Hybrid Simulation-Optimization Approach for Emergency Department Resource Allocation

Abstract

Proper allocation of resources within the Emergency Departments (EDs) is very important in enhancing patient outcomes, wait times, and operational efficiency. The following paper introduces a Hybrid Simulation-Optimization Approach, which is a hybrid of the Discrete-Event Simulation (DES) and optimization algorithms in the allocation of resources, including medical staff, beds, and equipment dynamically. The implementation of the model was based on real-life ED data available in a hospital in a metropolitan city, and the model was compared to the traditional cloud-based and heuristic resource allocation models. The findings indicated a great improvement using the Hybrid Simulation-Optimization Model with 85 percent staff utilization, 4.5 patients treated / hour per staff member and 15 percent staff idle time, in comparison to Cloud-Based System that achieved 70 percent staff utilization and the staff-patient ratio was 1:6. More so, the Hybrid Model cut patient waiting time to 10 minutes as compared to the Cloud-Based System that recorded 15 minutes. These results pinpoint the power of the integration of Simulation and optimization to optimize the allocation of staff, manpower, and usage of resources in EDs. The staff-patient ratio was also streamlined to 1:4, which allowed enhancing the balance of work. The results of the staff utilization and patient care time improvements prove the usefulness of real-time information integration and optimization algorithms. Further studies on the subject should aim at extending the method to multiple hospitals, adding real-time data feeds, and machine learning algorithms to better predict demand and allocate resources, eventually enhancing the management of healthcare resources in a wide range of EDs.