Resource planning in the emergency departments: A simulation-based metamodeling approach

• Published in: Simulation modelling practice and theory Vol. 53; pp. 123 - 138
• Main Authors: Zeinali, Farzad, Mahootchi, Masoud, Sepehri, Mohammad Mehdi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2015
• Subjects: Decision support system; Emergency department; Healthcare management; Metamodeling; Simulation-based-optimization; Simulation-based-optimization; Emergency department; Decision support system; Healthcare management; Metamodeling
• ISSN: 1569-190X; 1878-1462
• DOI: 10.1016/j.simpat.2015.02.002

Patient’s congestion and their long waiting times in Emergency Departments (EDs) are the most common problems in hospitals. This paper extends application domain of metamodels into decision-making in the EDs by developing a discrete event simulation (DES) model combined with suitable metamodels. This is used as a novel decision support system to improve the patients flow and relieve congestion by changing the number of ED resources (i.e., the number of receptionists, nurses, residents, and beds). This new tool could be used for decision-making in operational, tactical, and strategic levels. In the first step, we develop a simulation routine of the ED in order to evaluate the system performance measure (total average waiting times of patients) for each configuration of resources. In the next step, we use different metamodel techniques and choose one with the maximum efficiency through a cross validation technique to replace the computationally expensive DES model with an accurate and efficient metamodel. Then the proposed model is used to minimize the total average waiting times of patients subject to both budget and capacity constraints. We implement our proposed model in an emergency department in Iran. Experimental results with the current ED budget verify that after using the resource allocation obtained from the proposed model, the total waiting time of patients is reduced by about 48%. Furthermore, to evaluate the efficiency of the selected metamodel, we compare the respective results with those obtained through OptQuest in terms of both the accuracy and the time needed to perform the optimization process.