Clinical Decision Support Tool to Assess Risk and Prevent Agitation Events
用于评估风险和预防躁动事件的临床决策支持工具
基本信息
- 批准号:10683499
- 负责人:
- 金额:$ 10.8万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-09-15 至 2025-08-31
- 项目状态:未结题
- 来源:
- 关键词:Accident and Emergency departmentAddressAggressive behaviorAgitationAwardBehavioralBiometryCharacteristicsClinicalClinical Decision Support SystemsClinical TrialsCollectionComplexDataDissemination and ImplementationEarly DiagnosisEarly treatmentEducationElectronic Health RecordEmergency Department PhysicianEmergency MedicineEmergency department visitEnrollmentEnsureEnvironmentEnvironmental Risk FactorEquilibriumEventFaceFoundationsGoalsHarm ReductionHealth Services ResearchHealth systemInstitutionInterventionInterviewKnowledgeLaboratoriesLeadMedicalMental HealthMental Health ServicesMental disordersMentorsMethodsModelingModificationNational Institute of Mental HealthOutcomePatient riskPatient-Focused OutcomesPatientsPhysical RestraintPoliciesPredictive FactorPreventionProcessProviderPublic Health InformaticsQualitative MethodsRandomizedRecommendationRecording of previous eventsResearchResearch ActivityResourcesRiskRisk AssessmentRisk FactorsSafetySelf EfficacySudden DeathSymptomsSystemTechniquesTechnologyTestingTimeTrainingTraumatic injuryValidationVariantViolenceVisitWorkacute carebasecare deliverycareerclinical centerclinical decision supportclinical decision-makingclinical developmentclinical research siteclinical trial implementationcohortcomorbidityemergency settingsfeasibility testinghealth recordimprovedpatient safetypilot trialpredictive modelingpreemptive interventionpreventrapid diagnosisrestraintrisk prediction modelrisk stratificationsedativesevere mental illnessskillssupport toolstoolusabilityuser centered design
项目摘要
PROJECT SUMMARY/ABSTRACT
This award is a four-year plan to support Ambrose Wong, MD, MSEd, an emergency physician, in his
transition towards an independent research career that focuses on dissemination and implementation of
services for mental health crises in general, non-psychiatric emergency settings. The long-term goal of his
research is to improve safety related to symptoms of agitation. To date, Dr. Wong's training has focused on
emergency medicine, qualitative methods, and education-based interventions. Under a team of co-mentors
with expertise in biostatistics, psychiatric services research, health informatics, and clinical trial implementation,
Dr. Wong will build on his preliminary work on agitation prevention and management to accomplish the
following training goals: (1) acquire expertise in clinical prediction modeling, (2) gain foundational knowledge in
preventing, treating, and investigating mental health crises, (3) study health informatics and development of
clinical support tools, and (4) obtain fundamental skills in clinical trials. The application integrates formal
coursework and training through mentored research activities.
Behavioral conditions in acute care settings are rapidly rising in the U.S., with a 50% increase in number of
general emergency department (ED) visits for mental health conditions over the past decade. Agitation,
defined as excessive psychomotor activity leading to violent behavior, is often part of these patient encounters.
Of the 1.7 million agitation episodes occurring annually in general EDs, 83% are associated with an underlying
serious mental illness. Given the safety risks of agitation, clinicians commonly use physical restraint, which are
associated with up to 37% risk of complications including traumatic injuries and even sudden death in patients.
Thus, regulatory bodies and experts emphasize early risk assessment and use of behavioral techniques before
agitation occurs. However, variability in practice and policy of these techniques exists in emergency settings.
