An empirical study of comparing DEA and SFA methods to measure hospital units’ efficiency

Frontier techniques have been used to measure healthcare provider efficiency in hundreds of published studies. Although these methods have the potential to be useful to decision makers, their utility is limited by both methodological questions concerning their application. The aim of this paper is to examine the data envelopment analysis (DEA) and stochastic frontier analysis (SFA) results in order to facilitate a common understanding about the adequacy of these methods, defining any differences in healthcare efficiency estimation. A two-stage bootstrap DEA method and the Translog formula of the SFA were performed. Multi-inputs and multi-outputs were used in both of the approaches assuming two scenarios either including environmental variables or not. The introduced environmental variables were regressed with the bias corrected estimations derived from the first step of the two-stage bootstrap DEA model. In the Translog SFA functional form, these variables were introduced as shifted. Thirty-two Greek public hospital units constitute the sample. The main output of the analysis was that the efficiency scores increased with the incorporation of environmental variables in the SFA model, with the average efficiency score to become from 0.85 to 0.89. However, DEA and SFA were found to yield divergent efficiency estimates due to many factors such as the nature of the environmental variables, the measurement error and other random factors. Environmental variables being hospital status and geographical position were found significantly correlating with inefficiency, while patient mobility was not found strongly correlating. The analysis concludes that there is a need for careful attention by stakeholders since the nature of the data and its availability influence the measurement of the efficiency and thus it is necessary to be specific when choosing the appropriate mathematical form in order to test the behavior which generates the data.