RESUMEN
To prevent the great dangers caused by emergency situations, providing rapid and high-quality emergency aid highly depends on the location of emergency medical centers. The purpose of this research is to present a multi-objective mathematical programing model based on the minimum P-envy algorithm to locate and construct emergency medical services (EMS). Maximizing the coverage in order to increase the probability of survival of different categories of patients, minimizing the costs of constructing EMS and optimizing the ratio of regions having the right to emergency medical services is one of the fundamental challenges in the health care system of countries. In this paper, a model for maximum utilization of EMS considering budget limitations is presented. In this study, since the problem is NP-Hard, the Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm were used to solve this problem. The parameters of the metaheuristic algorithms were tuned using the Taguchi method. Several instance problems were solved to compare the performance of 2 algorithms. The results demonstrate that the validity of the proposed model. Also, the mean of the solutions obtained by GA for small, medium, and large-size problems are better than the SA algorithm. Also, the GA algorithm obtained more efficient solutions than the SA algorithm.
RESUMEN
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.