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Cited by 154Emergency and Acute Care StudiesHealthcare Operations and Scheduling OptimizationTrauma and Emergency Care Studies

Predicting Hospital Admissions at Emergency Department Triage Using Routine Administrative Data: PREDICTING HOSPITAL ADMISSION AT ED TRIAGE

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Sun, Yan, Heng, Bee Hoon, Tay, Seow Yian, Seow, Eillyne·2011·Academic Emergency Medicine
DOI 10.1111/j.1553-2712.2011.01125.x
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Abstract

OBJECTIVES: To be able to predict, at the time of triage, whether a need for hospital admission exists for emergency department (ED) patients may constitute useful information that could contribute to systemwide hospital changes designed to improve ED throughput. The objective of this study was to develop and validate a predictive model to assess whether a patient is likely to require inpatient admission at the time of ED triage, using routine hospital administrative data. METHODS: Data collected at the time of triage by nurses from patients who visited the ED in 2007 and 2008 were extracted from hospital administrative databases. Variables included were demographics (age, sex, and ethnic group), ED visit or hospital admission in the preceding 3 months, arrival mode, patient acuity category (PAC) of the ED visit, and coexisting chronic diseases (diabetes, hypertension, and dyslipidemia). Chi-square tests were used to study the association between the selected possible risk factors and the need for hospital admission. Logistic regression was applied to develop the prediction model. Data were split for derivation (60%) and validation (40%). Receiver operating characteristic curves and goodness-of-fit tests were applied to the validation data set to evaluate the model. RESULTS: Of 317,581 ED patient visits, 30.2% resulted in immediate hospital admission. In the developed predictive model, age, PAC status, and arrival mode were most predictive of the need for immediate hospital inpatient admission. The c-statistic of the receiver operating characteristic (ROC) curve was 0.849 (95% confidence interval [CI] = 0.847 to 0.851). The goodness-of-fit test showed that the predicted patients' admission risks fit the patients' actual admission status well. CONCLUSIONS: A model for predicting the risk of immediate hospital admission at triage for all-cause ED patients was developed and validated using routinely collected hospital data. Early prediction of the need for hospital admission at the time of triage may help identify patients deserving of early admission planning and resource allocation and thus potentially reduce ED overcrowding.

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  • The model's performance (AUROC 0.856) is state-of-the-art and comparable with recent admission models (AUROC 0.825-0.877).

    “This represents state-of-the-art performance comparable with recent admission models (e.g. AUROC 0.825–0.87722–25).”

    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 [22]

  • Prior models that incorporate past medical history utilize simplified chronic disease categories such as heart disease or diabetes while leaving out rich historical information accessible from the EHR such as outpatient medications and historical labs and vitals.

    “Prior models that incorporate past medical history utilize simplified chronic disease categories such as heart disease or diabetes [9,12] while leaving out rich historical information accessible from the electronic health record (EHR) such as outpatient medications and historical labs and vitals”

    From Predicting hospital admission at emergency department triage using machine learning (2018) · cites this paper as [9]

  • 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 [9]

  • 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 [9]

  • 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 [9]

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Research works printed but not linked: 1

  1. Dexheimer JW, Leegon J, Aronsky D. Predicting hospital admission at triage in emergency depart- ment patients. AMIA Annu Symp Proc. 2007; 11:937.

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Authors
Sun, Y., Heng, B. H., Tay, S. Y., & Seow, E.
Affiliations
Tan Tock Seng Hospital (SG)
Corresponding author
Sun, Yan
ORCID
Seow, Eillyne
Venue
Academic Emergency Medicine
Publisher
Wiley
Published
2011-08
Volume
18
Issue
8
Pages
844-850
ISSN
1069-6563 (ISSN-L)
DOI
10.1111/j.1553-2712.2011.01125.x
OpenAlex
W1887152358
Cited by
154
References
28
Citations by year
2026: 7 · 2025: 15 · 2024: 7 · 2023: 15 · 2022: 9 · 2021: 24 · 2020: 16 · 2019: 12 · 2018: 15 · 2017: 5 · 2016: 7 · 2015: 7 · 2014: 10 · 2013: 3 · 2012: 1
FWCI
5.55
Type
journal article
Language
en
Open access
Licence
doi.wiley.com/10.1002/tdm_license_1.1 (tdm, 1492 d)
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
hospital admission prediction, emergency department triage, emergency department throughput, age as risk factor, prediction model validation, emergency department overcrowding, resource allocation
MeSH terms
Adolescent, Adult, Aged, 80 and over, Chronic Disease, Decision Support Techniques, Emergency Service, Hospital, Female, Hospitalization, Humans, Male, Middle Aged, Retrospective Studies, Risk Factors, Singapore, Triage, Logistic Models, Databases, Factual, Risk Assessment, Young Adult

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