Machine Learning Trauma Triage for NEI-6 Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current trauma triage systems, particularly those relying on the Injury Severity Score (ISS), are inadequate in accurately identifying the need for full trauma-team activation, leading to high rates of undertriage and overtriage, which result in preventable mortality and inefficient resource allocation.
Innovation Solution
A computer-implemented system using machine-learning, artificial-intelligence, or deep-learning models to predict a 'Need for Emergent Intervention within 6 hours' (NEI-6) designation based on prehospital metrics, allowing for more accurate and efficient triage of trauma patients by first responders, incorporating a combination of physiologic, anatomic, and mechanistic criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional Injury Severity Score (ISS) based triage systems are used, then the system is simple to operate, but the measurement precision of trauma triage classification is insufficient leading to high rates of undertriage and overtriage
Solution Approach 1:
The system transforms the traditional ISS-based single-parameter triage approach into a multi-parameter machine learning model that incorporates demographic, physiologic, anatomic, and mechanistic criteria. This parameter expansion enables more precise NEI-6 classification while managing complexity through automated computational processing.
Solution Approach 2:
The patent replaces the manual mechanical calculation and interpretation of ISS scores with an automated machine learning system that processes multiple parameters simultaneously. This substitution eliminates human error in triage classification while providing consistent, data-driven NEI-6 designations through algorithmic processing.
2Loss of time
If traditional triage systems are used, then the device complexity is low, but the loss of time in identifying patients needing emergent intervention is excessive
Solution Approach 1:
The system performs preliminary classification of trauma patients into NEI-6 Positive and NEI-6 Negative categories during prehospital care, before hospital arrival. This advance triage designation allows receiving facilities to prepare appropriate resources in advance, eliminating delays in identifying patients needing emergent intervention upon arrival.
Solution Approach 2:
The machine learning model automatically processes input parameters and generates NEI-6 designations without requiring manual clinical judgment for each calculation. The system serves itself by autonomously classifying patients based on input data, freeing clinicians from time-consuming manual triage assessments while maintaining high accuracy.
3Reliability
If traditional triage systems are used, then the ease of operation is high, but the reliability of trauma team activation decisions is insufficient
Solution Approach 1:
The system provides structured feedback to clinicians through clear NEI-6 Positive/Negative designations that directly inform trauma team activation decisions. This feedback mechanism translates complex multi-parameter analysis into actionable clinical guidance, improving decision reliability while maintaining ease of use through binary classification outputs.
Solution Approach 2:
The machine learning model acts as an intermediary between raw patient data and clinical decision-making. It processes demographic, physiologic, anatomic, and mechanistic criteria through automated algorithms, serving as a mediator that translates complex data into reliable NEI-6 designations that guide trauma team activation without requiring clinicians to manually evaluate all parameters.
Data Source
AI summary
A computer-based, trauma-patient-triage system includes one or more computing devices configured to: receive, from a mobile computing device, user input comprising a plurality of parameters indicating a condition of a trauma patient; apply the plurality of parameters to one or more machine-learning algorithms trained to determine, based on the plurality of parameters, a trauma-triage category for the patient, wherein the trauma-triage category for the patient indicates an NEI-6 designation for the patient; and transmit the trauma-triage category to the mobile computing device for display on the mobile computing device.