Machine Learning Models of Appropriate Medevac Utilization in Rural Alaska

项目总结/摘要 这个奖项的目的是提供布赖恩赖斯博士，急诊医学助理教授在斯坦福大学 大学，必要的支持，他从一个初级研究员过渡到一个独立的临床医生- 科学家使用应用生物医学信息学来解决健康差距。莱斯医生是一种急救药 一位拥有流行病学和全球健康高级学位并具有计算机背景的医生 编程和人工智能。他的长期目标是利用他的跨学科培训， 并实施机器学习工具，以实现精确，高价值的临床决策环境 在历史上处于不利地位的人口的紧急护理和运输。他的培训活动集中在 通过这些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- 采用参与式方法进行健康差异研究，以及3）获得新的技能 学习和分类模型的建立。候选人召集了一个导师团队，其中包括博士。 Tina Hernana-Boussard，生物医学人工智能专家，专注于提高透明度 最大限度地减少机器学习模型中的偏见，使其更加公平和普遍。 斯泰西拉斯穆斯，领先的阿拉斯加原住民行为科学家，拥有丰富的经验，成功地进行了 阿拉斯加农村社区参与的定性研究。研究计划建立在候选人的先验基础上 在阿拉斯加农村进行空中医疗后送（medevacs），建立了中心假设， 医疗后送可以通过基于结果数据建立的机器学习模型来分类为适当或不适当 并通过定性方法进行了丰富。这一中心假设将通过以下具体目标进行检验：1） 确定阿拉斯加农村医疗后送的负担和结果; 2）确定关键的特定背景贡献者， 阿拉斯加农村的医疗后送利用率;以及3）开发机器学习模型， 阿拉斯加农村地区的医疗后送利用率。本申请中提出的研究是创新的，因为它采用了 接受机器学习分类建模方法，并将其应用于医疗后送的新领域 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， 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R 01级应用程序中。最终，这种连续的研究有可能减少开支 并通过将医疗后送资源重新分配给那些对时间敏感的条件受益的病人来提高安全性 从医疗后送和远离病人，招致风险和成本没有好处，无论是在阿拉斯加土著 阿拉斯加农村社区和全国农村地区的所有美国人。

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.