Training healthcare personnel for mass-casualty incidents in a virtual emergency department: VED II.

INTRODUCTION: Training emergency personnel on the clinical management of a mass-casualty incident (MCI) with prior chemical, biological, radioactive, nuclear, or explosives (CBRNE) -exposed patients is a component of hospital preparedness procedures. OBJECTIVE: The objective of this research was to determine whether a Virtual Emergency Department (VED), designed after the Stanford University Medical Center's Emergency Department (ED) and populated with 10 virtual patient victims who suffered from a dirty bomb blast (radiological) and 10 who suffered from exposure to a nerve toxin (chemical), is an effective clinical environment for training ED physicians and nurses for such MCIs. METHODS: Ten physicians with an average of four years of post-training experience, and 12 nurses with an average of 9.5 years of post-graduate experience at Stanford University Medical Center and San Mateo County Medical Center participated in this IRB-approved study. All individuals were provided electronic information about the clinical features of patients exposed to a nerve toxin or radioactive blast before the study date and an orientation to the "game" interface, including an opportunity to practice using it immediately prior to the study. An exit questionnaire was conducted using a Likert Scale test instrument. RESULTS: Among these 22 trainees, two-thirds of whom had prior Code Triage (multiple casualty incident) training, and one-half had prior CBRNE training, about two-thirds felt immersed in the virtual world much or all of the time. Prior to the training, only four trainees (18%) were confident about managing CBRNE MCIs. After the training, 19 (86%) felt either "confident" or "very confident", with 13 (59%) attributing this change to practicing in the virtual ED. Twenty-one (95%) of the trainees reported that the scenarios were useful for improving healthcare team skills training, the primary objective for creating them. Eighteen trainees (82%) believed that the cases also were instructive in learning about clinical skills management of such incidents. CONCLUSIONS: These data suggest that training healthcare teams in online, virtual environments with dynamic virtual patients is an effective method of training for management of MCIs, particularly for uncommonly occurring incidents.

Virtual worlds and team training.

An important component of all emergency medicine residency programs is managing trauma effectively as a member of an emergency medicine team, but practice on live patients is often impractical and mannequin-based simulators are expensive and require all trainees to be physically present at the same location. This article describes a project to develop and evaluate a computer-based simulator (the Virtual Emergency Department) for distance training in teamwork and leadership in trauma management. The virtual environment provides repeated practice opportunities with life-threatening trauma cases in a safe and reproducible setting.

Avatars alive! The integration of physiology models and computer generated avatars in a multiplayer online simulation.

In a mass casualty incident, injured and at-risk patients will pass through a continuum of care from many different providers acting as a team in a clinical environment. As presented at MMVR 14 [Kaufman, et al 2006], formative evaluations have shown that simulation practice is nearly as good as, and in some cases better than, live exercises for stimulating learners to integrate their procedural knowledge in new circumstances through experiential practice. However, to date, multiplayer game technologies have given limited physiological fidelity to their characters, thus limiting the realism and complexity of the scenarios that can be practiced by medical professionals. This paper describes the status of a follow-on program to merge medical and gaming technologies so that computer generated, but human-controlled, avatars used in a simulated, mass casualty training environment will exhibit realistic life signs. This advance introduces a new level of medical fidelity to simulated mass casualty scenarios that can represent thousands of injuries. The program is identifying the critical instructional challenges and related system engineering issues associated with the incorporation of multiple state-of-the-art physiological models into the computer generated synthetic representation of patients. The work is a collaboration between Forterra Systems and the SUMMIT group of Stanford University Medical School, and is sponsored by the US Army Medical Command's Telemedicine and Advanced Technologies Research Center (TATRC).

Virtual worlds for teaching the new CPR to high school students.

In this study we created a virtual 3D world for learning to manage medical emergencies and evaluated it with 24 high school students in the USA and Sweden. We found that students in both groups felt immersed and found the online simulation easy to use. Scores for flow and self-assessed flow were significantly higher for the RHS group as compared to the HG group (p=.001 and .023 respectively; Mann Whitney U test). Self-efficacy scores for the HG group were significantly higher after training (p=.016 Mann Whitney U test). Males in the RHS group scored significantly higher on flow and self assessed flow than females (p=.006 and p=.023 respectively; Mann Whitney U test). This study demonstrates the potential value of using MMOS for learning to respond to medical emergencies.

Virtual patient model for multi-person virtual medical environments.

We describe the architecture of a virtual patient model, the Virtual ED Patient, for scenarios in emergency medicine. The model is rule-based, and uses four vital signs as a representation of its state. The model is used in a multi-person learning environment based on online gaming technology. The efficacy of the model and the Virtual ED learning environment is evaluated in a study where advanced medical students and first year residents manage six trauma cases. Pre and post-test performance results show significant learning, with results comparable to those obtained in human manikin simulators. Some future directions for development of the model are also presented.