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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

基本信息

批准号：
10452759
负责人：
Stephanie Teeple
金额：
$ 3.42万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
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/10452759
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 experience health care settings health equity health inequalities improved indexing innovation learning strategy machine learning algorithm machine learning method machine learning model minority patient racial and ethnic racial bias racial disparity social social bias social inequality symposium tool

项目摘要

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.

项目摘要越来越多的证据表明，机器学习（ML）等人工智能（AI）技术可以在部署到现实世界环境中时，会使社会不平等永久化甚至恶化。这是在许多领域，包括警务，法院系统，银行，社会服务的提供，越来越多的人担心医学界也是如此。与此同时，基于人工智能的干预措施，美国食品和药物管理局（FDA）的数量增加了10倍自2017年以来，基于AI的技术获得批准。然而，很少有研究从经验上考察健康基于ML的临床决策工具的公平意义。一个临床竞技场，其中基于ML的工具急诊科（艾德）的分诊已经在使用，作为常见的紧急情况严重性的替代方案指数（ESI）系统。尽管它广泛流行，证据表明，基于ESI的分流有许多问题，包括敏锐度辨别力差，高达50%的患者在量表的中点进行分诊，与种族不平等有关，非洲裔美国人的等待时间更长，控制疾病严重程度的较低分类水平。本研究将使用基于ML的艾德分类工具，在美国的一个主要学术医疗中心使用，以探索在多大程度上，与患者种族/族裔群体之间预测性能的不平等相关。这项研究将采取混合方法，同时检查影响分类工具对患者的最终影响。目标1将是一项涉及人种学观察的定性研究，对分诊护士进行半结构化访谈，为临床医生的理解建立一个概念框架与基于ML的工具的交互。目标2将检查“标签偏差”，这是一种测量偏差。的申请人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，然后检查患者种族/民族群体的分类工具的预测性能。目标3将探索不同的方法来填补缺失的EHR数据。申请人将部署通用的、简单的基于删除的方法以及称为自动编码器的有前途的新的基于ML的填补方法，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。这个项目是创新的，因为它有助于开发基于ML的工具的“生命周期”模型及其健康公平的影响，使用混合方法的方法，结合人类和计算元素，同时也为申请人提供了严格的培训计划，申请人是一名MD-博士生，流行病学这个培训计划是严格的，协同而多样的，并将包括先进的课程，与该领域专家进行专门的一对一和小组辅导，参加研讨会，会议，与临床教育和专业发展的整合。该项目将是一个重要的向申请人成熟为独立的医生-科学家迈出一步。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Evaluating equity in performance of an electronic health record-based 6-month mortality risk model to trigger palliative care consultation: a retrospective model validation analysis.

评估基于电子健康记录的 6 个月死亡风险模型的绩效公平性以触发姑息治疗咨询：回顾性模型验证分析。

DOI：
10.1136/bmjqs-2022-015173
发表时间：
2023
期刊：
BMJ quality & safety
影响因子：
5.4
作者：
Teeple,Stephanie;Chivers,Corey;Linn,KristinA;Halpern,ScottD;Eneanya,Nwamaka;Draugelis,Michael;Courtright,Katherine
通讯作者：
Courtright,Katherine

Effects of Neighborhood-level Data on Performance and Algorithmic Equity of a Model That Predicts 30-day Heart Failure Readmissions at an Urban Academic Medical Center.

邻里级别数据对模型的性能和算法平等的影响，该模型可以预测城市学术医学中心30天心力衰竭的恢复。