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Cited by 138Emergency and Acute Care StudiesHealthcare Operations and Scheduling OptimizationArtificial Intelligence in Healthcare

Predicting emergency department inpatient admissions to improve same-day patient flow

Open access
Peck, Jordan S., Benneyan, James C., Nightingale, Deborah J., Gaehde, Stephan A.·2012·Academic Emergency Medicine
DOI 10.1111/j.1553-2712.2012.01435.x
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Abstract

OBJECTIVES: The objectives were to evaluate three models that use information gathered during triage to predict, in real time, the number of emergency department (ED) patients who subsequently will be admitted to a hospital inpatient unit (IU) and to introduce a new methodology for implementing these predictions in the hospital setting. METHODS: Three simple methods were compared for predicting hospital admission at ED triage: expert opinion, naïve Bayes conditional probability, and a generalized linear regression model with a logit link function (logit-linear). Two months of data were gathered from the Boston VA Healthcare System's 13-bed ED, which receives approximately 1,100 patients per month. Triage nurses were asked to estimate the likelihood that each of 767 triaged patients from that 2-month period would be admitted after their ED treatment, by placing them into one of six categories ranging from low to high likelihood. Logit-linear regression and naïve Bayes models also were developed using retrospective data and used to estimate admission probabilities for each patient who entered the ED within a 2-month time frame, during triage hours (1,160 patients). Predictors considered included patient age, primary complaint, provider, designation (ED or fast track), arrival mode, and urgency level (emergency severity index assigned at triage). RESULTS: Of the three methods considered, logit-linear regression performed the best in predicting total bed need, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.887, an R(2) of 0.58, an average estimation error of 0.19 beds per day, and on average roughly 3.5 hours before peak demand occurred. Significant predictors were patient age, primary complaint, bed type designation, and arrival mode (p < 0.0001 for all factors). The naïve Bayesian model had similar positive predictive value, with an AUC of 0.841 and an R(2) of 0.58, but with average difference in total bed need of approximately 2.08 per day. Triage nurse expert opinion also had some predictive capability, with an R(2) of 0.52 and an average difference in total bed need of 1.87 per day. CONCLUSIONS: Simple probability models can reasonably predict ED-to-IU patient volumes based on basic data gathered at triage. This predictive information could be used for improved real-time bed management, patient flow, and discharge processes. Both statistical models were reasonably accurate, using only a minimal number of readily available independent variables.

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  • Most prior models predicting hospital admission at ED triage include only information collected at triage such as demographics, vital signs, chief complaint, nursing notes, and early diagnostics.

    “Most models only include information collected at triage such as demographics, vital signs, chief complaint, nursing notes, and early diagnostics [11,14–19]”

    From Predicting hospital admission at emergency department triage using machine learning (2018) · cites this paper as [11]

  • Logistic regression and Naive Bayes are commonly used, with few studies using more complex algorithms like random-forests, artificial neural networks, and support vector machines.

    “Logistic regression and Naive Bayes are commonly used [9,11,16,18–22], with few studies using more complex algorithms like random-forests, artificial neural networks, and support vector machines [12,15,17].”

    From Predicting hospital admission at emergency department triage using machine learning (2018) · cites this paper as [11]

  • Notification of administrators and inpatient teams regarding potential admissions may help alleviate ED crowding.

    “Notification of administrators and inpatient teams regarding potential admissions may help alleviate this problem [11].”

    From Predicting hospital admission at emergency department triage using machine learning (2018) · cites this paper as [11]

  • One study in emergency medicine derived an expected number of admissions among a roomful of patients in ED by summing their individual probabilities of admission, but there was no presentation of the uncertainty of their point estimates.

    “One study in emergency medicine derived an expected number of admissions among a roomful of patients in ED by summing their individual probabilities of admission27, but there was no presentation of the uncertainty of their point estimates.”

    From Machine learning for real-time aggregated prediction of hospital admission for emergency patients (2022) · cites this paper as [27]

  • Peck et al.'s proposed summation of probabilities to calculate running bed need does not quantify uncertainty, and England et al.'s simulations do not benefit from real-time data.

    “Peck et al.’s27 proposed summation of probabilities to calculate running bed need does not quantify uncertainty, and England et al.’s32 simulations do not benefit from real-time data.”

    From Machine learning for real-time aggregated prediction of hospital admission for emergency patients (2022) · cites this paper as [27]

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Research works printed but not linked: 1

  1. Sun Y, Heng BH, Tay SY, Seow E. Predicting hospi- tal admissions at emergency department triage using routine administrative data. Acad Emerg Med. 2011; 18:844-50.

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Authors
Peck, J. S., Benneyan, J. C., Nightingale, D. J., & Gaehde, S. A.
Affiliations
Northeastern University (US)
Corresponding author
Peck, Jordan S.
ORCID
Venue
Academic Emergency Medicine
Publisher
Wiley
Published
2012-09
Volume
19
Issue
9
Pages
E1045-54
ISSN
  • 1069-6563 (ISSN-L)
  • 1553-2712
DOI
10.1111/j.1553-2712.2012.01435.x
OpenAlex
W2146767951
Cited by
138
References
29
Citations by year
2025: 10 · 2024: 6 · 2023: 5 · 2022: 14 · 2021: 13 · 2020: 20 · 2019: 12 · 2018: 23 · 2017: 10 · 2016: 6 · 2015: 8 · 2014: 10 · 2013: 1
FWCI
6.76
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor, 13 d)
Locations
  • Academic Emergency Medicine (publishedVersion)
  • PubMed (publishedVersion)
Funders
U.S. Department of Veterans Affairs
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 › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
emergency department admission, hospital bed management, triage, admission prediction, naive Bayes, expert opinion, Emergency Severity Index, fast tracking
MeSH terms
Adolescent, Adult, Aged, Aged, 80 and over, Bayes Theorem, Boston, Child, Emergency Medicine, Emergency Service, Hospital, Female, Hospitalization, Humans, Inpatients, Length of Stay, Male, Middle Aged, Patient Admission, Patient Transfer, Predictive Value of Tests, Triage, Waiting Lists, Linear Models, Total Quality Management, Young Adult

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