Development of Predictive Model for Patient Triaging at Emergency Department

Emergency departments (EDs) are vital components of healthcare systems, providing immediate care for acute illnesses and injuries. Accurate identification and prioritization of patients who require urgent care is critical for early intervention and to manage limited hospital resources. Using the data from the Electronic Health Intelligence System database, we applied a machine learning algorithm augmented with a rule-based approach to develop a predictive model based on patients' conditions to triage patients into the appropriate care zones. The AI triage model shows promise in assisting nurses with effective triaging of patients, potentially contributing to improved patient care and resource allocation in emergency settings.

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Publication Details

Published
2025-05-05
DOI
https://doi.org/10.1109/cai64502.2025.00103
Citations
1
Primary Topic
Emergency and Acute Care Studies
Type
article
Field-Weighted Citation Impact
2.25
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Development of Predictive Model for Patient Triaging at Emergency Department

Zhiyong Fu, Kristacia Kang, Annitha Annathurai, Yong Khiang Leow et al.
1 citations
Emergency and Acute Care Studies
2.25
article

Development of Predictive Model for Patient Triaging at Emergency Department

Zhiyong Fu, Kristacia Kang, Annitha Annathurai, Yong Khiang Leow, Emileen Chia
article en
1 citations

Abstract

Emergency departments (EDs) are vital components of healthcare systems, providing immediate care for acute illnesses and injuries. Accurate identification and prioritization of patients who require urgent care is critical for early intervention and to manage limited hospital resources. Using the data from the Electronic Health Intelligence System database, we applied a machine learning algorithm augmented with a rule-based approach to develop a predictive model based on patients' conditions to triage patients into the appropriate care zones. The AI triage model shows promise in assisting nurses with effective triaging of patients, potentially contributing to improved patient care and resource allocation in emergency settings.

Sengkang General Hospital (SG)
Openalex Percentile: Top 13%
Emergency and Acute Care Studies
2.25
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