Data Analytics and Simulation Optimization for Emergency Department Operations

Abstract

Hospital emergency department (ED) overcrowding is a severe and longstanding issue confronting many countries and cities around the world. Ideally, EDs are established to provide immediate medical care to critically ill or severely injured patients. Thus, timeliness and efficiency are their core attributes. However, due to various causes of overcrowding, it is challenging for EDs to guarantee the provision of timely medical care for patients. In this talk, I will present a collaborative project with an ED in Hong Kong on improving their patient flows and system efficiency. Machine learning models have been applied to provide real-time and personalized patient waiting times. A simulation model that captures all complicating factors in reality (e.g., time and category-dependent arrival rates of patients, multiple shift-times of doctors and re-entrant flows to the many “service stations” of the system) has been developed to examine possible solutions that could relieve the overcrowding situation. I will discuss the challenge that several key types of data were unavailable such that the stochastic components in the system could not be directly estimated. Computational results show that our simulation model can produce results consistent with the actual observations. Simulation optimization approaches have been developed to determine resource allocation decisions in the ED. I will also discuss some insights, derived from the simulation results, into managing ED operations.