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Cited by 52Emergency and Acute Care StudiesHealthcare Operations and Scheduling OptimizationMachine Learning in Healthcare

Progressive prediction of hospitalisation in the emergency department: uncovering hidden patterns to improve patient flow

Barak-Corren, Yuval, Israelit, Shlomo Hanan, Reis, Ben Y·2017·Emergency Medicine Journal
DOI 10.1136/emermed-2014-203819
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Abstract

INTRODUCTION: One of the factors contributing to ED crowding is the lengthy delay in transferring an admitted patient from the ED to an inpatient department (ie, boarding time). An earlier start of the admission process using an automatic hospitalisation prediction model could potentially shorten these delays and reduce crowding. METHODS: Clinical, operational and demographic data were retrospectively collected on 80 880 visits to the ED of Rambam Health Care Campus in Haifa, Israel, from January 2011 to January 2012. Using these data, a logistic regression model was developed to predict patient disposition (hospitalisation vs discharge) at three progressive time points throughout the ED visit: within the first 10 min, within an hour and within 2 hours. The algorithm was trained on 50% of the data (n=40 440) and tested on the remaining 50%. RESULTS: During the study time period, 58 197 visits ended in discharge and 22 683 in hospitalisation. Within 1 hour of presentation, our model was able to predict hospitalisation with a specificity of 90%, sensitivity of 94% and an AUCof 0.97. Early clinical decisions such as testing for calcium levels were found to be highly predictive of hospitalisations. In the Rambam ED, the use of such a prediction system would have the potential to save more than 250 patient hours per day. CONCLUSIONS: Data collected by EDs in electronic medical records can be used within a progressive modelling framework to predict patient flow and improve clinical operations. This approach relies on commonly available data and can be applied across different healthcare settings.

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Crossref · 10.1136/emermed-2014-203819

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  • 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 [20]

  • Some prior models predicting hospital admission at ED triage include additional features such as hospital usage statistics and past medical history.

    “while some models include additional features such as hospital usage statistics and past medical history [9,12,20,21].”

    From Predicting hospital admission at emergency department triage using machine learning (2018) · cites this paper as [20]

  • Many prior studies have been limited by technical factors, where continuous variables are often reduced to categorical variables through binning or to binary variables encoding presence or missing-ness of data due to the challenges of imputation.

    “Furthermore, many prior studies have been limited by technical factors, where continuous variables are often reduced to categorical variables through binning or to binary variables encoding presence or missing-ness of data due to the challenges of imputation [9,15,16,19–21].”

    From Predicting hospital admission at emergency department triage using machine learning (2018) · cites this paper as [20]

  • A progressive modeling approach that uses information available at later time-points has been able to achieve high predictive power and indicates the utility of these features.

    “Notably, a progressive modeling approach that uses information available at later time-points, such as lab tests ordered, medications given, and diagnoses entered by the ED provider during the patient’s current visit, has been able to achieve high predictive power and indicates the utility of these features [20,21].”

    From Predicting hospital admission at emergency department triage using machine learning (2018) · cites this paper as [20]

  • Barak-Corren et al. showed that the later datasets offered better predictions than at 10 minutes.

    “they were able to show that the later datasets offered better predictions than at 10 minutes.”

    From Machine learning for real-time aggregated prediction of hospital admission for emergency patients (2022) · cites this paper as [18]

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Authors
Barak-Corren, Y., Israelit, S. H., & Reis, B. Y.
Affiliations
  • Rambam Health Care Campus (IL)
  • Boston Children's Hospital (US)
ORCID
Reis, Ben Y
Venue
Emergency Medicine Journal
Publisher
BMJ
Published
2017-02-10
Volume
34
Issue
5
Pages
308-314
ISSN
  • 1351-0622 (ISSN-L)
  • 1472-0205
  • 1472-0213
DOI
10.1136/emermed-2014-203819
OpenAlex
W2587730730
Cited by
52
References
32
Citations by year
2026: 4 · 2025: 9 · 2024: 3 · 2023: 4 · 2022: 6 · 2021: 8 · 2020: 7 · 2019: 5 · 2018: 6
FWCI
6.26
Type
journal article
Language
en
Open access
closed
Locations
  • Emergency Medicine Journal (publishedVersion)
  • PubMed (publishedVersion)
  • HighWire Press Open Archive (submittedVersion)
Funders
U.S. National Library of Medicine
Topics
  • Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
  • Health Sciences › Health Professions › Emergency Medical Services › Healthcare Operations and Scheduling Optimization
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
emergency department overcrowding, patient boarding, patient flow, patient disposition, logistic regression, early admission, electronic health records, delayed hospital admission
MeSH terms
Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Crowding, Emergency Service, Hospital, Female, Hematologic Tests, Hospitalization, Humans, Infant, Israel, Length of Stay, Male, Middle Aged, Predictive Value of Tests, Retrospective Studies, Sensitivity and Specificity, Time Factors, Multivariate Analysis, Logistic Models

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