A17 Enhancing NATO medical evacuation efficiency through geospatial modeling and medical intelligence integration

Introduction The evolution of modern warfare, characterized by asymmetric engagements under broad-spectrum influencing and dispersed operational areas, necessitates technological innovations in optimizing casualty evacuation in military medical operations. In this context, recent research efforts on geospatial models have been gaining attraction in their ability to integrate large, heterogeneous datasets on operational and health-related information. 1 Research efforts have focused in optimizing evacuation logistics under uncertainty 2 and stochastic geospatial models for medical facility location and evacuation transport. 3 Dynamic dispatch-routing algorithms are also utilized for volatile battlefield casualty patient flow. 4 However, addressing the core casualty-incidence variables and patient pathway optimization in dispersed topography warrants further research. The present research develops a stochastic geospatial model to optimize evacuation pathways in improving adherence to MEDEVAC requirements. The model focuses on NATO’s northern and rural operating environments, addressing variable challenges of dispersed topography, differentiated injury profiles and the critical need for interoperable solutions under prolonged evacuation timelines. The proposed model addresses central predictor variables in reducing evacuation delays and improving continuity of care from POI to MTF in accordance with NATO 10–1-2(+2) evacuation timeline. Methods The system integrates supervised machine learning within a stochastic geospatial modelling framework to optimize casualty throughput from POI to MTF Role1. Its two primary components are 1) Casualty-incidence determinants. The ML algorithm ranks variable importance to reveal which factors govern casualty clustering, addressing injury hotspots utilizing simulated operation field data. 2) Throughput pathway optimization. Dynamic geospatial modelling identifies optimal POC and evacuation routes with a delay-minimizing function, stochastically modelling transition states between POI and higher levels of care. Results The model was implemented on a simulated small-scale operation field, trained with open-source Finnish geodata representative of NATO’s rural context. Validation metrics (i) proportion of variance in casualty density explained by the ranked determinants, (ii) evacuation-time reduction, and (iii) compliance with the doctrinal timeline. Model performance explained 86% of variance in casualty density with evacuation time reduction of 18%. The combined effect was a delay reduction to surgical transition state and improved adherence to evacuation timeline. Limitations in data dependency, standard simulation model deficiencies and changing operation condition adaptability highlight areas for refinement. Discussion This research contributes to NATO’s readiness for ‘fighting the next fight’ by providing a dual functionality: identifying core casualty clustering variables and optimizing throughput pathways. These parameters include standardized data structures for integrating new data sources, formulating a data-sharing protocols for continuous MEDINT exchange, and embedding model accuracy-improving factors, casualty profiles and medical resource availability, into the model algorithm. Critically, the model relies on known and predetermined injury profiles, which must be regularly shared and updated among coalition forces for the system to adapt to changes in operational situation. Competing interests The authors declare no competing interests. References Alizadeh M, Amiri-Aref M, Mustafee N, Matilal S. A robust stochastic casualty collection points location problem. Eur J Oper Res . 2019; 279 (3):965–983. doi:10.1016/j.ejor.2019.06.018 Çağlayan N, Satoğlu Sİ. Multi-objective two-stage stochastic programming model for a proposed casualty transportation system in large-scale disasters: a case study. Mathematics . 2021; 9 (4):327. doi:10.3390/math9040327 Clark LP, Zilber D, Schmitt C, Fargo DC, Reif DM, Motsinger-Reif AA, et al . A review of geospatial exposure models and approaches for health data integration. J Expo Sci Environ Epidemiol . 2024. doi:10.1038/s41370–024-00712–8 Frial V, Robbins MJ, Jenkins PR. Solving the military medical evacuation dispatching, preemptive rerouting, redeploying, and delivering problem via tree-based machine learning and approximate dynamic programming approaches. Transp Sci . 2024. doi:10.1287/trsc.2023.1212