Machine Learning Models of Appropriate Medevac Utilization in Rural Alaska

项目摘要/摘要 该奖项旨在为斯坦福大学急诊医学助理教授布莱恩·赖斯博士提供 大学，他从初级研究员转变为独立临床医生所需的支持- 使用应用生物医学信息学解决健康差距的科学家。赖斯医生是一位急诊医生 具有流行病学和全球卫生方面的高级学位和计算机背景的医生 编程和人工智能。他的长期目标是利用他的跨学科培训来发展 并实施机器学习工具，以增强精确、高价值的临床决策环境 在历史上处于不利地位的人口中的紧急护理和交通。他的培训活动主要集中在 通过以下培训目标提高他应用生物医学信息学解决健康差距的能力： 1)扩展他在数据管理和计算统计方面的技能2)社区学习方法- 参与和参与式的健康差距研究方法，以及3)获得新的技能机器 学习和分类模型的建立。这位候选人已经召集了一个导师团队，其中包括邓肯博士。 Tina Hernandez-Boussard，生物医学人工智能专家，专注于提高透明度 尽量减少机器学习模型中的偏见，使其更加公平和普遍。 Stacy Rasmus，阿拉斯加土著行为科学家，具有丰富的成功指挥经验 阿拉斯加农村社区参与的定性研究。研究计划是建立在候选人先前的基础上的 与阿拉斯加农村地区的空中医疗后送(医疗队)合作，建立了以下中心假设 根据建立在结果数据基础上的机器学习模型，医疗者可以被分类为合适或不合适 并通过定性的方法进行了丰富。这一中心假设将通过以下具体目标进行检验：1) 确定阿拉斯加农村地区医务人员的负担和结果；2)确定针对具体情况的关键贡献者 阿拉斯加农村地区医疗救护设备的使用；以及3)开发机器学习模型，以分类 阿拉斯加农村医疗救护中心的使用情况。本申请中提出的研究具有创新性，因为它采用了 被接受的机器学习分类建模方法，并将其应用于医疗保健新领域 和阿拉斯加原住民的健康差距。拟议的培训补助金的意义在于它将提供数据 以及赖斯博士随后研究这些模型作为决策工具的实施所需的技能 在未来的R01级应用程序中。归根结底，这种连续的研究有可能减少开支 并通过将医疗救护资源重新定向到时间敏感型疾病受益的患者来提高安全性 远离医疗吸毒者，远离会带来风险和成本而没有收益的患者，这两种情况都发生在阿拉斯加原住民 阿拉斯加农村社区和全国所有生活在农村地区的美国人。

PROJECT SUMMARY / ABSTRACT The purpose of this award is to provide Dr. Brian Rice, Assistant Professor of Emergency Medicine at Stanford University, the support necessary for his transition from a junior investigator into an independent clinician- scientist using applied biomedical informatics to address health disparities. Dr. Rice is an emergency medicine physician with an advanced degree in epidemiology and global health, and a background in computer programming and artificial intelligence. His long-term goal is to utilize his interdisciplinary training to develop and implement machine learning tools to empower precise, high-value clinical decision-making surrounding emergency care and transport in historically disadvantaged populations. His training activities focus on advancing his ability to apply biomedical informatics to address health disparities via these training objectives: 1) expanding his skills in data management and computational statistics 2) learning methods for community- engaged and participatory approaches to health disparities research, and 3) acquiring new skills machine learning and classification model building. The candidate has convened a mentorship team that includes Dr. Tina Hernandez-Boussard, a biomedical artificial intelligence expert with a focus on improving transparency and minimizing bias in machine learning models to make them more equitable and generalizable, and Dr. Stacy Rasmus, a leading Alaska Native behavioral scientist with extensive experience successfully conducting community-engaged qualitative research in rural Alaska. The research proposal builds off the candidate’s prior work with air medical evacuation (medevacs) in rural Alaska which established the central hypothesis that medevacs can be classified as appropriate or inappropriate by machine learning models built on outcome data and enriched by qualitative methods. This central hypothesis will be tested by the following specific aims: 1) define the burden and outcomes of medevacs in rural Alaska; 2) identify key context-specific contributors to medevac utilization in rural Alaska; and 3) develop machine learning models to classify appropriateness of medevac utilization in rural Alaska. The research proposed in this application is innovative because it employs accepted methods of machine learning classification modelling and applies them to novel fields of medevac and Alaska Native health disparities. The significance of the proposed training grant is it will provide the data and the skills required for Dr. Rice to subsequently study the implementation of these models as a decision tool in a future R01-level application. Ultimately, this continuum of research has the potential to decrease expenses and improve safety by redirecting medevac resources towards patients whose time-sensitive conditions benefit from medevacs and away from patients that incur risk and cost without benefit, both in Alaska Native communities in rural Alaska and for all Americans living in rural regions nationwide.