Productivity Growth and Efficiency Measurement in Fuzzy Environment with Application to Health Care

In a competitive environment, health care organizations must continuously improve their productivity to sustain long-term growth and profitability. Good productivity performance has been mostly assumed to be a natural outcome of successful health care management. Data envelopment analysis (DEA) is a widely used mathematical programming approach 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) has been 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, we propose a novel productivity measurement approach with MPI in Fuzzy environments. An application of the proposed framework 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