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

基本信息

批准号：
10633167
负责人：
Ambrose H Wong
金额：
$ 20.94万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-02 至 2025-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10633167
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 Development Discipline Discrimination Disparate Disparity Disparity population Economically Deprived Population 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 Status Health system Healthcare Systems Homelessness Human Individual Informatics Institution Insurance Interdisciplinary Study Interpersonal Relations Intervention Interview Investments Language Lead Maps Measures Mediating Mediation Mediator Methods Minority Minority Groups Modeling Natural Language Processing Nurses Patient Care Patients Physical Restraint Physicians 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 Writing analytical method behavioral health black patient candidate identification clinical decision-making cognitive task deep learning model emergency settings executive function experience heuristics high risk innovation interdisciplinary approach low socioeconomic status marginalization marginalized population minority disparity minority health minority health disparity natural language novel prevent public health insurance restraint severe mental illness social culture sociocultural determinant socioeconomic disadvantage structural determinants structured data task analysis therapy development tool

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 Development Discipline Discrimination Disparate Disparity Disparity population Economically Deprived Population 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 Status Health system Healthcare Systems Homelessness Human Individual Informatics Institution Insurance Interdisciplinary Study Interpersonal Relations Intervention Interview Investments Language Lead Maps Measures Mediating Mediation Mediator Methods Minority Minority Groups Modeling Natural Language Processing Nurses Patient Care Patients Physical Restraint Physicians 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 Writing analytical method behavioral health black patient candidate identification clinical decision-making cognitive task deep learning model emergency settings executive function experience heuristics high risk innovation interdisciplinary approach low socioeconomic status marginalization marginalized population minority disparity minority health minority health disparity natural language novel prevent public health insurance restraint severe mental illness social culture sociocultural determinant socioeconomic disadvantage structural determinants structured data task analysis therapy development tool

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项目摘要

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.

项目摘要搅动被定义为过度的心理运动活性，导致患者的侵略性和暴力行为。在紧急情况下表现出躁动的人是最边缘化的人群。当前，诸如身体限制之类的强制措施通常用于搅动的个体，但是与身体创伤，呼吸道抑郁和死亡有关。最近的研究表明对黑人患者，无家可归者以及有公共或有公共或有公共或没有保险。识别影响启发式和决策的特定人际交往和结构因素需要制造医疗保健工人关于约束用途的工作，以减轻系统性偏见和歧视针对这些边缘化患者。但是，当前的研究仅限于分析结构化定量数据元素，而叙事文本更好地反映了社会文化背景，人际关系和临床医生思考过程。自然语言处理是一门信息学科，可以解析免费文本在临床注释中，在可量化的变量中大规模的变量，可以与中介分析结合到发现导致约束使用差异的因素。互补工具是认知任务分析，使用定性方法来了解偏见介体如何影响床边的临床决策。我们的总体目标是利用这些创新分析方法的组合来克服缺陷标准卫生服务研究方法，以识别个人，人际，机构和系统导致身体约束使用偏见的因素。在AIM 1中，我们将使用自然语言处理和大型急诊科临床叙事注释的调解分析医疗保健系统提取和确定候选变量，导致少数群体增加的风险身体约束。这将使我们能够在新派生的概念上验证和测试潜在因素在约束使用过程中的偏见模型，以预测歧视性实践。在AIM 2中，我们将使用认知任务通过定性访谈和与紧急医疗保健工作者的视频焦点小组进行分析特征的驱动因素和线索影响了对少数群体的决策和启发式方法。这通过提供有关人际交往和结构的解释模型，可以补充AIM 1的结果导致偏见的因素在床边表现出来。这项拟议的工作将为少数群体和健康差异研究做出积极的贡献通过确定介导偏见和歧视的特定人际交往和结构因素来进行紧急设置反对少数群体和社会经济上的处于行为紧急情况的人。我们的研究是探索性和新颖性，因为它结合了两种互补的创新和多学科方法科学领域，以解决医疗保健系统中多个系统影响的研究紧急护理区域发生了严重的伤害对边缘化人群的危害。