A Simulation Optimization Approach for Resource Allocation in an Emergency Department Healthcare Unit
Author(s) -
Arman Bahari,
Farzaneh Asadi
Publication year - 2020
Publication title -
global heart
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.012
H-Index - 37
eISSN - 2211-8179
pISSN - 2211-8160
DOI - 10.5334/gh.528
Subject(s) - emergency department , medicine , resource allocation , unit (ring theory) , health care , resource (disambiguation) , order (exchange) , control (management) , quality (philosophy) , operations management , medical emergency , operations research , computer science , nursing , business , psychology , computer network , philosophy , mathematics education , finance , epistemology , artificial intelligence , engineering , economics , economic growth
Background: Effective Decision Making on the resources of the ED plays a significant role in the performance of the department. Since wrong decisions can have irreparable consequences on the quality of services, the decision-makers should analyze and allocate the resources effectively. Methods: The present study aimed to investigate the effective resources in the emergency department and provide an optimal combination of these resources based on the meta-modeling optimization approach to reduce the wait time for patients in the ED. Results: The results demonstrated that the number of CHWs and beds played a significant role in the total average wait time for patients. Although the effect of other variables was not statistically significant, they were deliberately used in this study to determine the optimal combination of such variables by solving the problem. Conclusion: The findings of the present simulation-model approach provide hospital managers with valuable data in order to control and re-design the admission to discharge procedure in the emergency in order to enhance efficiency. By considering the budget, the new configuration of 2 Community Health Worker, 1 Receptionist, 1 nurses, 3 Cardiologist and 10 beds, with 142 minutes of a patient’s wait time shows 49.6% wait time improvement and a reduction of 51% in the cost of resource usage.
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