Early Signs:digital phenotyping to identify digital biomarkers for predicting burnout and cognitive functioning in ED clinicians (Early Signs)
早期迹象:数字表型分析可识别数字生物标志物,用于预测 ED 临床医生的倦怠和认知功能(早期迹象)
基本信息
- 批准号:10884739
- 负责人:
- 金额:$ 74.25万
- 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:2021-07-15 至 2026-07-31
- 项目状态:未结题
- 来源:
- 关键词:Accident and Emergency departmentAffectBehavioralBiological MarkersBlood specimenCOVID-19 pandemicCardiovascular DiseasesChronic stressClinicalComplementComputing MethodologiesDataData AnalysesDetectionDevelopmentDiagnosticDigital biomarkerDisastersEmergency CareEmergency Department PhysicianEmergency NursingEmergency SituationEmergency department crowdingEmotionalEmotionsEnvironmentExposure toFaceFacial ExpressionFeedbackFoundationsGoalsHairHead MovementsHealthHealth PersonnelHealthcareHealthcare SystemsHigh PrevalenceHydrocortisoneImpaired cognitionIndividualInterventionInterviewKnowledgeMachine LearningMediatingMedical ErrorsMethodsModalityMonitorMovementMydriasisNursesOccupationsOutcomePathologyPatient CarePatient Self-ReportPatientsPatternPattern RecognitionPerformancePersonal SatisfactionPersonsPhenotypePhysiciansPhysiologicalPreventionPrevention strategyPrognosisPsyche structurePublic HealthReportingResearchRiskSamplingScheduleSecureSelf CareSelf ManagementSleepSocietiesSpeechStressStructureSymptomsTestingTrainingVertebral columnVideo RecordingVisitVoiceWell in selfWorkacute careanthropogenesisbiomarker panelburnoutcardiovascular disorder riskcardiovascular effectscardiovascular risk factorcare systemsclinical predictorsclinical riskclinically relevantcognitive functioncognitive performancecohortdeep learningdeep learning algorithmdeep neural networkdigitaldigital measureexecutive functionexperiencegazeimprovedinnovationlongitudinal, prospective studymortalitymultimodalitynovelpandemic diseasepredictive markerprognosticprognostic toolprognostic valueprognosticationprogramspsychologicpsychological outcomesresponsesafety netscreeningshift worksocialstressortransfer learning
项目摘要
Project Summary
The Emergency Department (ED) is a central pillar of the health care system. ED physicians and
nurses are exposed to high work-related stressors in addition to disruptive day-night shifts. The well-
being of the 150,000 ED clinicians in the U.S. is important for public health with significant
downstream effects on the well-being of the 145 million patients that are served every year. Of the 1
million U.S. physicians, 45% are reporting burnout symptoms and the number increases to 70% in
ED physicians and up to 82% in ED nurses. Clinician burnout is associated with increased risk for
cardiovascular diseases, mortality, medical errors, but also stress-mediated physiological alterations
and cognitive decline. Yet, the knowledge about the long-term development of burnout is limited. To
effectively intervene, the timely identification of ED clinicians at risk for burnout is a critical
prerequisite. This study proposes an innovative approach using digital phenotyping to discover and
test Digital Biomarkers as predictors of burnout symptoms and cognitive function.
This proposed prospective longitudinal study will chart cognitive functioning and burnout
symptom trajectories in a cohort of 350 ED clinicians to inform when, where, and how to intervene.
We will use advanced computational methods to extract objective markers for burnout and cognitive
decline from video and audio data. Based on our preliminary data, we hypothesize that voice and
speech content, head movement, pupil dilation, gaze, and facial landmark features of emotion
provide probabilistic information that will allow us to identify digital biomarkers for burnout and
cognitive function.
We will test the relevance of the discovered digital biomarkers and determine their discriminatory
accuracy to distinguish between risk for clinically relevant vs. non-relevant burnout symptoms. We
examine the association of digital biomarkers with physiological markers of chronic stress (i.e., hair
cortisol concentration). We will also assess the association of digital biomarkers with the long-term
job-related stress load of individual ED clinicians such as high ED crowding, patient acuity level, ED
staffing, sleep, and shift schedules. This research program aims to deliver an objective, accurate,
and reliable digital measure for clinician well-being. Such digital biomarkers will enable more efficient
ED clinician self-management and will promote low-threshold prevention strategies. The mental and
physical well-being of ED clinicians is of high value to those who work day in and day out to save the
lives of others and is the foundation of a well-functioning, high-quality emergency care system.
