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ABSTRACT
Background: Triage is a critical component of Emergency department care. Erroneous patient classification and mis-triaging are common in present triage systems worldwide. Therefore, several institutes worldwide have developed artificial intelligence-based algorithms that use machine learning approaches to sort and triage patients effectively. Objective: This study aims were to propose a machine learning model to predict the triage level for emergency medicine department patients and compare its performance to the standard nursing triage system. Methods: This retrospective pilot study collected the dataset of emergency department records from King Fahad Hospital of the University in khobar, between January 1, 2020, and December 31, 2022. A sample of 998 randomly selected patients was included in this cohort. The machine learning model was trained using 10-fold cross-validation. Two experiments were conducted, including five triage levels, and the second combing triage levels 2, 3, 4, and 5. Results: The machine learning model achieved an accuracy of 84% in experiment 1 and 64% in experiment 2. The mis-triage rates of the machine learning model were significantly lower than those of the standard nursing triage system. Conclusion: The machine learning model achieved higher accuracy and lower mis-triage rates than the standard nursing triage system. Thus, the proposed machine learning model can be a helpful tool for emergency department triage, enabling more efficient and accurate patient management.
Keywords Canadian Triage and Acuity Scale Machine Learning, Emergency Department Mis-triage, Random Forest.
1. BACKGROUND
Triage in emergency departments (ED) is vital to efficiently and effectively managing contemporary E.Ds. Overcrowding in emergency departments is a widespread issue that causes delays in medical care. Identifying acutely unwell patients is a crucial aspect of ED triage to provide timely care to those who require it. Unfortunately, erroneous patient classification and mis-triaging are commonplace in the present worldwide triage systems.
Triage scales were developed to categorize patients according to their health status and need for care, considering their subjective complaints, vital signs, and the nurse's clinical judgment. The Ipswich Triage Scale (ITS) was a five-category scale used for the first time 30 years ago. This method was validated and used by other scales; and served as the foundation for the Canadian Triage and Acuity Scale (CTAS) and the Manchester Triage Scale (MTS). (1) The purpose of triage...