Rabbani, M., Heidari, N., Farrokhi Asl, H. (2016). A Bootstrap Interval Robust Data Envelopment Analysis for Estimate Efficiency and Ranking Hospitals. Journal of Industrial Engineering and Management Studies, 3(2), 107-122.
M. Rabbani; N. Heidari; H. Farrokhi Asl. "A Bootstrap Interval Robust Data Envelopment Analysis for Estimate Efficiency and Ranking Hospitals". Journal of Industrial Engineering and Management Studies, 3, 2, 2016, 107-122.
Rabbani, M., Heidari, N., Farrokhi Asl, H. (2016). 'A Bootstrap Interval Robust Data Envelopment Analysis for Estimate Efficiency and Ranking Hospitals', Journal of Industrial Engineering and Management Studies, 3(2), pp. 107-122.
Rabbani, M., Heidari, N., Farrokhi Asl, H. A Bootstrap Interval Robust Data Envelopment Analysis for Estimate Efficiency and Ranking Hospitals. Journal of Industrial Engineering and Management Studies, 2016; 3(2): 107-122.
A Bootstrap Interval Robust Data Envelopment Analysis for Estimate Efficiency and Ranking Hospitals
1School of Industrial Engineering, College of Engineering, University of Tehran.
2School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran.
Abstract
Data envelopment analysis (DEA) is one of non-parametric methods for evaluating efficiency of each unit. Limited resources in healthcare economy is the main reason in measuring efficiency of hospitals. In this study, a bootstrap interval data envelopment analysis (BIRDEA) is proposed for measuring the efficiency of hospitals affiliated with the Hamedan University of Medical Sciences. The proposed method is capable to consider uncertainty and sampling errors. The inputs of this model include total number of personals, number of medical equipment, and number of operational beds. Also, outputs consist of number of inpatients, number of outpatients, number of special patients, bed-day, and bed occupancy rate. First, we estimate the efficiency by applying original DEA that does not consider any uncertainty and sampling error; then we utilize RDEA that considers uncertainty and after that we use BRDEA that consider both uncertainty and sampling error with an adaptation of bootstrapped robust data envelopment analysis and could be more reliable for efficiency estimating strategies.
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