Data Saves Lives—The U.S. Military Must Build a Real-Time Combat Casualty Care Data Ecosystem for the Future Battlefield

Large-scale combat operations (LSCO) will challenge the current U.S. military trauma system with high casualty volumes, prolonged evacuation timelines, and degraded logistics. The Joint Trauma System demonstrated the effectiveness of data-driven medical performance optimization during the Wars in Iraq and Afghanistan, reducing battlefield mortality. However, the current system relies on delayed, manual documentation processes that are inadequate for the operational tempo and complexity of future conflicts. This article advocates for the development of a real-time, automated combat casualty care data ecosystem that supports decision-making, resource allocation, and command and control across echelons. Current modernization efforts, including digital tools such as the Battlefield-assisted Trauma Distributed Observation Kit (BATDOK) and integration platforms like the Operational Medicine Data Service (OMDS) and the System for Injury Monitoring and Outcomes Nexus (SIMON), may improve data capture but are still heavily dependent on human input. We propose a future system centered on passive data collection, scalable edge computing, artificial intelligence-enabled triage and decision support, and seamless integration with tactical networks and operational planning tools. This system must distinguish between data needed for real-time care and that required for archival documentation of injuries and care provided. It must also enable both clinical optimization and trauma system learning without adding a burden to providers. Investments in real-time data infrastructure, machine learning, and automated sensing are necessary to maximize survivability in LSCO. Without this transformation, the military trauma system risks delays in care, degradation in outcomes, and reduced operational effectiveness. Real-time data are essential for modern combat casualty care and future mission success.