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.

抽象的 每年在美国有近1.5亿个急诊室（ED）访问。患者 出现在EDS中，从威胁生命的紧急情况到 门诊条件。此外，ED患者人口在人口统计方面高度多样化， 文化和社会经济因素。在大量，时期且多样化的高量中提供公平护理 事实证明，ED设置在临床决策和 种族和少数民族和妇女的医疗保健提供。分类和倾向决定涉及一些 主观性，尤其容易出现偏见。我们对电子健康记录（EHR）数据的初步分析 从一个学术的埃德（Ed 入学决定。我们的长期研究目标是充分表征性别，种族和种族差异 在美国的ED分类和分配决策中，并应用统计和机器学习 开发和评估创新解决方案以减轻这些差异的方法。我们的研究将是 嵌入到整合科学证据，内部数据和利益相关者的学习卫生系统中 参与急诊室的医疗保健交付平等。作为第一步，我们将获得并分析 回顾性EHR数据来自大型卫生系统的10种不同的ED。我们的目标是：（1）确定患者 ED决定中的性别，种族和种族差异（分类水平分配，优先级和医院 入院）并确定ED操作条件（例如，体积，等待时间）是否加剧了这些 差异； （2）开发一种原型机器学习模型，将患者和ED级数据集成到 预测ED中潜在的不平等决策。成功完成此试点项目后，我们 将获得基本的初步证据，以充分开发新的机器学习预测模型 并验证多个学习卫生系统中的模型。在未来的研究中，我们还打算调查 机器学习模型的潜在应用是不平等的Deicison制造模型，例如护理点 提醒ED提供商以及针对ED提供商和报告的数据监视和报告反馈系统的工具 管理员和卫生系统领导者。这项研究的发现有可能导致创新 数据驱动的解决方案，以促进每年出席EDS的数百万人以公平为中心的护理。

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