A Mixed Reality Conscious Sedation Simulator for Learning to Manage Variability

描述(由申请人提供)：患者间的变异性是清醒镇静程序中不良结果的一个主要因素。例如，两个身高、体重和年龄相似的患者对同一种镇静剂的反应可能非常不同，导致镇静过度或镇静不足。在许多这种情况下，被评估为风险最低的患者实际上处于高风险。然后，一些问题，如意想不到的呼吸抑制，会导致不良后果，如神经损伤或死亡。目前的培训计划主要通过讲座和真实世界经验来培训学生，从后勤上讲，这无法在可接受的时间内使学生获得能力。为了解决这一问题，本提案旨在开发一种基于沉浸式混合现实(MR)的培训模拟器，用于教育学生有意识的镇静过程中的可变性。 方法：具体地说，拟议的模拟将使用可变性的模拟模型、身临其境的虚拟现实患者以及患者内部过程的概念性可视化来增强人体模型患者模拟器。这一混合现实的患者不仅将通过纳入患者访谈(例如，病史记录)提供更广泛的实践体验，而且还将额外模拟各种外部特征，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性，提出的基于MR的模拟器将在短时间内为学生提供与各种模拟患者的动手体验。最终，该模拟器旨在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并汇报清醒镇静情景；3)初步研究系统的学习效果并建立面子效度。拟议的模拟器的目标是提供各种实践经验和反馈，以加速学习，增强决策技能，并使学生能够在有意识的镇静中更有效地管理可变性。

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.