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

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

Adel Hatami-Marbini, Madjid Tavana, Ali Emrouznejad
Copyright: © 2012 |Volume: 2 |Issue: 2 |Pages: 35
ISSN: 2156-177X|EISSN: 2156-1761|EISBN13: 9781466612167|DOI: 10.4018/ijfsa.2012040101
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MLA

Hatami-Marbini, Adel, et al. "Productivity Growth and Efficiency Measurements in Fuzzy Environments with an Application to Health Care." IJFSA vol.2, no.2 2012: pp.1-35. http://doi.org/10.4018/ijfsa.2012040101

APA

Hatami-Marbini, A., Tavana, M., & Emrouznejad, A. (2012). Productivity Growth and Efficiency Measurements in Fuzzy Environments with an Application to Health Care. International Journal of Fuzzy System Applications (IJFSA), 2(2), 1-35. http://doi.org/10.4018/ijfsa.2012040101

Chicago

Hatami-Marbini, Adel, Madjid Tavana, and Ali Emrouznejad. "Productivity Growth and Efficiency Measurements in Fuzzy Environments with an Application to Health Care," International Journal of Fuzzy System Applications (IJFSA) 2, no.2: 1-35. http://doi.org/10.4018/ijfsa.2012040101

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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.

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