Performance evaluation of nonhomogeneous hospitals: the case of Hong Kong hospitals
- 131 Downloads
Throughout the world, hospitals are under increasing pressure to become more efficient. Efficiency analysis tools can play a role in giving policymakers insight into which units are less efficient and why. Many researchers have studied efficiencies of hospitals using data envelopment analysis (DEA) as an efficiency analysis tool. However, in the existing literature on DEA-based performance evaluation, a standard assumption of the constant returns to scale (CRS) or the variable returns to scale (VRS) DEA models is that decision-making units (DMUs) use a similar mix of inputs to produce a similar set of outputs. In fact, hospitals with different primary goals supply different services and provide different outputs. That is, hospitals are nonhomogeneous and the standard assumption of the DEA model is not applicable to the performance evaluation of nonhomogeneous hospitals. This paper considers the nonhomogeneity among hospitals in the performance evaluation and takes hospitals in Hong Kong as a case study. An extension of Cook et al. (2013)  based on the VRS assumption is developed to evaluated nonhomogeneous hospitals’ efficiencies since inputs of hospitals vary greatly. Following the philosophy of Cook et al. (2013) , hospitals are divided into homogeneous groups and the product process of each hospital is divided into subunits. The performance of hospitals is measured on the basis of subunits. The proposed approach can be applied to measure the performance of other nonhomogeneous entities that exhibit variable return to scale.
KeywordsData envelopment analysis Hospital efficiency Nonhomogeneity Subunit
This paper was finished while Xiyang Lei was visiting the University of Strathclyde with financial support from the China Scholarship Council. Alec Morton would like to thank the University of Science and Technology of China for their hospitality while working on this paper, as well as the government of Anhui province for their support under the 100 Talents scheme. This research was supported by National Natural Science Foundation of China (Grant No.s 71271196 and 71701060), the Youth Innovation promotion Association of Chinese Academy of Sciences, the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (Grant No. 71121061), the Fund for International Cooperation and Exchange of the National Natural Science Foundation of China (Grant No. 71110107024), the National Science Foundation of China for Distinguished Youth Scholars (Grant No. 71225002), Anhui Social Sciences Foundation (Grant No. AHSKY2017D78), and the Fundamental Research Funds for the Central Universities (Grant Nos. JZ2016HGBZ0996 and JS2017HGXJ0028).
- 3.Cooper WW, Seiford LM, Zhu J (2011) Data envelopment analysis: history, models, and interpretations. Springer US, New YorkGoogle Scholar
- 38.Wang K, Xian YJ, Lee CY, Wei YM, Huang ZM (2017) On selecting directions for directional distance functions in a non-parametric framework: a review. Ann Oper Res:1–34Google Scholar