Table of Contents
Advances in Emergency Medicine
Volume 2014, Article ID 478675, 7 pages
http://dx.doi.org/10.1155/2014/478675
Research Article

A Simulated Discrete-Event and Queuing Model to Reduce Transfers from the Emergency Department and to Optimize Hospital Bed Management

1Université Pierre et Marie Curie-Paris 6, UMR S 707, 75012 Paris, France
2Hôpital Saint Camille, 94360 Bry-sur-Marne, France
3Study Group on Efficiency and Quality in Non-Scheduled Activities, 75018 Paris, France
4Department of Medical Information, AP-HP, Hôpital Saint Antoine, 75012 Paris, France
5Emergency Department, AP-HP, Hôpital Bichat-Claude Bernard, 75018 Paris, France
6Université Paris Diderot, Sorbonne Paris Cité, UA REMES, 75013 Paris, France
7Department of Intensive Care, AP-HP, Hôpital Saint Antoine, 75012 Paris, France

Received 18 August 2014; Accepted 9 November 2014; Published 1 December 2014

Academic Editor: Michael Blaivas

Copyright © 2014 M. Wargon et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objectives. Emergency departments (EDs) and elective hospitalizations compete for beds. Our aim was to reduce hospital transfers using a queuing-model study. Methods. Macros were created to simulate four priority groups of patients according to hospitalization mode (elective, ED) and age (≥75 and <75 years), with randomization of number of admissions and length of stay (LOS). Those priorities were assigned regarding usual situations (ED admission with less priority than scheduled admission) not regarding clinical contexts. Simulations were based on actual data from an academic hospital. Models simulated ED boarder queue according to different scenarios based on number of hospital beds, LOS, and preventable hospitalizations. Results. Observed hospital-LOS was longer for patients ≥75 years (12.2 ± 3.6 days versus 11.4 ± 3.8 days; ) and for ED admissions (12.2 ± 0.6 versus 9.7 ± 0.6 days; ). In simulation models, two scenarios stabilized the beds demand after admissions: limitation of LOS to 30 days or 20% decrease in elective admissions among older patients. With these scenarios, the queue would be 25.2 patients for 361 beds (+2%) and 16.7 patients for 354 beds. Conclusions. Queuing models offer an interesting approach to bed management. A significant reduction in ED transfers is feasible, by limiting LOS to <30 days or by reducing elective hospitalizations of patients by 20%.