Sally A. Santen MD PhD, Kimberly Lomis MD, Judee Richardson PhD, John S. Andrews MD, David Henderson MD, Sanjay V. Desai MD
<p>Across the continuum of emergency medicine (EM) education, physicians strive to continuously develop their skills while navigating multiple demands. To achieve the aim of learning amidst exponential growth in medical knowledge and increasingly complex medical care, a new system of accessible, personalized, and continuous learning is needed. In this commentary, we describe the model of precision medical education (PME),<span><sup>1</sup></span> which includes using data and technology to transform lifelong learning by improving data inputs, personalization, and efficiency.</p><p>Innovation creates transformation in medical education. In other spheres, the arc of innovation empowers users and builds value.<span><sup>2</sup></span> For example, Amazon shifted purchasing power from local stores to consumers.<span><sup>3</sup></span> Netflix transferred power of choice to viewers, creating an industry for asynchronous content. Google shifted power of information from the few to many.<span><sup>4</sup></span> Generative AI (artificial and augmented intelligence) similarly has shifted the ability to gain and apply knowledge from experts to the people. While it takes time to fully realize their potential, these innovations largely meet the needs of consumers and society by shifting the locus of control to the end users. We believe medical education should create similar transformational shifts for learning to bring the locus of control to individual—student, emergency medicine (EM) resident, and practicing physician in the arc of lifelong learning.</p><p>The goal of training is to produce an EM physician workforce capable of delivering high-quality care to patients and communities. Explosive growth in medical knowledge and remarkable procedural advances have underscored physicians’ need for continuous and effective lifelong learning. The need to make this learning simple and accessible so that it weaves within existing workflows is also an imperative. Yet how do physicians maintain and advance their knowledge?<span><sup>5</sup></span> The gap between the need and process of learning, including resources, time, and methods, can contribute to the challenges of ongoing learning, contributing to burnout and moral distress as physicians struggle to keep up.</p><p>Medical education for medical students, residents, and practicing physicians has not evolved sufficiently with the pace of change in technology and remains encumbered by inflexibility, inefficiency, and inequity. This gap enhances the struggle to meet the current and future needs of physicians.<span><sup>6</sup></span> There is little emphasis on the process of lifelong learning or maintaining competency in the rapidly expanding universe of medical knowledge and new procedures.</p><p>Because of resource constraints and the need to deliver training at scale, structured medical education (undergraduate medical education, graduate medical education, and continuing professional development [CPD]) are
接下来的行动和干预措施--将这些数据与个人特征和偏好、来自高质量来源(如已发表的研究和指南)的及时教育计划以及基准信息相结合,将有助于指导医生的发展。理想情况下,辅导将有助于解释反馈信息,提高参与度和个性化程度。例如,通过审查住院医师接触病人的情况(差距),可以针对这些差距实施具体的培训。包括学习评估、医师表现、病人结果和干预评估在内的结果将为个人和项目层面的反馈提供结果。然后,所有信息都会反馈到循环中,为更多的见解、学习和改进提供数据作为输入。此外,还可以通过对医生教育的干预来观察和解决医疗差距问题。图 2 构想了一个未来的环境,在这个环境中,继续医学教育系统得到发展,并帮助医生进行终身学习和改善病人护理。虽然其中一些图示还在未来,但 PME 的试点工作已经开始,我们预计生成式人工智能技术将加速这些努力。Schaye 和团队9 正在开发自然语言处理技术,用于评估住院医师的临床推理能力并提供反馈。该系统将审查每位住院医师的病历,并评估其临床推理能力,在住院医师仪表板上提供相关信息,住院医师可通过该仪表板链接到病历中的具体病历,从而了解自己在哪些方面可以改进。仪表板还将提供汇总数据,记录临床推理记录的改进情况,并将其作为进一步改进的成果。一些急诊团队正在使用 EPIC 元数据为住院医师和急诊项目提供信息。EPIC 提供 "信号数据",即医疗服务提供者如何使用电子病历 (EHR) 的元数据。例如,可以创建报告来记录所花费的时间和地点(笔记、医嘱、追踪、处置和病历审查)。同样,图表可以显示住院患者如何下达他们的大部分医嘱(单个医嘱或医嘱集),以及与科室其他人相比的智能短语/宏使用情况。这些输入可为医疗服务提供者提供数据,帮助他们了解自己的工作流程和效率。从这些洞察中,他们可以调整自己的方法并监控变化。在此基础上,Schauer 正在探索患者疗效指标与 EPIC 元数据(用户使用模式)之间的关系,如效率、收件箱指标和工作流程。虽然这项工作的对象是内科住院医师,但对急诊科住院医师而言,PME 可使住院医师和教师将电子病历的使用效率指标与病人吞吐量联系起来,更重要的是与病人预后联系起来。11 Woodworth 和团队12 正在为麻醉住院医师建立一个平台,将能力发展、核心知识和患者接触与学习资源结合起来,以弥补差距。一些项目使用实时定位服务跟踪器来收集住院医生的位置以及他们在病房或工作间花费的时间。例如,Phadnis 等人14 报道了使用电磁触觉模拟器进行外侧犬齿切开术和胸廓切开术培训的情况。这些项目有意利用数据向医生提供反馈,以调整他们的学习和实践,这些创新利用了 PME 来帮助医生改善病人护理。公共教育部也可以在项目和组织层面开展工作。例如,美国医学专科委员会(American Board of Medical Specialties)刚刚资助了一个项目,将就诊诊断自动映射到专科委员会的临床领域,以加强评估、认证和精准教育,其中包括将临床就诊映射到 EM 护理模式。
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