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Cited by 56Emergency and Acute Care StudiesMachine Learning in HealthcareHealthcare Operations and Scheduling Optimization

Early Prediction Model of Patient Hospitalization From the Pediatric Emergency Department

Barak-Corren, Yuval, Fine, Andrew M., Reis, Ben Y.·2017·PEDIATRICS
DOI 10.1542/peds.2016-2785
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Abstract

BACKGROUND AND OBJECTIVES: Emergency departments (EDs) in the United States are overcrowded and nearing a breaking point. Alongside ever-increasing demand, one of the leading causes of ED overcrowding is the boarding of hospitalized patients in the ED as they await bed placement. We sought to develop a model for early prediction of hospitalizations, thus enabling an earlier start for the placement process and shorter boarding times. METHODS: We conducted a retrospective cohort analysis of all visits to the Boston Children's Hospital ED from July 1, 2014 to June 30, 2015. We used 50% of the data for model derivation and the remaining 50% for validation. We built the predictive model by using a mixed method approach, running a logistic regression model on results generated by a naive Bayes classifier. We performed sensitivity analyses to evaluate the impact of the model on overall resource utilization. RESULTS: Our analysis comprised 59 033 patient visits, of which 11 975 were hospitalized (cases) and 47 058 were discharged (controls). Using data available within the first 30 minutes from presentation, our model identified 73.4% of the hospitalizations with 90% specificity and 35.4% of hospitalizations with 99.5% specificity (area under the curve = 0.91). Applying this model in a real-time setting could potentially save the ED 5917 hours per year or 30 minutes per hospitalization. CONCLUSIONS: This approach can accurately predict patient hospitalization early in the ED encounter by using data commonly available in most electronic medical records. Such early identification can be used to advance patient placement processes and shorten ED boarding times.

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Crossref · 10.1542/peds.2016-2785

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  • 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 [21]

  • 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 [21]

  • 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 [21]

  • 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 [21]

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Authors
Barak-Corren, Y., Fine, A. M., & Reis, B. Y.
Affiliations
Boston Children's Hospital (US)
ORCID
Reis, Ben Y.
Venue
PEDIATRICS
Publisher
American Academy of Pediatrics (AAP)
Published
2017-04-25
Volume
139
Issue
5
Pages
e20162785
ISSN
  • 0031-4005 (ISSN-L)
  • 1098-4275
DOI
10.1542/peds.2016-2785
OpenAlex
W2609161091
Cited by
56
References
22
Citations by year
2026: 5 · 2025: 7 · 2024: 7 · 2023: 4 · 2022: 8 · 2021: 11 · 2020: 3 · 2019: 7 · 2018: 4
FWCI
4.92
Type
journal article
Language
en
Open access
closed
Locations
  • PEDIATRICS (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Health Professions › Emergency Medical Services › Healthcare Operations and Scheduling Optimization
Keywords
pediatric emergency department, emergency department overcrowding, patient boarding, hospitalization prediction, early prediction model, resource utilization (health care), Naive Bayes classifier, logistic regression, electronic health records
MeSH terms
Bayes Theorem, Boston, Emergency Service, Hospital, Female, Hospitalization, Hospitals, Pediatric, Humans, Length of Stay, Male, Models, Theoretical, Patient Admission, Retrospective Studies, Sensitivity and Specificity, United States, Cohort Studies

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