Application of discrete-event simulation in health care clinics: A survey

Abstract

In recent decades, health care costs have dramatically increased, while health care organisations have been under severe pressure to provide improved quality health care for their patients. Several health care administrators have used discreteevent simulation as an effective tool for allocating scarce resources to improve patient¯ow, while minimising health care delivery costs and increasing patient satisfaction. The rapid growth in simulation software technology has created numerous new application opportunities, including more sophisticated implementations, as well as combining optimisation and simulation for complex integrated facilities. This paper surveys the application of discrete-event simulation modeling to health care clinics and systems of clinics (for example, hospitals, outpatient clinics, emergency departments, and pharmacies). Future directions of research and applications are also discussed.

Patient flow improvement for an ophthalmic specialist outpatient clinic with aid of discrete event simulation and design of experiment

“…Since the introduction of DES in the late 1950s, DES has evolved to become one of the most commonly used modeling techniques in industries involving manufacturing processes, public transportation systems, provision of services, etc. [6][7][8][9]. Over the past thirty years, the dramatic increase in the cost of healthcare compelled researchers and healthcare professionals to examine new ways to improve the efficiency of health services; DES is one such tool [9].…”

Maximising Patient Throughput using Discrete-event Simulation

“…The use of discrete-event simulation to model hospital departments is well documented in the literature (Jun, Jacobson and Swisher, 1999;Anderson and Merode, 2007;Gunal and Pidd, 2010). Studies include making strategic decisions for various departments (Ballard and Kuhl, 2006;Denton et al, 2006;Vanberkel and Blake, 2007;Leskovar et al, 2011); estimating capacity levels and measuring waiting times (Werker et al, 2009); analysing patient flows (Brenner et al, 2010;Zeng et al, 2012); measuring policy impact (Fletcher et al, 2007); and simulating patient scheduling and utilisations (Harper and Gamlin, 2003;Werker et al, 2009;Lee et al, 2013;Quevedo and Chapilliquén, 2014).…”

A Decision Support System for Capacity Planning in Emergency Departments

“…Simulation modelling is a popular tool to analyse and improve healthcare delivery (see, for instance, the review papers [9][10][11]). Consequently, a vast amount of literature on ED modelling and ED patient flow is available (see, e.g.…”