A hybrid multi-criteria decision making model for elective admission control in a Chinese public hospital

• Published in: Knowledge-based systems Vol. 173; pp. 37 - 51
• Main Authors: Zhu, Ting, Luo, Li, Liao, Huchang, Zhang, Xinli, Shen, Wenwu
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.06.2019; Elsevier Science Ltd
• Subjects: Admission control; Comparative analysis; Decision making; Decision making models; Dependence; Empirical analysis; Fuzzy sets; Fuzzy systems; Hospitals; Medical decision-making; Medical expert systems; Medical services; Multiple criteria decision making; Multiple criterion; Patient admissions; Sensitivity analysis; China; Fuzzy sets; Medical decision-making; Medical expert systems; Medical services; Fuzzy systems
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2019.02.020

In healthcare service systems, patients are not always served in the order they arrive, but are ranked with respect to their relative “importance” and “urgency” to the service system. We consider such a system where elective admission requests backlogged on a list wait to be assigned inpatient beds. To consolidate the performance of Classified Diagnose and Treatment in China, determining an optimal admission priority assignment policy for all waiting patients is vital. It is a complicated multi-criteria decision making (MCDM) problem involving both qualitative and quantitative criteria. Evaluating the admission priority of each patient is based on vague information or uncertain data in which significant dependence and feedback between the evaluation dimensions and criteria may exist. This paper applies a hybrid MCDM model that integrates the 2-tuple DEMATEL technique and the fuzzy VIKOR method to the elective admission control problem. It makes use of the modified 2-tuple DEMATEL to determine the relative weights of the evaluation criteria and the fuzzy VIKOR method to assess the alternatives (waiting patients) over the criteria. An empirical case in West China Hospital is presented to demonstrate the applicability of the proposed approach. Sensitivity analysis of the results by the proposed hybrid MCDM model and comparative analysis with other different approaches are presented. The results show that the proposed model is effective and provides insightful implications for hospital managers to refer.