Proof of concept of an Automated Battlefield Trauma System for large-scale combat operations.

Abstract

Background: Large-scale combat operations (LSCO) generate high casualty volumes, challenging battlefield trauma care and necessitating a synchronized approach that integrates medical operations with warfighting functions. Existing casualty management tools, such as the Medical Planners Toolkit and Joint Medical Planning Tool, provide static estimates and lack predictive capabilities for evacuation and medical resupply. The Automated Battlefield Trauma System (ABTS) was developed to address these limitations by automating casualty categorization, resource estimation, and Medical Common Operating Picture reporting.

Methods: Tested during the US Army I Corps' Warfighter Exercise (WFX) 25-02, ABTS used casualty data derived from the Russo-Ukrainian conflict to estimate critical resource needs for medical units across different echelons. Integrated with Warfighter Simulation data, ABTS provided automated dashboards to track casualty categories, estimate died-of-wounds rates, and predict resource shortages. Initially relying on Excel-based dashboards, late-stage integration with Palantir's Maven Smart Systems enabled enhanced real-time data visualization and decision support for commanders.

Results: Key takeaways from the proof of concept include the following: (1) automation significantly improves casualty care management in LSCO; (2) ABTS enhances predictive logistics for evacuation and medical resupply; (3) it serves as a critical risk management tool for commanders; and (4) integration with warfighting functions is essential for operational effectiveness. While successful, future iterations must refine casualty modeling, enhance data integration with emerging artificial intelligence and machine learning capabilities, and expand interoperability with Joint and allied forces.

Conclusion: The Automated Battlefield Trauma System demonstrated its potential to transform battlefield casualty management by leveraging automation and predictive analytics. Continued development will refine its capabilities, improve real-time data integration, and ensure its applicability across military operations, enhancing survivability and operational efficiency in LSCO environments.

Level of evidence: Proof of Concept; Level V.