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Reducing Emergency Diagnostic Uncertainty with TRACE: Triage and Risk Assessment via Cost Estimation.
Kian D Samadian1, Paul Chong2, Boyu Peng3
1Harvard Medical School, Massachusetts General Hospital, Department of Emergency Medicine, Boston, Massachusetts.
The Western Journal of Emergency Medicine
|April 29, 2026
Summary
Triage and Risk Assessment via Cost Estimation (TRACE) uses machine learning to improve emergency medicine accuracy by assessing clinical harm and patient similarity. This framework enhances diagnostic prediction and reduces uncertainty for better patient outcomes.
Area of Science:
- Machine Learning in Healthcare
- Emergency Medicine Decision Support
- Clinical Risk Assessment
Background:
- Diagnostic uncertainty in emergency medicine compromises patient safety, leading to missed diagnoses and harm.
- Existing predictive models focus on diagnostic likelihood, neglecting the critical aspect of potential clinical harm from errors.
- There is a need for advanced frameworks that integrate both diagnostic accuracy and risk assessment.
Purpose of the Study:
- To introduce the Triage and Risk Assessment via Cost Estimation (TRACE) machine-learning framework.
- To incorporate expected-value calculations (probability-weighted clinical harm) and patient similarity metrics.
- To enhance both diagnostic accuracy and risk assessment in emergency care settings.
Main Methods:
- Developed TRACE using the MIMIC-IV-ED dataset, featuring two modules: TRACE-T (expected value-powered triage index) and TRACE-Dx (patient similarity diagnosis engine).
- TRACE-T utilizes vital signs and chief complaints to calculate expected patient acuity.
- TRACE-Dx predicts diagnoses by identifying similar patients and weighting outcomes by clinical harm, evaluated via string matching and sentence embedding similarity.
Main Results:
- TRACE-T, particularly with a random forest classifier, significantly improved triage prediction accuracy (0.605 to 0.705, P = .04) and reduced root mean square error (0.635 to 0.541, P < .001).
- TRACE-Dx achieved high average similarities with actual outcomes: 93.3% for sentence embedding and 92.5% for string matching.
- The analysis included 2,501 patients, with TRACE-Dx generating 12,505 expected value-ranked diagnoses.
Conclusions:
- Integrating expected value-based clinical harm modeling and patient similarity scoring improves triage accuracy and diagnostic prediction in emergency care.
- The TRACE framework offers interpretable, actionable insights for real-time clinical decision support.
- TRACE has the potential to reduce diagnostic uncertainty and enhance patient outcomes in emergency medicine.
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