Simulation Approaches to Ventilator Education: Metrics and Evolution (SAVE-ME)

描述（由申请人提供）：机械通气（MV）是现代重症监护和急诊护理的基础干预措施，每年用于支持数十万无法独立呼吸的个体。不幸的是，这种挽救生命的干预可能会造成伤害：不明智的呼吸机设置可促进肺损伤，损害循环稳定性，使患者窘迫，刺激炎症反应，并延长所需支持的时间。机械通气的这些不良后果增加了患者痛苦的负担，增加了医疗资源的利用，并损害了结果。每年，相对较少的从业人员接受机械通气的详细培训。因此，需要MV的个体可能由从业人员进行管理，他们在这方面的培训有限或参差不齐。此外，机械通气的教学仍然主要是一种“床边”练习，更类似于学徒制，而不是一种系统的掌握方法。由于道德和实际的原因，新学习者不能在实际病人身上进行广泛的练习，而生理上现实的替代方法既昂贵又受到限制。此外，不能保证从业者暴露于所有可能的机械或生理紊乱。被教的人可能教得不好。当代评估机械通气专业知识的方法不适合在潜在有害干预的背景下定义临床医生的实践模式或能力，因为任何患者的问题都可能有许多可能的解决方案，普遍的生理学是高度动态的，临床医生在不确定性的背景下进行推理。同样，确定从业者维持其技能的程度也受到缺乏清晰度量标准的阻碍。我们将通过以下方式解决这三个问题：开发一个互联网可部署模拟器，学习者面对和管理一个精心构建的虚拟患者群体，患有常见的心肺疾病；2. 开发和应用最先进的模式识别方法来定义专家和新手在管理需要心肺支持的虚拟患者时采用的实践模式；3. 比较三种不同的培训方法的有效性：基于演示的，基于模拟的，以及基于模拟的培训，以适应学习者的“薄弱领域”；4. 阐明从培训和同时管理多个虚拟患者中抽出时间对提供者绩效的影响。拟议的工作主体将产生一个心肺管理培训工具，可通过互联网作为免费软件部署，一个专家和新手实践模式的数据库，关于最佳培训策略的信息，以及关于两个相关问题对提供者绩效妥协的数据。

DESCRIPTION (provided by applicant): Mechanical ventilation (MV) is a cornerstone intervention in modern intensive and emergency care that is used to support hundreds of thousands of individuals each year while they cannot breathe independently. Unfortunately, this lifesaving intervention can cause harm: injudicious ventilator settings can promote lung injury, compromise circulatory stability, produce patient distress, stimulate an inflammatory response, and prolong the period of support required. Such adverse consequences of mechanical ventilation add to the burden of patient suffering, increase healthcare resource utilization, and compromise outcomes. Each year, relatively few practitioners receive detailed training in mechanical ventilation. Consequently, individuals requiring MV may be managed by practitioners with limited or spotty training in this intervention. In addition, the teaching of mechanical ventilation remains primarily a "bedside" exercise more akin to an apprenticeship than a systematic approach to mastery. New learners cannot practice extensively on actual patients, for ethical and practical reasons, and physiologically realistic alternatives are expensive and suffer from limited access. Moreover, exposure of the practitioner to the full spectrum of possible mechanical or physiologic derangements cannot be guaranteed. Those who are taught may not be taught well. Contemporary approaches to the assessment of expertise in mechanical ventilation are ill-suited for defining clinician practice patterns or competence in the context of a potentially harmful intervention for which any patient problem may have many possible solutions, the prevailing physiology is highly dynamic, and the clinician is reasoning in the setting of uncertainty. Similarly, ascertaining the extent to which a practitioner maintains her or his skills is hampered by the lack of clear metrics. We will address these three concerns by: 1. Developing an Internet deployable simulator in which learners confront and manage a carefully constructed population of virtual patients suffering from common cardiopulmonary maladies; 2. Developing and applying state of the art pattern recognition approaches to define practice patterns adopted by experts and novices as they manage virtual patients who require cardiopulmonary support; 3. Comparing the effectiveness of 3 different approaches to training: presentation based, simulation based, and simulation based training that adapts to focus on the learner's "weak areas;" 4. Elucidating the consequences of time away from training and simultaneous management of multiple virtual patients on provider performance. The proposed body of work will produce a cardiopulmonary management training tool deployable as freeware via the Internet, a database of expert and novice practice patterns, information regarding optimal training strategies, and data regarding compromise of provider performance by two relevant issues.