Demonstrating Tactical Combat Casualty Care in Simulated Environments to Enable Passive, Autonomous Documentation:...

Area of Science:

• Military Medicine
• Biomedical Engineering
• Data Science

Background:

• The Telemedicine & Advanced Technology Research Center (TATRC) initiated research in Autonomous Casualty Care (AC2) in 2023.
• A key challenge in operational settings is the accurate capture of tactical combat casualty care (TCCC) data.

Purpose of the Study:

• To address TCCC data capture challenges, TATRC's Passive Data Collection using Autonomous Documentation project aims to automate combat care documentation.
• The project will create a data repository using passive sensor inputs from simulated casualty care scenarios.

Main Methods:

• Six randomized, simulated TCCC scenarios were conducted under an IRB-approved protocol.
• Participants included care providers and consenting live simulated patients (human actors) and mannikins (low and high fidelity).
• Sensors passively collected data on care delivery actions and patient physiology across multiple research sites.

Main Results:

• Approximately 2500 simulation procedures tasks will be collected and annotated by March 2025.
• The collected data will be used to develop machine learning and artificial intelligence algorithms for accurate Department of Defense (DD) Form 1380 population.
• Ongoing data collection beyond March 2025 will refine these algorithms.

Conclusions:

• The military health care system (MHS) lacks real-world TCCC datasets at the point of injury.
• Automating TCCC care documentation through a data repository is crucial for alleviating provider cognitive burden in austere environments.
• This research aims to advance passive, automated medical documentation across the healthcare continuum using AI and machine learning.