Background: Adequate training and preparation of medical first responders (MFRs) are essential for an optimal performance in highly demanding situations like disasters (e.g., mass accidents, natural catastrophes). The training needs to be as effective as possible, because precise and effective behavior of MFRs under stress is central for ensuring patients’ survival and recovery. This systematic review offers an overview of scientifically evaluated training methods used to prepare MFRs for disasters. It identifies different effectiveness indicators and provides an additional analysis of how and to what extent the innovative training technologies virtual (VR) and mixed reality (MR) are included in disaster training research. Methods: The systematic review was conducted according to the PRISMA guidelines and focused specifically on (quasi-)experimental studies published between January 2010 and September 2021. The literature search was conducted via Web of Science and PubMed and led to the inclusion of 55 articles. Results: The search identified several types of training, including traditional (e.g., lectures, real-life scenario training) and technology-based training (e.g., computer-based learning, educational videos). Most trainings consisted of more than one method. The effectiveness of the trainings was mainly assessed through pre-post comparisons of knowledge tests or self-reported measures although some studies also used behavioral performance measures (e.g., triage accuracy). While all methods demonstrated effectiveness, the literature indicates that technology-based methods often lead to similar or greater training outcomes than traditional trainings. Currently, few studies systematically evaluated immersive VR and MR training. Conclusion: To determine the success of a training, proper and scientifically sound evaluation is necessary. Of the effectiveness indicators found, performance assessments in simulated scenarios are closest to the target behavior during real disasters. For valid yet inexpensive evaluations, objectively assessible performance measures, such as accuracy, time, and order of actions could be used. However, performance assessments have not been applied often. Furthermore, we found that technology-based training methods represent a promising approach to train many MFRs repeatedly and efficiently. These technologies offer great potential to supplement or partially replace traditional training. Further research is needed on those methods that have been underrepresented, especially serious gaming, immersive VR, and MR.

In medical simulation training two approaches are currently rather disjunct: realistic manikins are used to teach physical skills and procedures and VR systems are used to train situation assessment and decision making. We propose a mixed reality approach, which allows trainees to use real tools and their hands when interacting with a physical manikin overlaid with a responsive virtual avatar. In close exchange with first responder organizations, we developed and evaluated an MR training scenario. In the scenario, users can talk to injured people in a car accident, assess the threat of the environment, and utilize real medical equipment. Participants experienced high levels of physical- and self-presence, increased stress levels, and reported a high technology acceptance. The proposed main requirements of first responders regarding haptic multi-sensory skill training in MR and the lessons learned from the workshop aim to guide the design of training solutions for medical training in MR.

Mixed reality (MR) technology has the potential to enhance the disaster preparedness of medical first responders in mass-casualty incidents through new training methods. In this manuscript, we present an MR training solution based on requirements collected from experienced medical first responders and technical experts, regular end-user feedback received through the iterative design process used to develop a prototype and feedback from two initial field trials. We discuss key features essential for an effective MR training system, including flexible scenario design, added realism through patient simulator manikins and objective performance assessment. Current technological challenges such as the responsiveness of avatars and the complexity of smart scenario control are also addressed, along with the future potential for integrating artificial intelligence. Furthermore, an advanced analytics and statistics tool that incorporates complex data integration, machine learning for data analysis and visualization techniques for performance evaluation is presented.

The contemporary geopolitical environment and strategic uncertainty shaped by asymmetric and hybrid threats urge the future development of hands-on training in realistic environments. Training in immersive, virtual environments is a promising approach. Immersive training can support training for contexts that are otherwise hard to access, dangerous, or have high costs. This paper discusses the challenges for virtual reality training in the CBRN (chemical, biological, radioactive, nuclear) domain. Based on initial considerations and a literature review, we conducted a survey and three workshops to gather requirements for CBRN training in virtual environments. We structured the gathered insights into four overarching themes—the future of CBRN training, ethical and safety requirements, evaluation and feedback, and tangible objects and tools. We provide insights on these four themes and discuss recommendations.

In the field of medical first responder training, the choice of training modality is crucial for skill retention and real-world application. This study introduces the Green Manikin, an advanced Mixed Reality (MR) tool, conceptually combining the immersiveness of Virtual Reality (VR) with the tangibility of real-world training, and compares it against traditional real-world simulations and VR training. Our findings indicate that MR and real-world settings excel in Self and Social Presence, and in intention to use, offering heightened psychological presence suitable for complex training scenarios. Effort expectancy was highest in real-world environments, suggesting their ease of use for basic skill acquisition. This nuanced understanding allows for better tailoring of training modalities to specific educational objectives. Our research validates the utility of MR and offers a framework for selecting the most effective training environment for different learning outcomes in medical first responder training.