Evaluation of Hospital Performance with Data Envelopment Analysis
AbstractPerformance evaluation provides information for lacking organizations and demonstrates how to improve performance for hospitals. Formerly, hospitals had to afford to meet the increased demand of their patients by only decreasing their operational costs. In this parallel most of the hospital was first to cut costs or avoid cases that would likely lose money. However, later health care administrators realized that the appropriate solution to keep their hospitals financially viable was to improve their performance. Efficiency analysis based on optimization techniques and their normative structure creates the benchmark for the hospitals. This is one of the most essential requirements of health care industry today. Data Envelopment Analysis (DEA) is a non-parametric linear programming technique that assesses the efficiency frontier by optimizing the weighted outputs to inputs. DEA models can provide the new solutions to increase the efficiency. DEA identifies the optimal ways of efficiency for each of the hospital rather than the averages. Since this is an appropriate way to understand the individual hospital efficiency, DEA provide the significant findings for the improvement process of hospitals. Hospitals can not only find their efficiency level, but also discover the alternative solutions to eliminate the inefficiency causes. The results of this study have also provided meaningful insights into Turkish health care managers’ views of the interaction between efficiency and health care expenditures. It is expected that the findings will provide guidance for health care providers. Results also might be beneficial for other researchers in this area.
Mar 2, 2018
How to Cite
KARAHAN, Mehmet. Evaluation of Hospital Performance with Data Envelopment Analysis. European Journal of Multidisciplinary Studies, [S.l.], v. 3, n. 2, p. 53-59, mar. 2018. ISSN 2414-8385. Available at: <http://journals.euser.org/index.php/ejms/article/view/3119>. Date accessed: 17 nov. 2018. doi: http://dx.doi.org/10.26417/ejms.v7i2.p53-59.
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