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Productivity Growth and Efficiency Measurements in Fuzzy Environments with an Application to Health Care

Author

Listed:
  • Adel Hatami-Marbini

    (Université catholique de Louvain, Belgium)

  • Madjid Tavana

    (La Salle University, USA)

  • Ali Emrouznejad

    (Aston Business School, UK)

Abstract

Health care organizations must continuously improve their productivity to sustain long-term growth and profitability. Sustainable productivity performance is mostly assumed to be a natural outcome of successful health care management. Data envelopment analysis (DEA) is a popular mathematical programming method for comparing the inputs and outputs of a set of homogenous decision making units (DMUs) by evaluating their relative efficiency. The Malmquist productivity index (MPI) is widely used for productivity analysis by relying on constructing a best practice frontier and calculating the relative performance of a DMU for different time periods. The conventional DEA requires accurate and crisp data to calculate the MPI. However, the real-world data are often imprecise and vague. In this study, the authors propose a novel productivity measurement approach in fuzzy environments with MPI. An application of the proposed approach in health care is presented to demonstrate the simplicity and efficacy of the procedures and algorithms in a hospital efficiency study conducted for a State Office of Inspector General in the United States.

Suggested Citation

  • Adel Hatami-Marbini & Madjid Tavana & Ali Emrouznejad, 2012. "Productivity Growth and Efficiency Measurements in Fuzzy Environments with an Application to Health Care," International Journal of Fuzzy System Applications (IJFSA), IGI Global, vol. 2(2), pages 1-35, April.
    • Handle: RePEc:igg:jfsa00:v:2:y:2012:i:2:p:1-35
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    Citations

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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. Ali Bahari & Ali Emrouznejad, 2014. "Influential DMUs and outlier detection in data envelopment analysis with an application to health care," Annals of Operations Research, Springer, vol. 223(1), pages 95-108, December.
    3. Lobo, Maria Stella de Castro & Estellita Lins, Marcos Pereira & Rodrigues, Henrique de Castro & Soares, Gabriel Martins, 2022. "Planning feasible and efficient operational scenarios for a university hospital through multimethodology," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).

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