CDR Administrative Supplement for COVID-19 Impacted NIMH Research

针对受新冠肺炎 (COVID-19) 影响的 NIMH 研究的 CDR 行政补充

基本信息

批准号：
10617502
负责人：
Edwin D Boudreaux
金额：
$ 29.49万
依托单位：
UNIV OF MASSACHUSETTS MED SCH WORCESTER
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：
2022-05-31 至 2024-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10617502
关键词：
Accident and Emergency department Address Administrative Supplement Adopted Adoption Adult Algorithms Behavior Behavioral Blinded Blunt Trauma COVID-19 impact Cervical spine Clinical Confusion Consultations Data Derivation procedure Detection Diagnostic Electronic Health Record Emergency Department Physician Emergency Department patient Emergency Department-based Intervention Emergency Medicine Emergency Nursing Emergency Situation Emergency department visit Engineering Enrollment Ensure Environment Evaluation Fright Funding Healthcare In Situ Individual Industrialization Industry Standard Informatics Injury Inpatients Interview Joints Literature Machine Learning Measures Medical Medical Records Methods Modeling National Institute of Mental Health Nurses Outcome Outpatients Participant Patient Care Patients Performance Physicians Predictive Analytics Procedures Prospective Studies Psychologist Published Comment Research Research Assistant Research Personnel Research Training Resources Risk Risk Assessment Safety Sampling Screening Result Self-Direction Stratification Suicide Suicide prevention Sum System Testing Training Translations Triage Validation Variant Visit base clinical care clinical practice common rule cost design economic cost economic evaluation effective intervention electronic data evidence base evidence based guidelines follow-up health care economics high risk implementation barriers improved indexing inferential statistics innovation learning strategy models and simulation novel prevent retention rate risk stratification screening secondary outcome simulation suicidal behavior suicidal patient suicidal risk trend

Accident and Emergency department Address Administrative Supplement Adopted Adoption Adult Algorithms Behavior Behavioral Blinded Blunt Trauma COVID-19 impact Cervical spine Clinical Confusion Consultations Data Derivation procedure Detection Diagnostic Electronic Health Record Emergency Department Physician Emergency Department patient Emergency Department-based Intervention Emergency Medicine Emergency Nursing Emergency Situation Emergency department visit Engineering Enrollment Ensure Environment Evaluation Fright Funding Healthcare In Situ Individual Industrialization Industry Standard Informatics Injury Inpatients Interview Joints Literature Machine Learning Measures Medical Medical Records Methods Modeling National Institute of Mental Health Nurses Outcome Outpatients Participant Patient Care Patients Performance Physicians Predictive Analytics Procedures Prospective Studies Psychologist Published Comment Research Research Assistant Research Personnel Research Training Resources Risk Risk Assessment Safety Sampling Screening Result Self-Direction Stratification Suicide Suicide prevention Sum System Testing Training Translations Triage Validation Variant Visit base clinical care clinical practice common rule cost design economic cost economic evaluation effective intervention electronic data evidence base evidence based guidelines follow-up health care economics high risk implementation barriers improved indexing inferential statistics innovation learning strategy models and simulation novel prevent retention rate risk stratification screening secondary outcome simulation suicidal behavior suicidal patient suicidal risk trend

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项目摘要

ABSTRACT Significance: As recent national controversy over Joint Commission mandates proves, universal suicide risk screening in emergency departments (ED) will not achieve widespread adoption because confusion remains around which specific risk indicators to assess, and clinicians fear that such screening will lead to massive surges in psychiatric evaluations. To address these two implementation barriers, this study was funded to derive a clinical decision rule to support universal risk detection and optimize patient care workflow in adults. Investigators: The Project Team has extensive expertise in ED-based suicide risk screening and assessment (Boudreaux, Larkin), clinical decision rule design (Boudreaux, Stiell), predictive analytics (Wang, Liu, Simon), machine learning and informatics (Liu, Simon), industrial engineering (Johnson), and healthcare economics (Clements). A Clinical Advisory Panel ensures that the proposal is grounded in the practical realities of the ED. Innovation: The study will be the first to apply industry standards for deriving decision rules to suicide risk and will directly inform the controversy regarding the relative strengths and weaknesses of universal versus targeted screening. We will pioneer new statistical innovations for rule derivation and will integrate simulation of potential workflow impact using industrial engineering modeling and economic analyses. Approach: We have developed a pool of empirically supported clinician-acceptable candidate suicide risk indicators. