ML Triage Algorithm Optimizing Hospital Transport for Mass Casualties
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Solution Overview
Problem
Current mass casualty triage systems face limitations in accurately predicting victim deterioration and optimizing transport decisions due to subjective assessments, inadequate validation, and insufficient distribution of patients across severity levels, leading to suboptimal triage outcomes.
Innovation Solution
A system utilizing a trained machine learning algorithm that generates triage decisions based on predicted survival probabilities and hospital capacity, recommending optimal hospital transport for each victim to maximize survival, incorporating location, medical, and transport information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional triage systems (START, SALT, ESI) are used to categorize patients, then triage decisions can be made quickly and simply, but the measurement precision of survival prediction is weak to modest and almost half of ED patients are categorized as yellow
Solution Approach 1:
The patent replaces traditional mechanical triage systems (START, SALT, ESI) with a machine learning-based triage algorithm that uses electronic data processing and computational models to predict patient outcomes. The system substitutes human subjective assessment with automated algorithms that analyze multiple patient variables to generate survival predictions, thereby improving measurement precision while maintaining operational efficiency through computerized decision support.
2Measurement precision
If machine learning algorithms are used to predict patient outcomes, then survival prediction accuracy is improved, but the device complexity increases and requires more computational resources
Solution Approach 1:
The patent develops a universal triage algorithm that can be applied across multiple hospitals and mass casualty incidents. The machine learning model is designed to handle various patient types, injury scenarios, and hospital capacities through a single standardized system. This multi-functional approach allows the complex algorithm to serve diverse purposes (triage decision-making, resource allocation, outcome prediction) while being deployed as a unified platform across different healthcare facilities.
Solution Approach 2:
The patent introduces an intermediary computational layer that bridges complex machine learning algorithms and simple triage decisions. The system uses intermediate representations such as predicted survival probabilities, expected survivors metrics, and standardized output formats that translate complex algorithmic outputs into actionable triage recommendations. This intermediary layer simplifies the interface between the complex algorithm and end-users while maintaining prediction accuracy.
3Ease of operation
If triage decisions are made based on current severity levels only, then the ease of operation is maintained, but the reliability of triage decisions deteriorates because different victims can have different rates of deterioration and severity status changes significantly during wait time
Solution Approach 1:
The patent applies preliminary action by predicting future patient outcomes before actual deterioration occurs. The machine learning algorithm estimates survival probabilities and expected survivors at different time points (e.g., 1 hour, 2 hours, 4 hours) based on current patient characteristics and historical data. This allows triage decisions to account for anticipated deterioration patterns, enabling providers to prioritize patients who are likely to worsen during transport and wait time, rather than relying solely on current severity assessments.
4Ease of operation
If optimal transport decisions are made without considering hospital capacity and transport times, then the ease of operation is improved, but the productivity of the triage system deteriorates because it cannot maximize the expected number of survival victims under constraints
Solution Approach 1:
The patent implements dynamic triage decision-making that adapts to changing hospital capacities and transport conditions. The system dynamically calculates optimal patient-hospital assignments by considering real-time constraints such as hospital bed availability, surgical capacity, transport times, and patient acuity levels. The algorithm continuously re-optimizes assignments as conditions change, allowing the system to maximize expected survivors under varying constraints while providing clear, actionable recommendations to triage providers.
Data Source
AI summary
A method for performing, using a victim triage system, triage analysis of victims of an incident, comprising: (i) receiving a location of the incident, medical information, hospital capability information for hospitals in a predetermined vicinity of the location, and transport information relative to the location; (ii) determining, by a trained triage machine learning algorithm using the received information, a triage decision for the victims, wherein the triage decision for a victim comprises: (1) a probability of the victim's survival over time; (2) a recommendation to transport or not transport the victim to a hospital; and (3) to which of the two or more hospitals the victim should be transported; (iii) generating (140) a triage report comprising the determined triage decision for each of the plurality of victims; and (iv) displaying the triage report on a user display of the victim triage system.