One of the most effective means of reducing medical errors is through good communication. The Immersive Modular Patient Care Team Trainer (IMPACTT) project is funded by DHA/JPC-1 in conjunction with Army Futures Command. Initially targeting pre-deployment medical teams, it is designed to address gaps in team training, specifically, ways to improve communication and enhance performance of teams working in emergency rooms and austere environments. This multi-user training simulation runs on commercial tablet computers and is built on the open source Advanced Modular Manikin (AMM™) platform. Using touchscreen tablets, learners assess and treat interactive 3D virtual patients suffering from multi-system trauma. Players select the appropriate Advanced Trauma Life Support® (ATLS) interventions from radial menus on their screen and, since the virtual patients’ physiology is driven by Biogears® Open Source Physiology Engine, their vital signs, behavior, and appearance improve or deteriorate, based on the appropriateness and timeliness of each treatment. The program supports a range of practitioners (doctors, nurses, techs, or respiratory techs). The virtual patient is displayed on each players’ tablet, as well as on a shared large screen. A separate array of tablets serves as virtual medical equipment, to include a patient monitor, IV pump, ventilator, labs, and a urine meter, making the system affordable and portable. Hands-on interventions, such as establishing vascular access (IV/IO) or intubating the patient, can be performed virtually or via an AMM-compliant part-task trainer, making the system scalable, based on learner needs. During virtual interventions, the player is presented with a task-specific cognitive exercise that engages them for the time it would normally take to perform the intervention. The IMPACTT system is designed to improve team communication and enhance performance, thereby reducing life-threatening medical errors in emergency settings. This paper will discuss implementation of this blended reality training capability, its challenges, lessons learned, and future applications.