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Evaluation of China's regional hospital efficiency: DEA approach with undesirable output

Author

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  • H-H Hu

    (Dalian Medical University, China)

  • Q Qi

    (Dalian Medical University, China)

  • C-H Yang

    (National Central University, Taiwan)

Abstract

This paper investigates regional hospital efficiency in China. As hospital operations naturally increase along with an undesirable output of patient mortality, which may induce medical disputes and ligations, this study adopts data envelopment analysis to evaluate and compare the efficiency scores obtained with and without considering the undesirable output. On the basis of province-level panel data over 2002–2008 and considering the risk-adjusted undesirable output, empirical estimates indicate that hospital efficiency is moderate, but increases gradually from 0.6881 to 0.8159. Importantly, without considering the undesirable output, the average efficiency score is overestimated and the efficiency ranking across provinces changes considerably. An efficiency gap is found between coastal and non-coastal regions, but this gap’s drop is mainly contributed by the fast efficiency improvement of the western regions. However, the central regions continue to achieve a significantly lower efficiency score than the eastern and western regions. Moreover, the initiation of the New Rural Cooperative Medical System has overall enhanced hospital efficiency in China, especially for the non-coastal regions.

Suggested Citation

  • H-H Hu & Q Qi & C-H Yang, 2012. "Evaluation of China's regional hospital efficiency: DEA approach with undesirable output," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(6), pages 715-725, June.
    • Handle: RePEc:pal:jorsoc:v:63:y:2012:i:6:p:715-725
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    Cited by:

    1. Sebastian Kohl & Jan Schoenfelder & Andreas Fügener & Jens O. Brunner, 2019. "The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals," Health Care Management Science, Springer, vol. 22(2), pages 245-286, June.
    2. Xiaohong Liu & Qingyuan Zhu & Junfei Chu & Xiang Ji & Xingchen Li, 2019. "Environmental Performance and Benchmarking Information for Coal-Fired Power Plants in China: A DEA Approach," Computational Economics, Springer;Society for Computational Economics, vol. 54(4), pages 1287-1302, December.
    3. Alejandro Arrieta & Jorge Guillén, 2017. "Output congestion leads to compromised care in Peruvian public hospital neonatal units," Health Care Management Science, Springer, vol. 20(2), pages 157-164, June.
    4. Zhiyang Shen & Vivian Valdmanis, 2020. "Identifying the contribution to hospital performance among Chinese regions by an aggregate directional distance function," Health Care Management Science, Springer, vol. 23(1), pages 142-152, March.
    5. Cordero, José Manuel & Alonso-Morán, Edurne & Nuño-Solinis, Roberto & Orueta, Juan F. & Arce, Regina Sauto, 2015. "Efficiency assessment of primary care providers: A conditional nonparametric approach," European Journal of Operational Research, Elsevier, vol. 240(1), pages 235-244.
    6. Huo, Baofeng & Gu, Minhao & Jiang, Bin, 2018. "China-related POM research: Literature review and suggestions for future research," International Journal of Production Economics, Elsevier, vol. 203(C), pages 134-153.
    7. Ya Chen & Justin Wang & Joe Zhu & H. David Sherman & Shin-Yi Chou, 2019. "How the Great Recession affects performance: a case of Pennsylvania hospitals using DEA," Annals of Operations Research, Springer, vol. 278(1), pages 77-99, July.
    8. Ortega-Díaz, M. Isabel & Martín, Juan Carlos, 2022. "How to detect hospitals where quality would not be jeopardized by health cost savings? A methodological approach using DEA with SBM analysis," Health Policy, Elsevier, vol. 126(10), pages 1069-1074.

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