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An AI virtual training system for medical and logistics personnel

Tailoring casualty care training with AI and human-centered computing

The KWYN EFECTIVE training system provides:

• Phone, computer, and VR options for mass casualty training
• Unlimited customized scenarios generated for individual learner needs

Military medical personnel must be prepared for mass casualty incidents in which extreme numbers of critically injured people overwhelm available resources. Responding to these events is complicated, requiring medical personnel and other responders to work in synchrony, often with little preparation, to save as many lives as possible. Executing a mass casualty response is made even more difficult due to the lack of training resources available for personnel to practice the critical skills needed during these events.

Meeting the training challenges of mass casualty incidents

Our scientists and engineers developed KWYN EFECTIVE (Extensible Field and Evacuation Care Training in a Virtual Environment), a virtual training system that provides practice for mass casualty responders.

KWYN EFECTIVE is the Department of Defense’s first comprehensive system to train military medical personnel on medical care and logistics in austere, far-forward environments where conditions are especially challenging—there may not be enough supplies available to save current casualties, or enemy fire may make evacuation impossible.

KWYN EFECTIVE lets medical professionals safely visit—and revisit—a challenging environment, better preparing them for real-world scenarios.

The KWYN EFECTIVE system provides an immersive treatment experience using interfaces designed by medical specialists.
KWYN EFECTIVE Mobile (MSTAT) presents users with virtual patients in unlimited scenarios so they can practice treating injuries and receive immediate feedback on their performance.


KWYN EFECTIVE has a mobile-friendly, personalized training program for first responders to practice and refine medical and trauma treatment skills on virtual patients.

KWYN EFECTIVE Mobile (previously MSTAT) intelligently adapts to a specific trainee’s learning curve, providing an optimized learning path and curriculum. 

“The Department of Defense recognizes the need for training systems that prepare personnel in the military medical operation to perform their roles in severely degraded, prolonged field and en-route care settings. Our KWYN EFECTIVE system delivers the first virtual environment to train and assess these critical skills.

Benjamin Bauchwitz,
Human-Centered AI Scientist and Principal Investigator for KWYN EFECTIVE 

Immersive training tailored to the individual

Existing training methods for mass casualty incidents require the assembly of large numbers of specialists in real-world enactments, where manikins or actors play the victims. Due to this complexity, these large-scale events don’t happen very frequently.

In contrast, KWYN EFECTIVE enables a modern, gamified experience that immerses medical personnel within realistic exercises in virtual environments that are accessible from anywhere via their computers, VR headsets, and phones.

KWYN EFECTIVE also captures important decision-making themes based on existing resources and past experiences of military medical personnel. The system then automatically assesses trainee proficiency against objective performance metrics linked to these skills.

In this scenario, developed in KWYN EFECTIVE, a trainee plays from the perspective of the Incident Commander to allocate resources and assign helper medics to perform tasks on casualties. They also interpret mission requirements to make decisions about patient care.
“KWYN EFECTIVE combines our ability to build these realistic game environments with our ability to understand a complex set of skills from an educational point of view. On top of everything, we apply our ‘secret sauce’—we use AI analytics and algorithms to adapt the training to the individual trainee and make sure it delivers maximum learning gains in the minimum time… something that’s especially important for those who have to take the training while still performing their jobs.”
Dr. Peter Weyhrauch
Vice President, Human-Centered AI Division

Personalized training modules

Charles River’s training system lets each type of personnel train in different modules tailored to their specific roles and skills. The system currently has three main training modes (with additional modes under development).


Search and Rescue lets first responders practice navigating hazardous scenes and treating casualties with a limited set of resources.

Triage gives hospital staff members a chance to practice prioritizing just-injured casualties and provide initial life-sustaining interventions in a hospital setting.

Incident Command is designed for personnel who manage the logistics of the medical response. In a neighborhood-level aerial view of the disaster scene, users practice establishing casualty collection points, routing vehicles, and coordinating the transfer of medical resources.

Key advantages

KWYN EFECTIVE is implemented in the Unity game engine and C#, making it compatible with a diverse set of hardware platforms

By involving medical experts and seeking collaborative partners mid-development, the Charles River team is creating a training system that targets the actual skills performed during mass casualty events

Virtual training helps trainees master the rapid, critical decision-making needed to save lives, giving them the confidence to perform quickly when faced with real-life emergencies

Training tailored to an individual’s current abilities and knowledge increases efficiency and reduces training time and cost

Contact us to learn more about KWYN EFECTIVE and our other medical training efforts.

This work is supported by the US Army Medical Research and Materiel Command under Contract No.W81XWH-19-C-0044. The views, opinions and/or findings contained in this report are those of the author(s) and should not be construed as an official Department of the Army position, policy or decision unless so designated by other documentation.

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