Abstract / Summary
Abstract Purpose: Emergency department (ED) overcrowding is a significant challenge worldwide. Triage systems categorize patients according to the urgency of their clinical conditions to ensure patient safety and minimize the risk of clinical errors. The aim of this study was to evaluate the utility of decision trees, an artificial intelligence-based model, in emergency medicine triage.Materials and methods: This prospective, cross-sectional study was conducted in the ED of a tertiary care hospital. A total of 1,999 patients aged >17 years were included. Prior to the study, triage staff received training on the Australasian Triage Scale (ATS). Two independent observers assessed the final triage category of each patient. A new triage algorithm was developed using a decision tree model.Results: Mean age of the study population was 41.1±17.2 years, and 49.1% (n=981) were male. A total of 867 patients (43.3%) were assigned to triage category 5, whereas 14 patients (0.7%) were assigned to category 1. The most common clinical descriptors were minimal pain with no high-risk features (n=409, 20.5%) and minor symptoms of low-risk conditions (n=362, 18.1%). There was excellent agreement between the independent observers (κ=0.997). The decision tree algorithm incorrectly classified only one patient, yielding an accuracy rate of 99.9%. Agreement between the ATS and the decision tree algorithm was also excellent (κ=0.999). Agreement between physicians and paramedics was moderate (κ=0.541).Conclusion: Decision tree models may be used to develop practical triage algorithms as decision-support tools in ED triage. Artificial intelligence-based decision trees have the potential to support triage practices.