项目摘要
急诊科(ED)是卫生保健系统的核心支柱。急诊科医生和
除了颠覆性的昼夜轮班外,护士还面临着与工作相关的高压力因素。这口井-
成为美国150,000名急诊临床医生之一对公共卫生来说很重要,因为
对每年接受服务的1.45亿名患者的福祉产生下游影响。1个中的
百万美国医生中,45%的人报告有职业倦怠症状,这一数字在
急诊医生的比例高达82%,急诊护士的比例高达82%。临床医生职业倦怠与增加的风险有关
心血管疾病、死亡率、医疗差错,但也有压力介导的生理变化
和认知能力下降。然而,关于职业倦怠的长期发展的知识是有限的。至
有效的干预,及时识别有职业倦怠风险的急诊医生是至关重要的
必备条件。这项研究提出了一种创新方法,使用数字表型来发现和
测试数字生物标记物作为职业倦怠症状和认知功能的预测因子。
这项拟议的前瞻性纵向研究将绘制认知功能和职业倦怠的图表。
350名急诊临床医生队列中的症状轨迹,以告知何时、何地和如何干预。
我们将使用先进的计算方法来提取职业倦怠和认知的客观标志
从视频和音频数据开始下降。根据我们的初步数据,我们假设声音和
语音内容、头部运动、瞳孔放大、凝视和面部表情特征
提供概率信息,使我们能够识别倦怠的数字生物标记物
认知功能。
我们将测试发现的数字生物标记物的相关性,并确定它们的区分性
准确区分临床相关和不相关的倦怠症状的风险。我们
检查数字生物标记物与慢性应激的生理标记物(即头发)之间的关系
皮质醇浓度)。我们还将评估数字生物标志物与长期
急诊科临床医生个体的工作压力负荷,如急诊室拥挤程度、患者视力水平、急诊室
人员配备、睡眠和班次安排。这项研究计划旨在提供一个客观、准确、
和可靠的临床医生幸福感数字测量。这样的数字生物标记物将使
ED临床医生自我管理,并将推广低门槛预防策略。精神上的和
急诊科临床医生的身体健康对于那些日复一日地工作以挽救
这是一个运作良好、高质量的紧急护理系统的基础。
项目成果
期刊论文数量(8)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Associations between emergency department crowding and perceptions of interpersonal care in patients presenting with suspected acute coronary syndrome.
- DOI:10.1136/emermed-2020-210493
- 发表时间:2022-03
- 期刊:
- 影响因子:0
- 作者:
- 通讯作者:
Traumatic stress symptoms in family caregivers of patients with acute leukaemia: protocol for a multisite mixed methods, longitudinal, observational study.
- DOI:10.1136/bmjopen-2022-065422
- 发表时间:2022-11-04
- 期刊:
- 影响因子:2.9
- 作者:Jibb, Lindsay A.;Nanos, Stephanie M.;Alexander, Sarah;Malfitano, Carmine;Rydall, Anne;Gupta, Sumit;Schimmer, Aaron D.;Zimmermann, Camilla;Hales, Sarah;Nissim, Rinat;Marmar, Charles;Schultebraucks, Katharina;Mah, Kenneth;Rodin, Gary
- 通讯作者:Rodin, Gary
Oxytocin vs. placebo effects on intrusive memory consolidation using a trauma film paradigm: a randomized, controlled experimental study in healthy women.
催产素与安慰剂对侵入性记忆巩固的影响使用创伤膜范式:健康女性的一项随机,受控的实验研究。
- DOI:10.1038/s41398-023-02339-z
- 发表时间:2023-02-04
- 期刊:
- 影响因子:6.8
- 作者:Maslahati, Tolou;Wingenfeld, Katja;Hellmann-Regen, Julian;Kraft, Julia;Lyu, Jing;Keinert, Marie;Voss, Aline;Cho, An Bin;Ripke, Stephan;Otte, Christian;Schultebraucks, Katharina;Roepke, Stefan
- 通讯作者:Roepke, Stefan
Intranasal oxytocin administration impacts the acquisition and consolidation of trauma-associated memories: a double-blind randomized placebo-controlled experimental study in healthy women.
- DOI:10.1038/s41386-021-01247-4
- 发表时间:2022-04
- 期刊:
- 影响因子:0
- 作者:Schultebraucks K;Maslahati T;Wingenfeld K;Hellmann-Regen J;Kraft J;Kownatzki M;Behnia B;Ripke S;Otte C;Roepke S
- 通讯作者:Roepke S
Evaluation of emergency department visits for mental health complaints during the COVID-19 pandemic.
- DOI:10.1002/emp2.12728
- 发表时间:2022-06
- 期刊:
- 影响因子:2.3
- 作者:
- 通讯作者:
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Katharina Schultebraucks其他文献
Katharina Schultebraucks的其他文献
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{{ truncateString('Katharina Schultebraucks', 18)}}的其他基金
Point-of-care prognostic modeling of PTSD risk after traumatic event exposure using digital biomarkers and clinical data from electronic health records in the emergency department setting (PREDICT)
使用数字生物标志物和急诊科电子健康记录中的临床数据对创伤事件暴露后的 PTSD 风险进行护理点预后建模 (PREDICT)
- 批准号:
10884738 - 财政年份:2023
- 资助金额:
$ 74.25万 - 项目类别:
Early Signs: digital phenotyping to identify digital biomarkers for predicting burnout and cognitive functioning in ED clinicians
早期迹象:通过数字表型分析来识别数字生物标志物,以预测急诊临床医生的倦怠和认知功能
- 批准号:
10298751 - 财政年份:2021
- 资助金额:
$ 74.25万 - 项目类别:
Early Signs: digital phenotyping to identify digital biomarkers for predicting burnout and cognitive functioning in ED clinicians
早期迹象:通过数字表型分析来识别数字生物标志物,以预测急诊临床医生的倦怠和认知功能
- 批准号:
10449250 - 财政年份:2021
- 资助金额:
$ 74.25万 - 项目类别:
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