Health Inequality and a Machine Learning-Based Tool for Emergency Department Triage: A Mixed Methods Approach

项目摘要 越来越多的证据表明，像机器学习(ML)这样的人工智能(AI)技术可以 在部署到现实世界环境中时，会使社会不平等永久化，甚至加剧。这已经是 在许多领域，包括警务、法院系统、银行、社会服务提供和 越来越多的人担心，医学界也是如此。与此同时，涌现出了大量新的 基于人工智能的干预，食品和药物管理局(FDA)的数量增加了十倍 自2017年以来，批准了基于人工智能的技术。然而，很少有研究对健康进行经验性的检查 基于ML的临床决策工具的公平性含义。一个临床领域，基于ML的工具是 已在使用的是急诊科(ED)分诊，作为常见紧急情况严重性的替代方案 指数(ESI)体系。尽管它广受欢迎，但有证据表明，基于ESI的分诊有许多 问题，包括视力辨别能力差，高达50%的患者被分流到量表的中点， 并与种族不平等有关，非裔美国人患者经历更长的等待时间和 较低的分诊级别控制了疾病的严重性。本研究将使用基于ML的ED分诊工具，该工具已经 在美国一家主要的学术医学中心使用，探索几个因素在多大程度上 与患者种族/民族群体的预测表现不平等有关。这项研究将 采用混合方法的方法，同时检查影响 分诊工具对患者的最终影响。目标1将是一项定性研究，包括人种学观察和 对分诊护士的半结构化访谈，为临床医生的理解建立一个概念性框架 使用基于ML的工具并与之交互。目标2将检验“标签偏差”，这是一种测量偏差。这个 申请者将使用合成和真实的电子健康记录(EHR)数据，并模拟不同级别的标签 偏见，然后检查分诊工具在患者种族/民族群体中的预测性能。目标3将 探索用于输入丢失的电子病历数据的不同方法。申请人将部署常见的、简单化的 基于删除的方法以及被称为自动编码器的有前景的基于ML的新的补偿方法， 应用分诊模型来生成预测并检查患者种族/民族群体的表现。 该项目具有创新性，因为它有助于开发基于ML的工具的“生命周期”模型 以及它们对健康公平的影响，使用一种混合方法，将人类和 计算元素，同时也为申请者提供严格的培训计划，一名医学博士研究生在 流行病学。这项培训计划严谨、协同但又多样，将包括高级课程， 专门的一对一和小组辅导，与该领域的专家一起，出席研讨会和有针对性的 会议，与临床教育和专业发展相结合。这个项目将是一个必不可少的 在申请者成长为独立的内科科学家的过程中迈出一步。

Project Summary There is growing evidence that artificial intelligence (AI) technologies like machine learning (ML) can perpetuate or even worsen social inequalities when deployed into real-world settings. This has been demonstrated in many realms, including policing, the court system, banking, social services provision, and there is growing concern the same is true in medicine. At the same time, there has been an outpouring of new AI-based interventions, with a ten-fold increase in the number of Food and Drug Administration (FDA) approvals for AI-based technologies since 2017. However, little research empirically examines the health equity implications of ML-based clinical decision-making tools. One clinical arena in which ML-based tools are already in use is emergency department (ED) triage, as an alternative to the common Emergency Severity Index (ESI) system. Despite its widespread popularity, evidence has shown that ESI-based triage has many problems, including poor acuity discrimination, with up to 50% of patients triaged at the midpoint of the scale, and is associated with racial inequalities, with African-American patients experiencing longer wait-times and lower triage levels controlling for illness severity. This study will use an ML-based ED triage tool that is already in use at a major academic medical center in the United States to explore the extent to which several factors are associated with inequality in predictive performance across patient racial/ethnic groups. This research will take a mixed methods approach to concurrently examine both human and ‘machine’ elements that affect the triage tool’s final impact on patients. Aim 1 will be a qualitative study involving ethnographic observation and semi-structured interviewing of triage nurses, to develop a conceptual framework for clinicians’ understanding of and interaction with an ML-based tool. Aim 2 will examine ‘label bias’, a type of measurement bias. The Applicant will use synthetic and real electronic health record (EHR) data and simulate different levels of label bias, then examine predictive performance of the triage tool across patient racial/ethnic groups. Aim 3 will explore different methods for imputing missing EHR data. The Applicant will deploy common, simplistic deletion-based methods as well as a promising new ML-based imputation method called an autoencoder, apply the triage model to generate predictions and examine performance across patient racial/ethnic groups. This project is innovative because it contributes to the development of a ‘life cycle’ model of ML-based tools and their health equity implications using a mixed methods approach that integrates both human and computational elements, while also providing a rigorous training plan for the Applicant, an MD-PhD student in epidemiology. This training plan is rigorous, synergistic yet diverse, and will include advanced coursework, dedicated 1-on-1 and group mentoring with experts in the field, attendance at seminars and targeted conferences, integration with clinical education and professional development. This project will be an essential step toward the Applicant’s maturation into an independent physician-scientist.