Machine Learning Models of Appropriate Medevac Utilization in Rural Alaska

阿拉斯加农村地区适当使用医疗后送的机器学习模型

基本信息

批准号：
10653776
负责人：
Brian Travis Rice
金额：
$ 16.63万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-26 至 2027-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10653776
关键词：
Acceleration Accidents Acute Address Affect Air Alaska Alaska Native Alaskan American Artificial Intelligence Award Behavioral Benefits and Risks Classification Clinical Collaborations Communities Data Data Science Databases Decision Making Dependence Development Disparity Disparity population Electronic Health Record Emergency Care Emergency Medicine Emergency Situation Epidemiology Equity Excess Mortality Expenditure Future Goals Grant Guidelines Health Health Disparities Research Improve Access Interview Machine Learning Medical Medical Informatics Mentors Mentorship Methodology Modeling Native-Born Outcome Patient-Focused Outcomes Patients Physicians Population Publications Qualitative Methods Qualitative Research Quality of Care Research Research Assistant Research Personnel Research Proposals Resource-limited setting Resources Rice Risk Rural Safety Scientist Structure Students Surveys System Testing Time Training Training Activity Underserved Population Universities Work Yukon-Kuskokwim Delta access disparities biomedical informatics care delivery career clinical care clinical decision-making computer program cost data management empowerment evidence base expectation experience global health health disparity improved informant innovation learning strategy machine learning classification machine learning method machine learning model model building mortality novel outcome disparities professor rural Alaska rural Americans rural area skills stakeholder perspectives statistics tool urban area

项目摘要

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.

项目摘要 /摘要该奖项的目的是为斯坦福大学急诊医学助理教授Brian Rice博士提供大学，从初级调查员过渡到独立临床 - 科学家使用应用生物医学信息来解决健康分布。赖斯博士是急诊药物理学具有流行病学和全球健康的高级学位，计算机背景编程和人工智能。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，以增强围绕围绕的精确，高价值的临床决策历史上处于弱势群体的急诊和运输。他的培训活动的重点推进他通过这些培训目标应用生物医学信息来解决健康分配的能力： 1）扩大他在数据管理和计算统计方面的技能2）社区学习方法 - 参与和参与健康差异研究的方法，以及3）获取新技能机器学习和分类模型构建。候选人召集了包括Dr. 生物医学人工智能专家蒂娜·埃尔南德斯·布萨德（Tina Hernandez-Boussard）专注于提高透明度并最大程度地减少机器学习模型中的偏见，以使其更加公平和可推广，并史黛西·拉斯穆斯（Stacy Rasmus），阿拉斯加领先的本地行为科学家，拥有丰富的经验成功进行阿拉斯加农村地区的社区参与定性研究。研究提案建立在候选人的先前与阿拉斯加农村地区的空中医疗疏散（MEDEVAC）一起工作，该假设确定了一个中心假设可以根据成果数据构建的机器学习模型对MEDEVAC进行适当的分类或不适当的分类并通过定性方法丰富。该中心假设将通过以下特定目的进行检验：1）定义阿拉斯加粗糙的Medevacs的燃烧和结果； 2）确定特定于上下文的关键贡献者阿拉斯加粗糙的Medevac利用； 3）开发机器学习模型以对适当性进行分类阿拉斯加农村地区的MEDEVAC利用。该应用程序中提出的研究具有创新性，因为IT员工接受的机器学习分类建模的方法并将其应用于Medevac的新领域和阿拉斯加本地健康差异。拟议的培训补助金的重要性是它将提供数据赖斯博士随后将这些模型作为决策工具的实施所需的技能在未来的R01级应用中。最终，这一继续研究有可能减少支出并通过将MEDEVAC资源重定向到时间敏感条件受益的患者来提高安全性来自阿拉斯加本地人阿拉斯加农村地区和所有居住在全国农村地区的美国人的社区。