Flokou Angeliki, Aletras Vassilis, Niakas Dimitris
School of Social Sciences, Hellenic Open University, Patra, Greece.
Department of Business Administration, University of Macedonia, Thessaloniki, Greece.
PLoS One. 2017 May 23;12(5):e0177946. doi: 10.1371/journal.pone.0177946. eCollection 2017.
The main objective of this study was to apply the non-parametric method of Data Envelopment Analysis (DEA) to measure the efficiency of Greek NHS hospitals between 2009-2013. Hospitals were divided into four separate groups with common characteristics which allowed comparisons to be carried out in the context of increased homogeneity. The window-DEA method was chosen since it leads to increased discrimination on the results especially when applied to small samples and it enables year-by-year comparisons of the results. Three inputs -hospital beds, physicians and other health professionals- and three outputs-hospitalized cases, surgeries and outpatient visits- were chosen as production variables in an input-oriented 2-year window DEA model for the assessment of technical and scale efficiency as well as for the identification of returns to scale. The Malmquist productivity index together with its components (i.e. pure technical efficiency change, scale efficiency change and technological scale) were also calculated in order to analyze the sources of productivity change between the first and last year of the study period. In the context of window analysis, the study identified the individual efficiency trends together with "all-windows" best and worst performers and revealed that a high level of technical and scale efficiency was maintained over the entire 5-year period. Similarly, the relevant findings of Malmquist productivity index analysis showed that both scale and pure technical efficiency were improved in 2013 whilst technological change was found to be in favor of the two groups with the largest hospitals.
本研究的主要目的是应用数据包络分析(DEA)的非参数方法来衡量2009年至2013年希腊国民健康服务体系(NHS)医院的效率。医院被分为四个具有共同特征的独立组,这使得能够在更高同质性的背景下进行比较。之所以选择窗口DEA方法,是因为它能增强结果的区分度,尤其是应用于小样本时,并且能对结果进行逐年比较。在一个以投入为导向的2年窗口DEA模型中,选择了三个投入变量——医院病床数、医生和其他卫生专业人员——以及三个产出变量——住院病例数、手术例数和门诊就诊次数——作为生产变量,以评估技术效率和规模效率以及确定规模报酬情况。还计算了Malmquist生产率指数及其组成部分(即纯技术效率变化、规模效率变化和技术进步),以便分析研究期第一年和最后一年之间生产率变化的来源。在窗口分析的背景下,该研究确定了个体效率趋势以及“所有窗口”中的最佳和最差表现者,并表明在整个5年期间都保持了较高水平的技术效率和规模效率。同样,Malmquist生产率指数分析的相关结果表明,2013年规模效率和纯技术效率均有所提高,而技术进步有利于两家最大医院所在的两组。