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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：
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
项目类别：
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

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.

项目总结