Characterizing Bias and Care Disparities with Physical Restraint Use in the Emergency Setting Using Natural Language and Cognitive Data
Characterizing Bias and Care Disparities with Physical Restraint Use in the Emergency Setting Using Natural Language and Cognitive Data
批准号:
10633167
负责人:
Ambrose H Wong
金额:
$20.94万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-06-02 至 2025-02-28
关键词:
Accident and Emergency departmentAddressAffectAggressive behaviorAgitationAlgorithmsAreaArtificial IntelligenceAsphyxiaBehavioralBlack raceCaringCensusesCessation of lifeClinicalCoercionCognitiveComplementComputersCross-Sectional StudiesCuesDataData ElementDatabasesDecision MakingDevelopmentDisciplineDiscriminationDisparateDisparityDisparity populationEconomically Deprived PopulationElectronic Health RecordEmergency CareEmergency Department patientEmergency SituationEmergency department visitEnvironmentEventFocus GroupsHealth Disparities ResearchHealth PersonnelHealth Services ResearchHealth StatusHealth systemHealthcare SystemsHomelessnessHumanIndividualInformaticsInstitutionInsuranceInterdisciplinary StudyInterpersonal RelationsInterventionInterviewInvestmentsLanguageLeadMapsMeasuresMediatingMediationMediatorMethodsMinorityMinority GroupsModelingNatural Language ProcessingNursesPatient CarePatientsPhysical RestraintPhysiciansPopulation AnalysisPopulations at RiskPrevalenceProcessPsyche structureQualitative MethodsResearchResearch MethodologyRiskRisk FactorsSafetySecurityStructureSubstance Use DisorderSystemTechniquesTestingTextTherapeuticTimeTrainingTraumaTrustVentilatory DepressionViolenceVisitWorkWritinganalytical methodbehavioral healthblack patientcandidate identificationclinical decision-makingcognitive taskdeep learning modelemergency settingsexecutive functionexperienceheuristicshigh riskinnovationinterdisciplinary approachlow socioeconomic statusmarginalizationmarginalized populationminority disparityminority healthminority health disparitynatural languagenovelpreventpublic health insurancerestraintsevere mental illnesssocial culturesociocultural determinantsocioeconomic disadvantagestructural determinantsstructured datatask analysistherapy developmenttool
中文摘要
项目摘要
躁动是指过度的精神活动导致患者的攻击性和暴力行为。
那些在紧急情况下表现得焦躁不安的人是最边缘化的群体。
强制措施,如身体限制,目前经常用于激动的个人,但
与身体创伤、呼吸抑制和死亡有关。最近的研究表明
对黑人病人、无家可归者和有公共或私人设施的人不成比例地使用身体约束,
没有保险。识别影响物流和决策的具体人际和结构因素-
需要对医务工作者进行约束使用方面的培训,以减轻系统性偏见和歧视
这些边缘化的病人。然而,目前的研究仅限于分析结构化的定量,
数据元素,而叙事文本更好地反映了社会文化背景,人际互动,
临床思维过程自然语言处理是一门可以解析自由文本的信息学学科
在临床笔记中,可以大规模地将其转化为可量化的变量,这些变量可以与中介分析相结合,
发现导致使用约束手段方面差异的因素。一个补充工具是认知任务分析,
使用定性的方法来了解偏见的介质如何影响临床决策在床边。
我们的总体目标是结合使用这些创新的分析方法来克服不足之处
标准的卫生服务研究方法,以确定个人,人际,机构和系统
导致身体约束使用偏差的因素。在目标1中,我们将使用自然语言处理,
对本地区急诊科临床叙述性记录大型数据库的中介分析
医疗保健系统,以提取和识别候选变量,导致少数群体的风险增加,
身体约束。这将使我们能够验证和测试潜在的因素,在我们新得出的概念
约束使用期间的偏见模型,预测歧视性做法。在目标2中,我们将使用认知任务
通过定性访谈和视频知情的焦点小组与紧急医疗保健工作者进行分析,
描述影响针对少数群体的决策和策略的驱动因素和线索。这
我将通过提供解释性模型来补充目标1的结果,
导致偏倚的因素在床旁表现出来。
这项拟议的工作将对少数民族和健康差距研究作出积极贡献,
通过确定引起偏见和歧视的具体人际和结构因素,
针对行为紧急的少数群体和社会经济弱势群体。我们的研究是
探索性和新颖性,因为它结合了两个互补的创新和多学科方法,
科学领域,以解决医疗保健系统内的多个系统层次的影响,
紧急护理区域,对边缘化人群造成重大伤害。
英文摘要
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.
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会议论文
Characterizing Bias and Care Disparities with Physical Restraint Use in the Emergency Setting Using Natural Language and Cognitive Data
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批准号:10431043
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项目类别:
-
资助金额:$25.13万
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财政年份:2022
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负责人:Ambrose H Wong
-
依托单位:
Clinical Decision Support Tool to Assess Risk and Prevent Agitation Events
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批准号:10683499
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项目类别:
-
资助金额:$10.8万
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财政年份:2021
-
负责人:Ambrose H Wong
-
依托单位:
Clinical Decision Support Tool to Assess Risk and Prevent Agitation Events
-
批准号:10365272
-
项目类别:
-
资助金额:$19.38万
-
财政年份:2021
-
负责人:Ambrose H Wong
-
依托单位:
Clinical Decision Support Tool to Assess Risk and Prevent Agitation Events
-
批准号:10687170
-
项目类别:
-
资助金额:$19.38万
-
财政年份:2021
-
负责人:Ambrose H Wong
-
依托单位:
海外基金