The Simulation of Hospital Systems

Discrete event simulation for performance modelling in health care: a review of the literature

“…The literature has very few examples of such studies. Surprisingly, though, Fetter and Thompson (1965) is a very early example of DES that reports a whole hospital simulation, with a special interest in maternity processes. The aim of this work was to give a decision support tool to hospital administrations to predict the consequences of design changes and alternative policies.…”

Maximising Patient Throughput using Discrete-event Simulation

“…It can be argued that at the core of these concerns is the scheduling of patient appointments, with much research available on systems to aid appointment scheduling (Fetter and Thompson, 1965;Kuljis, Paul and Chen, 2001;Harper and Gamlin, 2003;Gunal and Pidd, 2010). It can be difficult to successfully balance utilisation and satisfaction if the patient scheduling is not optimised for the current clinic setup.…”

Integrated decision making in healthcare : an operations research and management science perspective

“…Methods: computer simulation [12,178,333,390,417,496], heuristics [333], Markov processes [390], queueing theory [419,535].…”

“…References differ in the extent in which various aspects are incorporated in the applied models. Frequently modeled aspects that influence the performance of an appointment schedule are patient punctuality [178,323,518], patients not showing up ('no-shows') [178,179,255,283], walk-in patients or urgent patients [12,178,417,535], doctor lateness at the start of a consultation session [178,179,332,421], doctor interruptions (e.g., by comfort breaks or administration) [179,323], and the variance of consultation duration [254]. These factors can be taken into account when modeling the following key decisions that together design an appointment schedule.…”

“…The number of patients per consultation session is chosen to control patient access times and patient waiting times. When the number of patients is increased, access times may decrease, but patient waiting times and provider overtime tend to increase [85,178,254].…”