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

健康不平等和基于机器学习的急诊科分诊工具：混合方法

• 批准号: 10248299
• 负责人: Stephanie Teeple
• 金额: $ 3.35万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10248299
• 关键词: Academic Medical Centers; Accident and Emergency department; Address; Adjuvant; Affect; African American; Algorithms; Artificial Intelligence; Characteristics; Clinical; Collaborations; Critiques; Data; Data Science; Data Scientist; Data Set; Data Sources; Decision Making; Demographic Factors; Development; Diagnosis; Discrimination; Education; Electronic Health Record; Elements; Emergency Medicine; Emergency Situation; Emergency department visit; Empirical Research; Epidemiology; Ethnic Origin; Ethnic group; Ethnography; Generations; Health; Health Policy; Healthcare; Human; Inequality; Intervention; Interview; Label; Life Cycle Stages; Literature; Machine Learning; Measurement; Medicine; Mentors; Methods; Minority Groups; Modeling; Nurses; Outcome; Patient Triage; Patients; Performance; Pharmaceutical Preparations; Physicians; Police; Policy Maker; Process; Provider; Public Health; Qualitative Methods; Race; Research; Research Personnel; Scientist; Service provision; Severities; Severity of illness; Social Work; Socioeconomic Status; Structure; System; Technology; Testing; Time; Training; Treatment outcome; Triage; United States; United States Food and Drug Administration; Universities; University Hospitals; Variant; Wait Time; Work; algorithm training; artificial neural network; autoencoder; base; clinical decision-making; computer science; court; deep learning; doctoral student; ethnic minority population; experience; health care settings; health equity; health inequalities; improved; indexing; innovation; learning strategy; machine learning algorithm; racial and ethnic; racial bias; social; social bias; social inequality; symposium; tool

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.

项目概要 越来越多的证据表明，机器学习 (ML) 等人工智能 (AI) 技术可以 当部署到现实世界环境中时，会延续甚至加剧社会不平等。这已经是 在许多领域都得到了证明，包括治安、法院系统、银行、社会服务提供和 人们越来越担心医学领域也会出现同样的情况。与此同时，新的事物也不断涌现 基于人工智能的干预措施，食品药品监督管理局（FDA）的数量增加了十倍 自 2017 年以来，基于人工智能的技术获得了批准。然而，很少有研究实证检验健康 基于机器学习的临床决策工具的公平性影响。基于 ML 的工具所在的临床领域之一 急诊科 (ED) 分诊已投入使用，作为常见紧急严重程度的替代方案 索引（ESI）系统。尽管广泛流行，但有证据表明基于 ESI 的分类有许多优点 问题，包括视力辨别力差，高达 50% 的患者在量表的中点进行分类， 并且与种族不平等有关，非洲裔美国患者的等待时间更长， 较低的分类水平控制疾病的严重程度。这项研究将使用基于机器学习的 ED 分类工具，该工具已经 在美国的一个主要学术医疗中心使用，以探讨几个因素的影响程度 与患者种族/族裔群体预测表现的不平等相关。这项研究将 采用混合方法同时检查影响系统的人类和“机器”元素 分诊工具对患者的最终影响。目标 1 将是一项涉及民族志观察和 对分诊护士进行半结构化访谈，为临床医生的理解制定一个概念框架 基于 ML 的工具的使用和交互。目标 2 将检查“标签偏差”，这是一种测量偏差。这 申请人将使用合成的真实电子健康记录（EHR）数据并模拟不同级别的标签 偏见，然后检查分类工具在患者种族/族裔群体中的预测性能。目标3将 探索估算缺失 EHR 数据的不同方法。申请人将部署通用的、简单的 基于删除的方法以及一种有前途的新型基于 ML 的插补方法（称为自动编码器）， 应用分类模型来生成预测并检查患者种族/族裔群体的表现。 该项目具有创新性，因为它有助于开发基于机器学习的工具的“生命周期”模型 及其对健康公平的影响，采用混合方法，将人类和 计算元素，同时还为申请人（医学博士生）提供严格的培训计划 流行病学。该培训计划严谨、协同且多样化，并将包括高级课程、 与该领域的专家进行专门的一对一和小组指导、参加研讨会和有针对性的指导 会议、与临床教育和专业发展的结合。这个项目将是一个必不可少的项目 申请人逐渐成熟为一名独立的医师科学家。