Project mTEAM: Advancing Emergency Medicine Trainee Skills using Multimodal Debriefing System in Simulation-based Training

随着美国人口寿命的延长和慢性疾病的增多，突发医疗紧急情况的发生率不断上升。这些急性事件造成了一个复杂、高压力的环境，需要医疗保健专业人员团队准确、快速地采取行动，为患者提供最佳的生存机会。现在比以往任何时候都更需要了解如何更好地为团队在快节奏的急性护理环境中工作做好准备。医疗保健专业人员需要频繁的、现实的培训机会，提供有意义的技能反馈，这些技能对于高质量的基于团队的临床护理至关重要。研究组开发了用于心脏骤停复苏的多用户虚拟现实（VR）平台。该平台旨在为医疗保健专业人员的培训创造现实的时间压力和快速的工作量变化。然而，现有的医疗保健模拟培训，包括VR和基于人体模型的学习，需要持续和实时的人体观察。这种限制导致学习者接收到的反馈质量参差不齐——通常是泛化的、不一致的，并且高度依赖于模拟讲师。由于反馈对学习和发展至关重要，这限制了模拟训练的有效性和可扩展性。为了填补这一空白，研究小组将开发和评估一种新的汇报系统，旨在捕获和可视化多模态数据流，评估学习者的认知（例如，临床决策）和行为（例如，态势感知，沟通）过程，以提供数据为基础的反馈，重点是改善突发医疗紧急情况患者的团队护理。通过这种新的多模式汇报系统，教师将能够在模拟后的汇报会议中为临床医生提供新的见解和个性化反馈，从而允许更有意义的反思、有针对性的干预和这些复杂技能的快速发展。该项目的目标是：1)与受训者和教师进行以人为中心的设计研究，以完善多模式汇报系统的概念；2)设计并发展一个不引人注目的多模式传感器数据收集系统；3)进行准实验研究，评估本研究述职系统在提高临床知识和团队合作技能方面的潜力。拟议的研究不仅为跨机构开展心脏骤停培训的方式提供了一条强有力的前进道路，而且还将创造知识和系统，可以转化为开发基于数据的团队培训计划，用于其他医疗领域和其他依赖专家团队的高风险行业。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

With the United States population living longer and having more chronic health conditions, the incidence of sudden medical emergencies is continually growing. These acute events create a complex, high-stress environment that requires teams of healthcare professionals to act precisely and quickly to give patients the best chance of survival. The need for knowledge about how to better prepare teams for work in fast-paced, acute care settings is more important than ever. Healthcare professionals need frequent, realistic training opportunities that offer meaningful feedback on the skills that are essential for quality team-based clinical care. The research team has developed a multi-user Virtual Reality (VR) platform for Cardiac Arrest Resuscitation. This platform is designed to create realistic time pressure and rapid workload changes for the training of healthcare professionals. However, existing healthcare simulation training, including VR and manikin–based learning, requires constant and real-time human observation. This limitation results in learners receiving feedback that is of variable quality - often generalized, inconsistent, and highly dependent on simulation instructors. With feedback being essential for learning and development, this limits the effectiveness and scalability of simulation training. To fill this gap, the research team will develop and evaluate a novel debriefing system that aims to capture and visualize multimodal data streams that evaluate learners’ cognitive (e.g., clinical decision-making) and behavioral (e.g., situational awareness, communication) processes to provide data-informed feedback focused on improving team-based care of patients who suffer sudden medical emergencies. Through this new multimodal debriefing system, instructors will be able to provide new insights and personalized feedback to clinicians during post-simulation debriefing sessions to allow for more meaningful reflection, targeted intervention, and rapid development of these complex skills. The project objectives are to: 1) conduct a human-centered design study with trainees and faculty to refine the concept of a multimodal debriefing system 2) engineer and evolve an unobtrusive multi-modal sensor-based data collection system; and 3) conduct a quasi-experimental study to evaluate the potential of this study’s debriefing system to improve clinical knowledge and teamwork skills. The proposed research not only provides a strong path forward to impact the way cardiac arrest training is carried out across institutions, but it will also create knowledge and systems that can be translated to develop data-informed, team-based training programs for other medical domains and other high-risk industries that rely on expert teams.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.