Understanding and Addressing Disparities in Triage and Disposition Decisions in the Emergency Department

了解并解决急诊科分诊和处置决策中的差异

• 批准号: 10510091
• 负责人: Mehul D. Patel
• 金额: $ 9.99万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10510091
• 关键词: Understanding; Addressing; Disparities; Triage; Disposition

Abstract There are nearly 150 million emergency department (ED) visits in the United States each year. Patients present to EDs for a wide range of health problems and acuities from life-threatening emergencies to ambulatory conditions. Further, the ED patient population is highly diverse with respect to demographics, cultural, and socioeconomic factors. Providing equitable care in the high-volume, time-constrained, and diverse ED setting has proven to be a challenge with growing evidence of disparities in clinical decision making and health care delivery for racial and ethnic minorities and women. Triage and disposition decisions involve some subjectivity and are especially prone to bias. Our preliminary analyses of electronic health record (EHR) data from a single academic ED found evidence of disparities in ED triage, prioritization for rooming, and hospital admission decisions. Our long-term research goal is to fully characterize gender, racial, and ethnic disparities in ED triage and disposition decisions in the United States and to apply statistical and machine learning methods to develop and evaluate innovative solutions to mitigate these disparities. Our research will be embedded within a Learning Health System that integrates scientific evidence, internal data, and stakeholder engagement to improve equity of healthcare delivery in the ED. As an initial step, we will obtain and analyze retrospective EHR data from 10 diverse EDs across a large health system. Our aims are to: (1) identify patient gender, racial, and ethnic disparities in ED decisions (triage level assignment, rooming priority, and hospital admission) and determine whether ED operating conditions (e.g., volumes, wait times) exacerbate these disparities; and (2) develop a prototype machine learning model that integrates patient- and ED-level data to predict potentially inequitable decision making in the ED. Upon successful completion of this pilot project, we will have obtained essential preliminary evidence to fully develop a novel machine learning prediction model and validate the model in multiple Learning Health Systems. In future research, we also intend to investigate potential applications of the machine learning model of inequitable deicison making, such as a point-of-care tool to alert ED providers and a data monitoring and reporting feedback system for ED providers and administrators and health system leaders. Findings from this research has the potential to lead to innovative data-driven solutions to promote equitable patient-centered care for the millions who present to EDs each year.

摘要 在美国，每年有近1.5亿次急诊（艾德）就诊。患者 向急诊科医生介绍各种健康问题和急症，从危及生命的紧急情况到 流动条件。此外，艾德患者群体在人口统计学方面高度多样化， 文化和社会经济因素。在高容量，时间有限和多样化的情况下提供公平的护理 艾德设置已被证明是一个挑战，越来越多的证据表明，临床决策的差异， 为少数民族和妇女提供保健服务。分类和处置决定涉及一些 主观性，特别容易产生偏见。我们对电子健康记录（EHR）数据的初步分析 从一个单一的学术艾德发现证据的差异，艾德分诊，优先排序的房间，和医院 录取决定。我们的长期研究目标是充分描述性别，种族和民族差异 在美国的艾德分诊和处置决策中， 开发和评估创新解决方案以缩小这些差距的方法。我们的研究将是 嵌入学习健康系统，整合科学证据，内部数据和利益相关者 参与，以提高医疗保健服务的公平性在ED。作为第一步，我们将获得和分析 来自大型卫生系统中10个不同ED的回顾性EHR数据。我们的目标是：（1）识别患者 艾德决策中的性别、种族和民族差异（分诊级别分配、房间优先级和医院 准入）并确定艾德操作条件（例如，数量、等待时间）加剧了这些问题 差异;（2）开发一个原型机器学习模型，将患者和ED水平的数据整合到 预测教育署可能作出不公平的决定。在成功完成这项试验计划后，我们 将获得必要的初步证据，以充分开发新的机器学习预测模型 并在多个学习健康系统中验证该模型。在未来的研究中，我们还打算调查 不公平决策的机器学习模型的潜在应用，例如护理点 提醒艾德提供者的工具，以及用于艾德提供者的数据监测和报告反馈系统， 管理人员和卫生系统领导人。这项研究的结果有可能导致创新 数据驱动的解决方案，以促进公平的以病人为中心的护理，为数百万谁提出的ED每年。

项目成果

Patient sex, racial and ethnic disparities in emergency department triage: A multi-site retrospective study.

急诊科分诊中的患者性别、种族和民族差异：一项多地点回顾性研究。

• DOI: 10.1016/j.ajem.2023.11.008
• 发表时间: 2024
• 期刊: The American journal of emergency medicine
• 作者: Patel,MehulD;Lin,Peter;Cheng,Qian;Argon,NilayT;Evans,ChristopherS;Linthicum,Benjamin;Liu,Yufeng;Mehrotra,Abhi;Murphy,Laura;Ziya,Serhan
• 通讯作者: Ziya,Serhan