This is due to lack of knowledge regarding specific risk factors that predict the need for pre-emptive
intervention and challenges in assessing these risk factors in the busy environment of an ED. The objective of
this project is to develop and test the Early Detection and Treatment to Reduce Events with Agitation Tool (ED-
TREAT), a clinical decision support system embedded in the electronic health record that will guide clinicians
in early risk assessment and appropriate treatment of mental health patients likely to develop agitation. We will
first derive a clinical model using health record data and preliminary analyses by our team that predicts which
at-risk patients will develop agitation and require use of physical restraint. Next, we will develop and refine ED-
TREAT through user-centered design techniques with clinicians and patients. Finally, we will conduct a pilot
trial to test the feasibility, fidelity, and bedside acceptability of ED-TREAT. Aligned with NIMH's strategic
priorities to use technology for improving mental health delivery in non-specialty settings, this study will form
the basis for a subsequent full-scale clinical trial to examine ED-TREAT's effect on clinical outcomes.
项目总结/摘要
这个奖项是一个为期四年的计划,以支持黄汉铨,医学博士,MSEd,急诊医生,在他的
过渡到一个独立的研究生涯,重点是传播和实施
一般精神健康危机服务,非精神病紧急情况。他的长期目标是
研究是为了提高与焦虑症状相关的安全性。迄今为止,王博士的培训重点是
急诊医学、定性方法和基于教育的干预措施。在一群共同导师的指导下
具有生物统计学、精神病服务研究、健康信息学和临床试验实施方面的专业知识,
博士黄先生将以他在预防及管理煽动方面的初步工作为基础,
以下培训目标:(1)获得临床预测建模的专业知识,(2)获得
预防,治疗和调查心理健康危机,(3)研究健康信息学和发展
临床支持工具,以及(4)获得临床试验的基本技能。该应用程序集成了正式的
通过指导研究活动开展课程和培训。
在美国,急性护理环境中的行为状况正在迅速上升,增加了50%,
在过去的十年里,一般急诊科(艾德)因精神健康状况而就诊。激动,
被定义为导致暴力行为的过度精神活动,通常是这些患者遭遇的一部分。
在一般ED中每年发生的170万次激越发作中,83%与潜在的
严重的精神疾病。考虑到躁动的安全风险,临床医生通常使用身体约束,这是
与高达37%的并发症风险相关,包括患者的创伤性损伤甚至猝死。
因此,监管机构和专家强调早期风险评估和使用行为技术,
发生了骚动。然而,在紧急情况下,这些技术的做法和政策存在差异。
这是由于缺乏关于预测是否需要先发制人的特定风险因素的知识。
在急诊室忙碌的环境中评估这些风险因素的干预和挑战。的目标
该项目是开发和测试早期检测和治疗,以减少事件与激动工具(艾德-
TREAT),这是一个嵌入电子健康记录的临床决策支持系统,将指导临床医生
在早期风险评估和适当的治疗精神健康患者可能发展激动。我们将
首先使用健康记录数据和我们团队的初步分析得出临床模型,
处于危险中的病人会出现躁动,需要使用身体约束。接下来,我们将开发和完善艾德-
通过以用户为中心的设计技术与临床医生和患者进行治疗。最后,我们将进行试点
试验,以测试的可行性,保真度,和床旁可接受的ED-TREAT。
优先使用技术来改善非专业环境中的心理健康服务,这项研究将形成
为随后的全面临床试验研究ED-TREAT对临床结果的影响奠定了基础。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Ambrose H Wong其他文献
Ambrose H Wong的其他文献
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{{ truncateString('Ambrose H Wong', 18)}}的其他基金
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 - 财政年份:2022
- 资助金额:
$ 10.8万 - 项目类别:
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:
10633167 - 财政年份:2022
- 资助金额:
$ 10.8万 - 项目类别:
Clinical Decision Support Tool to Assess Risk and Prevent Agitation Events
用于评估风险和预防躁动事件的临床决策支持工具
- 批准号:
10365272 - 财政年份:2021
- 资助金额:
$ 10.8万 - 项目类别:
Clinical Decision Support Tool to Assess Risk and Prevent Agitation Events
用于评估风险和预防躁动事件的临床决策支持工具
- 批准号:
10687170 - 财政年份:2021
- 资助金额:
$ 10.8万 - 项目类别:
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