Integrating Wearable Sensor Data with an AI-based Protocol-Flexible Triage Platform could improve outcomes in Combat Casualty Care
AI and machine learning (ML) can be used to improve medical triage during the "golden hour" of combat casualty care.
Traditional methods of assessing a patient's condition are often impractical in prehospital and combat environments. The integration of wearable sensor technology offers a solution by continuously monitoring key physiological parameters, including heart rate, respiratory rate, and blood oxygen saturation (SpO2).
A New Triage System
The core of the proposed system is an AI-based trauma severity scoring system. This system analyses the data from wearable sensors to quantify an individual's health status. Based on the calculated score, the system assigns a tri-colour triage code:
Green: Low-priority casualty
Orange: Delayable casualty
Red: Urgent casualty
This automated triage process provides an objective, real-time assessment that can help combat medics and first responders make faster and more accurate decisions, improving resource allocation and patient outcomes.
Challenges and Future Implications
While promising, the article also acknowledges challenges, including poor data quality from environmental interference and a lack of interoperability standards. However, it concludes that with further design improvements and seamless integration with existing medical records, these AI/ML-based triage models have the potential to significantly enhance medical care in challenging prehospital settings.
Author
John Galatas
Editor-in-Chief CTM-E
About CTM-E
CTM-E is a research organisation investigating the medical challenges posed by mass casualty incidents in Europe and developing solutions to public policy issues to help make communities safer and more secure.
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