Effect of different patient peak arrivals on an Emergency Department via discrete event simulation

Abstract

Emergency Departments (EDs) overcrowding is a well recognized worldwide phenomenon. The consequences range from long waiting times for visit and treatment of patients, up to life-threatening health conditions. The international community is devoting greater and greater efforts to analyze this phenomenon aiming at reducing waiting times, improving the quality of the service. Within this framework, we propose a Discrete Event Simulation (DES) model to study the patient flows through a medium-size ED located in a region of Central Italy recently hit by a severe earthquake. In particular, our aim is to simulate unusual ED conditions, corresponding to critical events (like a natural disaster) which cause a sudden spike in the number of patient arrivals. The availability of detailed data concerning the ED processes enabled to build an accurate DES model and to perform extensive scenario analyses. The model provides a valid decision support system for the ED managers also in defining specific emergency plans to be activated in case of mass casualty disasters.

A simulation-based optimization approach for the calibration of a discrete event simulation model of an emergency department

“…Over the last few years, Emergency Departments (EDs) have been raising an increasing attention in the Operations Research and Management Science com-munities due to the international phenomenon of the overcrowding [19,30,36,11,28], which leads to longer waiting times, higher mortality rates, and lower patient satisfaction [31]. One of the most popular tools adopted to study this crucial problem is Discrete Event Simulation (DES) [39,20,37,7,23,13], which is used to represent the complex and stochastic patient flows in the ED in place of analytical models (for a recent literature review of simulation modeling in EDs, see [32]). Frequently, DES models are combined with an optimization algorithm to make optimal decisions in relation to some Key Performance Indicators (KPIs).…”