A Mixed Reality Conscious Sedation Simulator for Learning to Manage Variability

描述（由申请方提供）：患者间变异性是清醒镇静手术中不良结局的主要影响因素。例如，两个身高、体重和年龄相似的患者可能对相同的镇静剂反应非常不同，导致过度镇静或镇静不足。在许多情况下，被评估为风险最小的患者实际上处于高风险状态。然后，问题，如意外的呼吸抑制，导致不良后果，如神经损伤或死亡。目前的培训项目主要通过讲座和真实的世界经验来培训学生，这在逻辑上不能在可接受的时间内使学生胜任。为了解决这个问题，该提案旨在开发一种基于沉浸式混合现实（MR）的训练模拟器，用于教育学生有关清醒镇静程序的可变性。 方法：具体而言，拟议的模拟将增加一个人体模型患者模拟器的可变性，沉浸式虚拟现实患者的模拟模型，以及患者的内部过程的概念可视化。这种混合现实患者不仅可以通过患者访谈（例如，历史记录），但它还将模拟各种外部特征，如种族、体重、身高和年龄，并提供高度可变的内部特征的概念可视化，如药物敏感性。基于这些可重构的特点，建议基于MR的模拟器将给学生的动手经验，在很短的时间内与各种各样的模拟病人。最终，模拟器的目的是让学生更深入地了解他们的程序决定如何对患者产生不同的影响。 具体目标：1）开发清醒镇静中患者变异性的模拟模型，2）开发模型驱动的混合现实患者和清醒镇静场景的报告，3）试点研究系统的学习有效性并建立表面有效性。拟议的模拟器的目标是提供各种动手的经验和反馈，将加速学习，提高决策能力，并使学生能够更有效地管理清醒镇静的变化。

DESCRIPTION (provided by applicant): Inter-patient variability is a major contributing factor to adverse outcomes in conscious sedation procedures. For example, two patients of similar height, weight, and age may react very differently to the same sedative, resulting in over sedation or under sedation. In many of these cases, patients who are assessed to be at minimal risk are actually at high risk. Then, problems, such as unexpected respiratory depression, cause an adverse outcome, such as neurologic damage or death. Current training programs mainly train students through lectures and real world experience, which logistically cannot bring students to competency in an acceptable amount of time. To address this problem, this proposal aims to develop an immersive mixed reality (MR) based training simulator for educating students about variability in conscious sedation procedures. Approach: Specifically, the proposed simulation will augment a mannequin patient simulator with a simulation model of variability, an immersive virtual reality patient, and conceptual visualizations of the patient's internal processes. This mixed reality patient will not only offer a wider variety of hands-on experience through the incorporation of patient interviews (e.g., history taking), but it will additionally simulate a variety of external characteristics, such as race, weight, height, and age, and offer conceptual visualizations of highly variable internal characteristics, such as drug sensitivity. Based on these reconfigurable characteristics, the proposed MR-based simulator will give students hands-on experience with a wide variety of simulated patients in a short amount of time. Ultimately, the simulator aims to afford students a deeper understanding of how their procedural decisions can have variable effects on patients. Specific Aims: 1) develop a simulation model of patient variability in conscious sedation, 2) develop model-driven mixed reality patients and debriefing for a conscious sedation scenario, 3) pilot-study the learning effectiveness of the system and establish face validity. The goal of the proposed simulator is to provide a variety of hands-on experiences and feedback that will accelerate learning, enhance decision making skills, and enable students to more effectively manage variability in conscious sedation.