Towards patient flow optimization in emergency departments using genetic algorithms

Abstract

This study aims to optimize patient flow in emergency departments (ED) while minimizing associated costs. In order to compare the effects of the optimization, a simulation model for emergency departments has been implemented using District Event Simulation (DES) and queuing theory, while for the optimization, Genetic Algorithm is used to find the best arrangements. Principally, a discrete event based, multi-class, multi-server queuing network is designed considering the emergency department as a set of stages each associated with a queue of patients waiting to be served. Each stage has multiple service providers such as Nurses, Doctors or other staff. We also classified patients passing through the stages according to their acuity level and personal characteristics. Then, a function is defined to measure the ED performance in respect to the calculated wait times and the cost. Finally, a customized genetic algorithm was developed to discover the best performance which reflects the best allocation of service providers to the different stages of the emergency department.