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，人工智能（AI）技术（例如机器学习（ML））可以部署到现实世界中时，会使社会不平等永久化甚至担心。这是在许多领域中证明，包括政策，法院系统，银行业，社会服务规定以及越来越关注医学也是如此。同时，新的基于AI的干预措施，食品和药物管理人数增加了十倍（FDA）自2017年以来对基于AI的技术的批准。但是，很少的研究经验研究了健康基于ML的临床决策工具的平等影响。一个基于ML的工具是一个临床领域已经使用的是急诊科（ED）Triage，作为常见紧急严重性的替代方案索引（ESI）系统。尽管有广泛的知名度，但有证据表明，基于ESI的Triage有很多问题，包括敏锐歧视不良，多达50％的患者在量表的中点分类，并且与种族不平等有关，非裔美国人患者的等待时间更长，控制疾病严重程度的较低分类水平。这项研究将使用基于ML的ED Triage工具在美国一个主要的学术医学中心使用，以探索几个因素的程度与患者种族/族裔群体的预测表现不平等有关。这项研究会采用混合方法的方法，同时检查影响该元素的人类和“机器”元素 Triage工具对患者的最终影响。 AIM 1将是一项定性研究，涉及人种学观察和半结构式访谈分类护士，以为临床医生的理解开发一个概念框架与基于ML的工具的交互。 AIM 2将检查一种测量偏差的“标签偏见”。这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，然后检查跨患者种族/族裔群体的分类工具的预测性能。目标3意志探索不同的方法来推出丢失的EHR数据。申请人将部署常见，简单基于删除的方法以及一种有希望的新基于ML的插补方法，称为自动编码器，应用分诊模型在患者种族/族裔群体中产生预测和考试表现。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”模型以及他们使用混合方法的方法来融合人类和计算元素，同时还为申请人提供了严格的培训计划流行病学。该培训计划是严格的，协同的但潜水员的，将包括高级课程，专门与该领域的专家一起专门的1对1和小组心理，参加下水道并针对目标会议，与临床教育和专业发展的融合。这个项目将是必不可少的迈向申请人的成熟到独立的身体科学家。

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.