Decreasing Length of Stay in the Emergency Department With a Split Emergency Severity Index 3 Patient Flow Model
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OBJECTIVES: There has been a steady increase in emergency department (ED) patient volume and wait times. The desire to maintain or decrease costs while improving throughput requires novel approaches to patient flow. The break-out session "Interventions to Improve the Timeliness of Emergency Care" at the June 2011 Academic Emergency Medicine consensus conference "Interventions to Assure Quality in the Crowded Emergency Department" posed the challenge for more research of the split Emergency Severity Index (ESI) 3 patient flow model. A split ESI 3 patient flow model divides high-variability ESI 3 patients from low-variability ESI 3 patients. The study objective was to determine the effect of implementing a split ESI 3 flow model has on patient length of stay (LOS) for discharged patients. METHODS: This was a retrospective chart review at an urban academic ED seeing over 70,000 adult patients a year. Cases consisted of adults who presented from 9 a.m. to 11 p.m. from June 1, 2011, to December 31, 2011, and were discharged. Controls were patients who presented on the same times and days, but in 2010. Visit descriptors included age, race, sex, ESI score, and first diagnosis. The first diagnosis was coded based on methods used by the Agency for Healthcare Research and Quality to codify International Classification of Diseases, ninth version, into disease groups. Linear models compared log-transformed LOS for cases and controls. A front-end ED redesign involved creating guidelines to split ESI 3 patients into low and high variability, a hybrid sort/triage registered nurse, an intake area consisting of an internal results waiting room, and a treatment area for patients after initial assessment. The previous low-acuity area (ESI 4s and 5s) began to see low-variability ESI 3 patients as well. This was done without additional beds. The intake area was staffed with an attending emergency physician (EP), a physician assistant (PA), three nurses, two medical technicians, and a scribe. RESULTS: There was a 5.9% decrease, from 2.58 to 2.43 hours, in the geometric mean of LOS for discharged patients from 2010 to 2011 (95% confidence interval CI = 4.5% to 7.2%; 2010, n = 20,215; 2011, n = 20,653). Abdominal pain was the most common diagnostic grouping (2010, n = 2,484; 2011, n = 2,464) with a reduction in LOS of 12.9%, from 4.37 to 3.8 hours (95% CI = 10.3% to 15.3%). CONCLUSIONS: A split ESI 3 patient flow model improves door-to-discharge LOS in the ED.
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Models that predict patient outcomes, such as discharge or admission, seek to improve logistics in EDs and organise downstream resource allocation.
“Models which predict patient outcomes (e.g. whether a patient is likely to be discharged or admitted to hospital) seek to improve logistics in EDs and organise downstream resource allocation9,10.”
From Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19 (2021) · cites this paper as [9]
A patient's likelihood of admission may serve as a proxy for acuity, which is used in downstream decisions such as bed placement and the need for emergency intervention.
“From the perspective of patient care in the ED setting, a patient's likelihood of admission may serve as a proxy for acuity, which is used in a number of downstream decisions such as bed placement and the need for emergency intervention [12–14].”
From Predicting hospital admission at emergency department triage using machine learning (2018) · cites this paper as [13]
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- Authors
- Arya, R., Wei, G., McCoy, J. V., Crane, J., Ohman-Strickland, P., & Eisenstein, R. M.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Kaiser Permanente (US)
- Department of Biostatistics; UMDNJ/ Robert Wood Johnson Medical School; New Brunswick NJ
- Corresponding author
- Arya, Rajiv
- ORCID
- Wei, Grant
- Venue
- Academic Emergency Medicine
- Publisher
- Wiley
- Published
- 2013-11
- Volume
- 20
- Issue
- 11
- Pages
- 1171-1179
- ISSN
- 1069-6563 (ISSN-L)
- OpenAlex
- W2063725531
- Cited by
- 78
- References
- 24
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 7 · 2023: 5 · 2022: 13 · 2021: 6 · 2020: 8 · 2019: 6 · 2018: 8 · 2017: 3 · 2016: 5 · 2015: 8
- FWCI
- 4.27
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Academic Emergency Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
- Health Sciences › Health Professions › Emergency Medical Services › Healthcare Operations and Scheduling Optimization
- Health Sciences › Medicine › Emergency Medicine › Trauma and Emergency Care Studies
- Keywords
- emergency department patient flow, length of hospital stay, emergency department throughput, patient heterogeneity, emergency department redesign, triage, emergency physician staffing, nurse staffing, medical technicians, emergency care quality
- MeSH terms
- Adult, Aged, Crowding, Emergency Service, Hospital, Female, Hospitals, Urban, Humans, Length of Stay, Male, Medical Staff, Hospital, Middle Aged, Patient Discharge, Retrospective Studies, Severity of Illness Index, Triage, United States, Case-Control Studies, Models, Organizational, Efficiency, Organizational
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