A novel method for computing single output for DEA with application in hospital efficiency

• Published in: Socio-economic planning sciences Vol. 76; p. 100995
• Main Authors: Yeşilyurt, M. Ensar, Şahin, Emre, Elbi, M. Doğan, Kızılkaya, Aydın, Koyuncuoğlu, M. Ulaş, Akbaş-Yeşilyurt, Filiz
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.08.2021; Elsevier Science Ltd
• Subjects: Data envelopment analysis; Decision making; Decision making units; Hospital efficiency; Particle swarm optimization; Public sector efficiency; Real time; Simplicity; Simulation; Single output; Stochastic frontier analysis; Usefulness; Virtual reality; Turkey; Stochastic frontier analysis; Particle swarm optimization; Hospital efficiency; Single output; Data envelopment analysis; Public sector efficiency
• ISSN: 0038-0121; 1873-6041
• DOI: 10.1016/j.seps.2020.100995

There are two main methods for measuring the efficiency of decision-making units (DMUs): data envelopment analysis (DEA) and stochastic frontier analysis (SFA). Each of these methods has advantages and disadvantages. DEA is more popular in the literature due to its simplicity, as it does not require any pre-assumption and can be used for measuring the efficiency of DMUs with multiple inputs and multiple outputs, whereas SFA is a parametric approach that is applicable to multiple inputs and a single output. Since many applied studies feature multiple output variables, SFA cannot be used in such cases. In this research, a unique method to transform multiple outputs to a virtual single output is proposed. We are thus able to obtain efficiency scores from calculated virtual single output by the proposed method that are close (or even the same depending on targeted parameters at the expense of computation time and resources) to the efficiency scores obtained from multiple outputs of DEA. This will enable us to use SFA with a virtual single output. The proposed method is validated using a simulation study, and its usefulness is demonstrated with real application by using a hospital dataset from Turkey. •We propose a unique, customized PSO-based method integrated with a DEA to transfer multiple outputs to a single output.•Various datasets are produced by a random data processor to test the performance of the proposed method.•The objective function is to minimize RMSE or maximize the correlation.•The performance of the proposed method is demonstrated with real application of hospital efficiency.