An emergency department patient flow model based on queueing theory principles

56Citations
Citations of this article
142Readers
Mendeley users who have this article in their library.

Abstract

Objectives The objective was to derive and validate a novel queuing theory-based model that predicts the effect of various patient crowding scenarios on patient left without being seen (LWBS) rates. Methods Retrospective data were collected from all patient presentations to triage at an urban, academic, adult-only emergency department (ED) with 87,705 visits in calendar year 2008. Data from specific time windows during the day were divided into derivation and validation sets based on odd or even days. Patient records with incomplete time data were excluded. With an established call center queueing model, input variables were modified to adapt this model to the ED setting, while satisfying the underlying assumptions of queueing theory. The primary aim was the derivation and validation of an ED flow model. Chi-square and Student's t-tests were used for model derivation and validation. The secondary aim was estimating the effect of varying ED patient arrival and boarding scenarios on LWBS rates using this model. Results The assumption of stationarity of the model was validated for three time periods (peak arrival rate = 10:00 a.m. to 12:00 p.m.; a moderate arrival rate = 8:00 a.m. to 10:00 a.m.; and lowest arrival rate = 4:00 a.m. to 6:00 a.m.) and for different days of the week and month. Between 10:00 a.m. and 12:00 p.m., defined as the primary study period representing peak arrivals, 3.9% (n = 4,038) of patients LWBS. Using the derived model, the predicted LWBS rate was 4%. LWBS rates increased as the rate of ED patient arrivals, treatment times, and ED boarding times increased. A 10% increase in hourly ED patient arrivals from the observed average arrival rate increased the predicted LWBS rate to 10.8%; a 10% decrease in hourly ED patient arrivals from the observed average arrival rate predicted a 1.6% LWBS rate. A 30-minute decrease in treatment time from the observed average treatment time predicted a 1.4% LWBS. A 1% increase in patient arrivals has the same effect on LWBS rates as a 1% increase in treatment time. Reducing boarding times by 10% is expected to reduce LWBS rates by approximately 0.8%. Conclusions This novel queuing theory-based model predicts the effect of patient arrivals, treatment time, and ED boarding on the rate of patients who LWBS at one institution. More studies are needed to validate this model across other institutions. © 2013 by the Society for Academic Emergency Medicine.

References Powered by Scopus

Systematic Review of Emergency Department Crowding: Causes, Effects, and Solutions

1162Citations
N/AReaders
Get full text

Statistical analysis of a telephone call center: A queueing-science perspective

551Citations
N/AReaders
Get full text

Emergency Department Crowding, Part 1-Concept, Causes, and Moral Consequences

367Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Operations research/management contributions to emergency department patient flow optimization: Review and research prospects

113Citations
N/AReaders
Get full text

Outcomes of Crowding in Emergency Departments; a Systematic Review

78Citations
N/AReaders
Get full text

Emergency department resource optimisation for improved performance: a review

57Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Wiler, J. L., Bolandifar, E., Griffey, R. T., Poirier, R. F., & Olsen, T. (2013). An emergency department patient flow model based on queueing theory principles. Academic Emergency Medicine, 20(9), 939–946. https://doi.org/10.1111/acem.12215

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 53

63%

Researcher 18

21%

Professor / Associate Prof. 8

10%

Lecturer / Post doc 5

6%

Readers' Discipline

Tooltip

Medicine and Dentistry 32

42%

Engineering 21

28%

Business, Management and Accounting 12

16%

Nursing and Health Professions 11

14%

Save time finding and organizing research with Mendeley

Sign up for free