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Benchmarking in Healthcare: An Approach Based on Closest Targets

In: Managing Service Productivity

Author

Listed:
  • Juan Aparicio

    (University Miguel Hernandez of Elche)

  • Fernando Borras

    (University Miguel Hernandez of Elche)

  • Lidia Ortiz

    (University Miguel Hernandez of Elche)

  • Jesus T. Pastor

    (University Miguel Hernandez of Elche)

Abstract

This chapter examines the process of benchmarking in healthcare. In particular, we focus on hospital performance. We are especially interested in showing that the determination of closest targets as a benchmarking technique has significant advantages over traditional Data Envelopment Analysis (DEA) methods for signaling keys for the inefficient hospitals to improve their performance. In doing so, we use a sample of hospital located in the eastern region of Spain. We show the computational problems related to the estimation of this type of targets and the ways that exist to solve such problems. Finally, we suggest in the application some guidance in relation to determining potential improvement targets for each of the inefficient units.

Suggested Citation

  • Juan Aparicio & Fernando Borras & Lidia Ortiz & Jesus T. Pastor, 2014. "Benchmarking in Healthcare: An Approach Based on Closest Targets," International Series in Operations Research & Management Science, in: Ali Emrouznejad & Emilyn Cabanda (ed.), Managing Service Productivity, edition 127, pages 67-91, Springer.
    • Handle: RePEc:spr:isochp:978-3-662-43437-6_5
    DOI: 10.1007/978-3-662-43437-6_5
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    Cited by:

    1. Aparicio, Juan & Garcia-Nove, Eva M. & Kapelko, Magdalena & Pastor, Jesus T., 2017. "Graph productivity change measure using the least distance to the pareto-efficient frontier in data envelopment analysis," Omega, Elsevier, vol. 72(C), pages 1-14.
    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. Zhu, Qingyuan & Aparicio, Juan & Li, Feng & Wu, Jie & Kou, Gang, 2022. "Determining closest targets on the extended facet production possibility set in data envelopment analysis: Modeling and computational aspects," European Journal of Operational Research, Elsevier, vol. 296(3), pages 927-939.
    4. Zhu, Qingyuan & Wu, Jie & Ji, Xiang & Li, Feng, 2018. "A simple MILP to determine closest targets in non-oriented DEA model satisfying strong monotonicity," Omega, Elsevier, vol. 79(C), pages 1-8.

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