CRS-Triage: Confidence- and Reliability-Aware Selective Triage under Incomplete Clinical Evidence

2026-08-04

AI Summary

This study addresses the challenges posed by missing, unreliable, and multimodally inconsistent data in electronic health records (EHRs) that hinder accurate triage prediction in emergency settings. The authors propose a confidence- and reliability-aware selective triage method that jointly models the reliability of structured data and clinical notes along with their cross-modal consistency to generate actionable prediction confidence scores. Integrating cost-sensitive learning with a safety-oriented misdiagnosis penalty mechanism, the approach slightly overestimates acuity to reduce under-triage risk and dynamically decides between automated triage or human review based on a confidence threshold. Evaluated on the MIMIC-IV-ED dataset, the method significantly improves the trade-off between risk and coverage, demonstrating robust and highly reliable performance even under conditions of data incompleteness or modality conflict.

Abstract

Emergency triage requires reliable decisions within a short time period. However, the available electronic health record (EHR) data, including structured data and clinical text, are often incomplete, unreliable, and inconsistent. This makes machine learning (ML)-based triage prediction more challenging, as existing ML models typically rely on complete and reliable EHR data to accurately predict patients' acuity levels. To address this, we propose confidence- and reliability-aware selective triage (CRS-Triage) to predict patients' acuity levels with a confidence score. By comparing the confidence score with a predefined threshold, CRS-Triage can selectively determine whether the model should make the decision or defer the case. Specifically, CRS-Triage separately evaluates the reliability of structured data and clinical text and then jointly considers the consistency between the two modalities to estimate the confidence of each prediction. Moreover, to reduce the risk of missing high-acuity patients, namely under-triage, CRS-Triage prefers to assign patients slightly higher acuity levels, namely over-triage, by penalizing under-triage errors. Experiments on the MIMIC-IV-ED dataset show that CRS-Triage achieves strong predictive performance. It also provides a better risk-coverage trade-off and remains reliable when the available EHR data are incomplete, degraded, or inconsistent across modalities.

Problem

• emergency triage
• incomplete EHR data
• unreliable clinical evidence
• acuity prediction
• clinical decision-making

Innovation

• selective triage
• confidence-aware prediction
• multimodal reliability
• under-triage mitigation
• incomplete EHR