Developing and validating an artificial intelligence-based electronic triage model for predicting clinical outcomes among cardiac-suspected patients in the emergency department.

Abstract

Aims: Emergency department overcrowding, especially in cardiac units, delays care and raises mortality. Conventional triage is error-prone. We developed an AI-based model integrating routine data and automated ECGs to improve early risk classification.

Methods and results: This retrospective cross-sectional study involved 600 medical records of patients presenting with suspected cardiac symptoms. Model development was conducted in three phases: Designing a triage model using routine triage data, designing a triage model based on ECG images, and combining the ECG-based model and triage data. Model performance was evaluated regarding standard clinical outcomes within the first 24 h and compared against the Emergency Severity Index. The best-performing model based on triage data alone (i.e. multilayer perceptron neural network) yielded an accuracy of 89.42%, F-score of 84.51, and area under the curve between 0.815 and 0.858. The best-performing model based on ECG interpretation alone (i.e. convolutional neural network) yielded an accuracy of 93.83%, F-score of 91.08, and area under the curve ranging from 0.852 to 0.914. The fusion model demonstrated superior performance, with an accuracy of 97.22%, F-score of 94.60, and area under the curve between 0.881 and 0.938-significantly outperforming the conventional Emergency Severity Index. In the fusion model, the key predictive variables included ECG interpretation, heart rate, and mode of entry to emergency department.

Conclusion: Given its advantages over models using only routine data, ECG, or conventional triage, the fused AI-based triage model may effectively prioritize and predict cardiac emergency outcomes, providing a foundation for developing reliable, intelligent support systems in acute care.