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Cited by 69Emergency and Acute Care StudiesHealthcare Operations and Scheduling OptimizationAdvanced Queuing Theory Analysis

The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.

An Emergency Department Patient Flow Model Based on Queueing Theory Principles

Open access
Wiler, Jennifer L., Bolandifar, Ehsan, Griffey, Richard T., Poirier, Robert F., Olsen, Tava·2013·Academic Emergency Medicine
DOI 10.1111/acem.12215
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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.

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Cites

Research works printed but not linked: 5

  1. Stout, 2005, Proceedings of the 2005 Systems and Information Engineering Design Symposium; 2005
  2. Broyles, 2007, Estimating Business Loss to a Hospital Emergency Department from Patient Reneging by Queuing-Based Regression
  3. Roche, 2007, Improving patient safety by maximizing fast-track benefits in the emergency department-a queuing network approach
  4. Allon SD Deo S Lin W The impact of size and occupancy of hospitals on the extent of ambulance diversion: theory and evidence Soc Sci Res Network. Kellogg School of Management, Northwestern University http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1516843
  5. 2008, Sampling-Based Chi-Squared Test for Interval-Censored Data

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Metadata

Authors
Wiler, J. L., Bolandifar, E., Griffey, R. T., Poirier, R. F., & Olsen, T.
Affiliations
  • Washington University in St. Louis (US)
  • Chinese University of Hong Kong (HK)
  • University of Auckland (NZ)
Corresponding author
Wiler, Jennifer L.
ORCID
Venue
Academic Emergency Medicine
Publisher
Wiley
Published
2013-09
Volume
20
Issue
9
Pages
939-946
ISSN
1069-6563 (ISSN-L)
DOI
10.1111/acem.12215
OpenAlex
W2058575484
Semantic Scholar
b2313cd2d4b5f07afba07cc3d30b6aa51f0a2c93
Cited by
69
References
50
Citations by year
2026: 2 · 2025: 8 · 2024: 5 · 2023: 4 · 2022: 4 · 2021: 6 · 2020: 4 · 2019: 6 · 2018: 9 · 2017: 8 · 2016: 4 · 2015: 5 · 2014: 4
FWCI
4.27
Type
journal article
Language
en
Open access
Licence
Locations
  • Academic Emergency Medicine (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Healthcare Operations and Scheduling Optimization
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Queuing Theory Analysis
Keywords
emergency department overcrowding, patient flow, queueing theory, left without being seen, patient boarding, treatment duration, stationarity, emergency department capacity, treatment time reduction, triage
MeSH terms
Academic Medical Centers, Adult, Emergency Service, Hospital, Health Services Needs and Demand, Humans, Patient Admission, Retrospective Studies, Systems Theory, Time Factors, Triage, Waiting Lists

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