Loading...
Thumbnail Image
Publication

A Queue-Based Monte Carlo Analysis to Support Decision Making for Implementation of an Emergency Department Fast Track

Fitzgerald, Kristin
Pelletier, Lori R.
Reznek, Martin A
Embargo Expiration Date
Link to Full Text
Abstract

Emergency departments (EDs) are seeking ways to utilize existing resources more efficiently as they face rising numbers of patient visits. This study explored the impact on patient wait times and nursing resource demand from the addition of a fast track, or separate unit for low-acuity patients, in the ED using a queue-based Monte Carlo simulation in MATLAB. The model integrated principles of queueing theory and expanded the discrete event simulation to account for time-based arrival rates. Additionally, the ED occupancy and nursing resource demand were modeled and analyzed using the Emergency Severity Index (ESI) levels of patients, rather than the number of beds in the department. Simulation results indicated that the addition of a separate fast track with an additional nurse reduced overall median wait times by 35.8 +/- 2.2 percent and reduced average nursing resource demand in the main ED during hours of operation. This novel modeling approach may be easily disseminated and informs hospital decision-makers of the impact of implementing a fast track or similar system on both patient wait times and acuity-based nursing resource demand.

Source

J Healthc Eng. 2017;2017:6536523. doi: 10.1155/2017/6536523. Epub 2017 Mar 28. Link to article on publisher's site

Year of Medical School at Time of Visit
Sponsors
Dates of Travel
DOI
10.1155/2017/6536523
PubMed ID
29065634
Other Identifiers
Notes
Funding and Acknowledgements
Corresponding Author
Related Resources
Related Resources
Repository Citation
Rights
Copyright © 2017 Kristin Fitzgerald 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.