Characterizing Bias and Care Disparities with Physical Restraint Use in the Emergency Setting Using Natural Language and Cognitive Data

使用自然语言和认知数据描述紧急情况下使用身体约束的偏见和护理差异

• 批准号: 10431043
• 负责人: Ambrose H Wong
• 金额: $ 25.13万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-02 至 2024-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10431043
• 关键词: Accident and Emergency department; Address; Affect; Aggressive behavior; Agitation; Algorithms; Area; Artificial Intelligence; Asphyxia; Behavioral; Black race; Caring; Censuses; Cessation of life; Clinical; Coercion; Cognitive; Complement; Computers; Cross-Sectional Studies; Cues; Data; Data Element; Databases; Decision Making; Discipline; Discrimination; Electronic Health Record; Emergency Care; Emergency Department patient; Emergency Situation; Emergency department visit; Environment; Event; Focus Groups; Health Disparities Research; Health Personnel; Health Services Research; Health system; Healthcare Systems; Homelessness; Human; Individual; Informatics; Insurance; Interdisciplinary Study; Interpersonal Relations; Interview; Investments; Language; Lead; Maps; Measures; Mediating; Mediation; Mediator of activation protein; Methods; Minority; Minority Groups; Modeling; Natural Language Processing; Nurses; Patient Care; Patients; Physical Restraint; Physicians; Population; Population Analysis; Populations at Risk; Prevalence; Process; Psyche structure; Qualitative Methods; Research; Research Methodology; Risk; Risk Factors; Safety; Security; Structure; Substance Use Disorder; System; Techniques; Testing; Text; Therapeutic; Time; Training; Trauma; Trust; Ventilatory Depression; Violence; Visit; Work; analytical method; base; behavioral health; black patient; clinical decision-making; cognitive task; deep learning model; emergency settings; executive function; experience; health disparity; heuristics; high risk; innovation; interdisciplinary approach; low socioeconomic status; marginalized population; minority health disparity; natural language; novel; prevent; public health insurance; restraint; severe mental illness; social culture; socioeconomic disadvantage; structured data; task analysis; therapy development; tool

PROJECT SUMMARY Agitation is defined as excessive psychomotor activity leading to aggressive and violent behavior in patients. Those presenting with agitation in the emergency setting represent the most marginalized populations. Coercive measures like physical restraints are currently used routinely on agitated individuals, but are associated with physical trauma, respiratory depression, and death. Recent studies have shown disproportionate use of physical restraint on Black patients, those who are homeless, and those with public or no insurance. Identification of specific interpersonal and structural factors that affect heuristics and decision- making of healthcare workers regarding restraint use is needed to mitigate systemic bias and discrimination against these marginalized patients. However, current research is limited to analyzing structured quantitative data elements, while narrative text better reflects sociocultural contexts, interpersonal interactions, and clinician thought processes. Natural language processing is an informatics discipline that can parse free text within clinical notes into quantifiable variables on a large scale that can be combined with mediation analysis to uncover factors leading to disparities in restraint use. A complementary tool is cognitive task analysis, which uses qualitative methods to understand how mediators of bias affect clinical decision-making at the bedside. Our overall objective is to use the combination of these innovative analytical methods to overcome deficiencies of standard health service research methods in identifying individual, interpersonal, institutional, and systems factors leading to bias in physical restraint use. In Aim 1, we will use natural language processing and mediation analysis on a large database of emergency department clinical narrative notes across our regional healthcare system to extract and identify candidate variables that lead minority populations to increased risk of physical restraint. This will allow us to verify and test potential factors within our newly derived conceptual model of bias during restraint use that predict discriminatory practices. In Aim 2, we will use cognitive task analyses through qualitative interviews and video-informed focus groups with emergency healthcare workers to characterize drivers and cues that influence decision-making and heuristics against minority populations. This will complement the results from Aim 1 by providing explanatory models for how interpersonal and structural factors that lead to bias are manifested at the bedside. This proposed work will make a positive contribution to minority and health disparities research in the emergency setting by identifying specific interpersonal and structural factors mediating bias and discrimination against minority and socioeconomically disadvantaged individuals with behavioral emergencies. Our study is exploratory and novel as it combines innovative and multidisciplinary approaches from two complementary scientific fields to address multiple system levels of influence within the healthcare system in an understudied area for emergency care where significant harm is occurring for marginalized populations.

项目总结 激动被定义为过度的精神运动活动，导致患者的攻击性和暴力行为。 那些在紧急情况下出现骚动的人代表了最边缘化的人群。 强制措施，如身体束缚，目前通常用于焦虑不安的个人，但 与身体创伤、呼吸抑制和死亡有关。最近的研究表明 不成比例地对黑人患者、无家可归者和有公共或 没有保险。确定影响启发式和决策的特定人际和结构因素-- 需要对医护人员进行有关约束使用的培训，以减轻系统性偏见和歧视 针对这些被边缘化的患者。然而，目前的研究仅限于对结构化的量化分析 数据元素，而叙述性文本更好地反映了社会文化背景、人际互动和 临床医生的思维过程。自然语言处理是一门能够分析自由文本的信息学学科 将临床记录转化为可量化的大范围变量，可与中介分析相结合 发现导致节制使用差异的因素。一个补充工具是认知任务分析，它 使用定性的方法来理解偏见的中介如何影响床边的临床决策。 我们的总体目标是使用这些创新分析方法的组合来克服不足 在确定个人、人际、机构和系统方面的标准卫生服务研究方法 导致身体束缚使用偏颇的因素。在目标1中，我们将使用自然语言处理和 我区急诊科临床病历大型数据库的中介分析 医疗保健系统提取和识别导致少数族裔人群罹患糖尿病风险增加的候选变量 身体上的约束。这将使我们能够验证和测试新派生的概念中的潜在因素 在约束使用过程中预测歧视性做法的偏见模型。在目标2中，我们将使用认知任务 通过定性访谈和视频了解情况的焦点小组与急救医护人员进行分析，以 描述影响决策的驱动因素和线索以及针对少数群体的启发式方法。这 将通过提供解释模型来补充目标1的结果，解释人际关系和结构 导致偏见的因素在床边表现出来。 这项拟议的工作将对中国少数民族和健康差距的研究做出积极贡献 通过确定调解偏见和歧视的特定人际和结构性因素的紧急情况 针对有行为紧急情况的少数民族和社会经济上处于不利地位的个人。我们的研究是 探索性和新颖性，因为它结合了来自两个互补的 在未充分研究的情况下，解决医疗保健系统内多个系统层面影响的科学领域 对边缘人群造成重大伤害的紧急护理区域。