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Measuring performance in the presence of noisy data with targeted desirable levels: evidence from healthcare units

Author

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  • Panagiotis Mitropoulos

    (University of Patras)

  • Panagiotis D. Zervopoulos

    (University of Sharjah)

  • Ioannis Mitropoulos

    (University of Patras)

Abstract

Noise in data is not uncommon in real-world cases, although it is commonly omitted from performance measurement studies. In this paper, we develop a stochastic DEA-based methodology to measure performance when the endogenous (e.g. efficiency) and exogenous variables (e.g. perspectives of patients’ satisfaction), which are incorporated in the assessment, are inversely related. This methodology identifies benchmark units that are not only efficient but are also assigned scores for their exogenous variables, which are at least equal to user-defined critical values. We apply the performance measurement methodology to the 14 largest Cypriot health centers. The advantages of our methodology are pointed out through comparative analysis with alternative stochastic and non-stochastic DEA approaches.

Suggested Citation

  • Panagiotis Mitropoulos & Panagiotis D. Zervopoulos & Ioannis Mitropoulos, 2020. "Measuring performance in the presence of noisy data with targeted desirable levels: evidence from healthcare units," Annals of Operations Research, Springer, vol. 294(1), pages 537-566, November.
    • Handle: RePEc:spr:annopr:v:294:y:2020:i:1:d:10.1007_s10479-019-03280-5
    DOI: 10.1007/s10479-019-03280-5
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