Optimization of Service Process in Emergency Department Using Discrete Event Simulation and Machine Learning Algorithm

Emergency departments are operating with limited resources and high levels of unexpected re-quests. This study aimed to minimize patients’ waiting time and the percentage of units’ engagement to improvethe emergency department (ED) efficiency.

A comprehensive combination method involving DiscreteEvent Simulation (DES), Artificial Neural Network (ANN) algorithm, and finally solving the model by use of Ge-netic Algorithm (GA) was used in this study. After simulating the case and making sure about the validity of themodel, experiments were designed to study the effects of change in individuals and equipment on the averagetime that patients wait, as well as units’ engagement in ED. Objective functions determined using Artificial Neu-ral Network algorithm and MATLAB software were used to train it. Finally, after estimating objective functionsand adding related constraints to the problem, a fractional Genetic Algorithm was used to solve the model.Re-sults:According to the model optimization result, it was determined that the hospitalization unit, as well asthe hospitalization units’ doctors, were in an optimized condition, but the triage unit, as well as the fast trackunits’ doctors, should be optimized. After experiments in which the average waiting time in the triage sectionreached near zero, the average waiting time in the screening section was reduced to 158.97 minutes and also thecoefficient of units’ engagement in both sections were 69% and 84%, respectively.

Using the ser-vice optimization method creates a significant improvement in patient’s waiting time and stream at emergencydepartments, which is made possible through appropriate allocation of the human and material resources.