08/09/2024
Fully autonomous care
Just as the integration of automation will enable combat units to be more successful, efficient, and deadlier in their mission, automation should be applied to disease nonbattle injury and combat casualty care to optimize the efficiency and, therefore, the capability and capacity, of the Military Health System (MHS) to ensure Warfighter survivability.
The authors of this article posit that the MHS must adopt an automation paradigm to achieve its goals—minimizing casualties by optimizing health, maximizing casualty return to duty, optimizing battlefield casualty clearance while maintaining or exceeding current casualty outcomes, and overcoming contested logistics.
These key concepts are important because:
• When machines perform basic tasks that are simple, manual, and do not require human judgment, they free humans from these tasks providing humans more time to do other activities. Thus, automation increases system capacity (more tasks competed by the same or fewer humans).
• Faster, more accurate decision-making at echelon—from the “bedside” across the care continuum and at command and control (C2), evacuation, and logistics nodes—will further increase system capacity by prioritizing activities and making care more efficient.
Current Challenges Include :
• Lack of real-time data from the point of care and across the continuum of care (including evacuation and Role 1-4) and the lack of an ecosystem that promotes automation.
• A culture averse to humans being watched by sensors during patient care, to trusting technologies, and to automating most aspects of care.
• Medicine has few sensors or standards (compared to other automation fields) and produces variable outcomes, even with the same interventions.
In the future, we will have human-technology teams—the Pinnacle of Automation & Optimization—composed of robotic actors, artificial intelligence, and human medical providers working together in an efficient, collaborative manner that maximizes system capability and capacity to manage large volumes of casualties with limited human resources.
All autonomous systems begin with a sensing layer built upon devices and sensors that passively collect data about context, state, and activities. It is followed by an understanding or learning layer that interprets and makes sense of this data. This information can then be used to make or support decisions at all echelons of patient care. Finally, the MHS can use information to make faster decisions or offload certain decisions and tasks to machines.
Ultimately, creating human technology teams will change how the MHS delivers patient care across the continuum, affecting all aspects of doctrine, organization, training, material, leadership and education, personnel, facilities, and policy.
Future Steps
• Creating autonomous solutions that passively sense data (casualty status, caregiver actions, and resource consumption).
• Rapidly acquire sensors.
• Accelerate the development of the data infrastructure necessary to receive data collected from sensors, transfer it to a storage environment, annotate the data, curate it as needed, analyze it, model it, and share it.
• Develop algorithms, software, and robotics that automate casualty care tasks without requiring each team to collect proprietary datasets.
Article đź”— https://academic.oup.com/milmed/advance-article/doi/10.1093/milmed/usae377/7726880
Army Medicine Medical Service Corps Chief Medical Readiness Command, West Medical Readiness Command, Europe U.S. Army Medical Center of Excellence Medical Readiness Command, Pacific U.S. Army Medical Research & Development Command U.S. Army Telemedicine & Advanced Technology Research Center - TATRC
Create a culture of continuous learning generating adaptive medical leaders capable of leading across the competition/conflict continuum.
