WG-4
Sustainment
Medical Planners' Toolkit Demonstration
Jonathan Davis, Tracy Negas
Teledyne Brown Engineering / Naval Health Research Center
A discrete-event simulation evaluates expeditionary medical platforms, casualty recovery, and patient care across contested maritime operations.
Abstract
Delivering more agile, medically capable platforms across the spectrum of Joint Operations improves Navy Medical adaptability to Distributed Maritime Operations, Expeditionary Advanced Base Operations, and the Joint Warfighting Concept. The Naval Health Research Center (NHRC) is developing a new model to evaluate the performances of the Expeditionary Resuscitative Surgical System, Expeditionary Medical Unit (EMU), and En-Route Care System to determine the appropriate level of support these platforms provide in contested environments.
NHRC developed a discrete-event simulation framework in Python using the SimPy package. The model simulates patient care aboard damaged vessels, platform movement throughout the theater to recover patients from point of injury, and patient care aboard medical platforms as patients are transported out of theater. A resource allocation objective function was constructed to determine where EMU platforms or air transports travel to recover casualties. The allocation is allowed to be entirely situation-based, using variables such as the location of damaged vessels, the number of casualties aboard a damaged vessel, and the vessel’s condition. NHRC models mortality for personnel waiting in the water if their attacked vessel sinks using methodology from the Medical Planners’ Toolkit and real-world sea temperature data of the vessel’s location in the ocean. Casualties from ashore combat scenarios can be incorporated in the model by importing a casualty stream from JMPT and placing them at ashore EMU facilities for treatment. Examples of model output data include throughput at individual medical facilities, delays in casualty care, and the final casualty dispositions. A visualization of the model was created in Python using the matplotlib and cartopy packages.
Disclaimer: Some authors are service members or employees of the U.S. Government. This work was prepared as part of their official duties. Title 17, U.S.C. §105 provides that copyright protection under this title is not available for any work of the U.S. Government. Title 17, U.S.C. §101 defines a U.S. Government work as work prepared by a military service member or employee of the U.S. Government as part of that person’s official duties. The views expressed in this work are those of the authors and do not necessarily reflect the official policy or position of the Department of the Navy, Department of Defense, nor the U.S. Government.
Additional Information: This work was supported by the Joint Operational Medicine Information Systems under work unit no. N1214.
NHRC developed a discrete-event simulation framework in Python using the SimPy package. The model simulates patient care aboard damaged vessels, platform movement throughout the theater to recover patients from point of injury, and patient care aboard medical platforms as patients are transported out of theater. A resource allocation objective function was constructed to determine where EMU platforms or air transports travel to recover casualties. The allocation is allowed to be entirely situation-based, using variables such as the location of damaged vessels, the number of casualties aboard a damaged vessel, and the vessel’s condition. NHRC models mortality for personnel waiting in the water if their attacked vessel sinks using methodology from the Medical Planners’ Toolkit and real-world sea temperature data of the vessel’s location in the ocean. Casualties from ashore combat scenarios can be incorporated in the model by importing a casualty stream from JMPT and placing them at ashore EMU facilities for treatment. Examples of model output data include throughput at individual medical facilities, delays in casualty care, and the final casualty dispositions. A visualization of the model was created in Python using the matplotlib and cartopy packages.
Disclaimer: Some authors are service members or employees of the U.S. Government. This work was prepared as part of their official duties. Title 17, U.S.C. §105 provides that copyright protection under this title is not available for any work of the U.S. Government. Title 17, U.S.C. §101 defines a U.S. Government work as work prepared by a military service member or employee of the U.S. Government as part of that person’s official duties. The views expressed in this work are those of the authors and do not necessarily reflect the official policy or position of the Department of the Navy, Department of Defense, nor the U.S. Government.
Additional Information: This work was supported by the Joint Operational Medicine Information Systems under work unit no. N1214.
Presenters
- Jonathan DavisTeledyne Brown Engineering
- Tracy NegasNaval Health Research Center