Using Machine Learning Technique in Managing Emergency Triage Flow.

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.