsmentioning

confidence: 99%

“…The most important are results are sensitive to outlier values (e.g. [ 29 , 45 , 49 , 57 , 66 ]), it’s just about measuring relative efficiency [ 28 , 49 , 56 ], limitation for the sample size [ 38 ].When choosing a DEA model, it is necessary to define initially if the input or output-oriented method will be used. The used methods are different for application to the healthcare sector.…”

Section: Introductionmentioning

confidence: 99%

“…The used methods are different for application to the healthcare sector. The output-oriented method is preferred by Araújo et al [ 2 ], Hernandez and San Sebastian [ 28 ], Oikonomou et al [ 51 ], Li and Dong [ 38 ], Karagiannis [ 32 ], Cheng et al [ 13 ], Villalobos et al [ 66 ]; Mujasi et al [ 49 ], Mahate et al [ 45 ], De Souza et al [ 18 ]. The input-oriented method was used in studies presented by Grosskopf et al [ 27 ], Kontodimopoulos et al [ 34 ], Czypionka et al [ 16 ], Rezapour et al [ 57 ], Büchner et al [ 10 ], Fragkiadakis et al [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Healthcare efficiency assessment using DEA analysis in the Slovak Republic

2018

View full text Add to dashboard Cite

A regional disparity is becoming increasingly important growth constraint. Policy makers need quantitative knowledge to design effective and targeted policies. In this paper, the regional efficiency of healthcare facilities in Slovakia is measured (2008–2015) using data envelopment analysis (DEA). The DEA is the dominant approach to assessing the efficiency of the healthcare system but also other economic areas. In this study, the window approach is introduced as an extension to the basic DEA models to evaluate healthcare technical efficiency in individual regions and quantify the basic regional disparities and discrepancies. The window DEA method was chosen since it leads to increased discrimination on results especially when applied to small samples and it enables year-by-year comparisons of the results. Two stable inputs (number of beds, number of medical staff), three variable inputs (number of all medical equipment, number of magnetic resonance (MR) devices, number of computed tomography (CT) devices) and two stable outputs (use of beds, average nursing time) were chosen as production variable in an output-oriented 4-year window DEA model for the assessment of technical efficiency in 8 regions. The database was made available from the National Health Information Center and the Slovak Statistical Office, as well as from the online databases Slovstat and DataCube. The aim of the paper is to quantify the impact of the non-standard Data Envelopment Analysis (DEA) variables as the use of medical technologies (MR, CT) on the results of the assessment of the efficiency of the healthcare facilities and their adequacy in the evaluation of the monitored processes. The results of the analysis have shown that there is an indirect dependence between the values of the variables over time and the results of the estimated efficiency in all regions. The regions that had low values of the variables over time achieved a high degree of efficiency and vice versa. Interesting knowledge was that the gradual addition of variables number of MR, number of CT and number of medical devices together, to the input side did not have a significant impact on the overall estimated efficiency of healthcare facilities.

show abstract

The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals

Kohl

Schoenfelder

Fügener

et al. 2018

Health Care Manag Sci

320

221

View full text Add to dashboard Cite

The healthcare sector in general and hospitals in particular represent a main application area for Data Envelopment Analysis (DEA). This paper reviews 262 papers of DEA applications in healthcare with special focus on hospitals and therefore closes a gap of over ten years that were not covered by existing review articles. Apart from providing descriptive statistics of the papers, we are the first to examine the research purposes of the publications. These research goals can be grouped into four distinct clusters according to our proposed framework. The four clusters are (1) "Pure DEA efficiency analysis", i.e. performing a DEA on hospital data, (2) "Developments or applications of new methodologies", i.e. applying new DEAy approaches on hospital data, (3) "Specific management question", i.e. analyzing the effects of managerial specification, such as ownership, on hospital efficiency, and (4) "Surveys on the effects of reforms", i.e. researching the impact of policy making, such as reforms of health systems, on hospital efficiency. Furthermore, we analyze the methodological settings of the studies and describe the applied models. We analyze the chosen inputs and outputs as well as all relevant downstream techniques. A further contribution of this paper is its function as a roadmap to important methodological literature and publications, which provide crucial information on the setup of DEA studies. Thus, this paper should be of assistance to researchers planning to apply DEA in a hospital setting by providing information on a) what has been published between 2005 and 2016, b) possible pitfalls when setting up a DEA analysis, and c) possible ways to apply the DEA analysis in practice. Finally, we discuss what could be done to advance DEA from a scientific tool to an instrument that is actually utilized by managers and policymakers.

show abstract

Measuring the Effect of Size on Technical Efficiency of the United Arab Emirates Hospitals

Cited by 11 publications

References 16 publications

Health system efficiency in OECD countries: dynamic network DEA approach

Health system efficiency in OECD countries: dynamic network DEA approach

Healthcare efficiency assessment using DEA analysis in the Slovak Republic

The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals

Contact Info

Product

Resources

About