SFA vs. DEA for Measuring Healthcare Efficiency: A Systematic Review

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 search articles applying combined data envelopment analysis (DEA) and stochastic frontier analysis (SFA) in order to facilitate a common understanding about the adequacy of these methods, defining any differences in healthcare efficiency estimation and the reasons that are behind this. A systematic review of 21 such studies published the last decade was conducted. Only studies written in English were considered. Results are summarized in a form of meta-analysis in order to synthesize results and draw out further implications. Overall, DEA and SFA were found to yield divergent efficiency estimates due to many factors such as statistical noise, how inputs and outputs were defined, as well as data availability. Researchers, besides the combination of models to measure efficiency, lately have introduced environmental variables in their analyses, aiming at better understanding the relationship of these factors to efficiency and thus achieving a better decision making process. In any case 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 efficiency and thus it is necessary to model the behavior which generates the data by choosing the appropriate mathematical for