Reducing Emergency Diagnostic Uncertainty with TRACE: Triage and Risk Assessment via Cost Estimation

The authors aim to address the issue of diagnostic uncertainty in emergency medicine by developing a machine-learning framework called Triage and Risk Assessment via Cost Estimation (TRACE). This framework integrates expected-value calculations and patient similarity metrics to enhance triage accuracy and diagnostic predictions. The results demonstrate that TRACE significantly improves triage prediction accuracy and aligns closely with actual patient outcomes, suggesting its potential as a decision-support tool in clinical settings.