As augmented reality (AR) technology offers enhanced capabilities for medical simulation training, research is required to understand how to best leverage the strengths of AR. We describe a literature review that will provide the foundation for articulating and testing candidate principles for AR adaptive training design. Our goal is to articulate and test design principles for designing recognition skills training as described in the recognition-primed decision model (Klein, 1997). We identified seminal articles from the the Naturalistic Decision Making literature, and used those to seed a systematic review of the medical education and combat medic training literatures. Findings from the literature review suggest three categories of candidate design principles: 1) fidelity and realism, 2) engagement, and 3) scaffolding. Next steps will include articulating specific design principles and designing a series of studies to test them using the VPIT AR training platform.
Immersive technology such as virtual, augmented, and mixed reality has been used in entertainment. Applying this technology for educational purposes is a natural extension. We tested the ability of immersive technology to enhance medical education within a scenario about progressively worsening tension pneumothorax using a virtual patient. The goals of the study were 1) to determine whether those in the experimental group were better able to differentiate between normal and abnormal perceptual cues, and 2) to obtain feedback about the Augmented Reality (AR) training experience. For this study, the control group received traditional textbook training about tension pneumothorax. The experimental group received the same textbook training plus the AR tension pneumothorax scenario. An augmented reality headset was used to display a virtual patient on a table for the experimental group participants. All participants completed a pre and post-training knowledge test. Changes in the score of the accuracy from pre-post tests were used to establish whether the experimental group was better able to classify the perceptual cues. All participants responded to questions about the training experience at the end of the session. We discuss whether adding augmented reality training allowed medical students to better discern between abnormal and normal cues, and report our insights for what learning objectives AR can support in simulation-based training.
There is a great need for creating schedules that are optimized. Yet, some individuals have had less than desirable experiences with “optimal” scheduling. This could have been due to prioritization of the wrong criteria, leading to schedules that did not make practical sense, or that were math-intensive and were not able to be easily interpreted. Also, there are many types of optimization problem formulations and solution methods. Here, we divide the formulations into two major types: batched and online scheduling classes are discussed. A different technique has been created that allows schedules to be made that are not only optimal, based on the formulations or framing, but that are actually useful. Here, we discuss two types of methods, one batched called Genetic Algorithms with an Earliest Due Date encoding Method (GAGEDD) and the other online called Markov Decision Processes and Reinforcement Learning extensions. These methods are already being employed to create practical and optimal schedules that can include many different constraints and are able to instantly take into account new scheduling requests and take optimal actions regardless of what state the system is currently in. Especially with current world events (COVID-19), it is important to intelligently schedule patients.
It is necessary to educate medical students to prepare them for the healthcare world they will enter upon completion of their training. This pertains to not only the content of the education, but also the experiences during the instruction, and the application of the learning. However, during their time in training, a student will likely not encounter all the ailments that they will while working. This means that their education should be supplemented to help them be better prepared. We propose augmented reality (AR) virtual patients as a method to assist in the delivery of this supplementation. This paper’s focus is on enhancing the usefulness of AR by providing learners with interactions with a variety of virtual patients. The target audience for this topic includes medical educators, medical school students, residents, educators interested in AR, and designers of educational AR systems. The mentioned topics can even apply to continuing education or refresher courses for physicians. A review of aspects that can allow AR to be more immersive and beneficial to those learning about various ailments is provided. Elements of AR for medical education training are discussed to enhance the relevance and applicability of the learning experience. These include presenting multiple simultaneous ailments and the ability to modify AR patient characteristics, which can be accomplished more quickly and with more possibilities than when using manikins.
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