CJEM
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This study investigates the potential to improve emergency department (ED) triage using machine learning models by comparing their predictive performance with the Canadian Triage Acuity Scale (CTAS) in identifying the need for critical care within 12 h of ED arrival. ⋯ Machine learning models outperformed CTAS in identifying, at the point of ED triage, patients likely to need early critical care. If validated in future studies, machine learning models such as the ones developed here may be considered for incorporation in future revisions of the CTAS triage algorithm, potentially improving discrimination and reliability.
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ECGs performed at ED triage are mandatorily assessed by an emergency physician contributing to task interruptions, decreased quality of care and increased error risk. Recent literature suggests that a triage ECG interpreted as normal by the ECG machine software correlates with benign interpretation from attending cardiologists. Ambiguity persists regarding the safety of the normal computerized ECG interpretation and whether real-time physician review is needed. ⋯ A normal ECG interpretation from the GE Marquette 12SL ECG software at ED triage has a very high accuracy and a very low probability of clinically relevant change in patient outcome and ED trajectory.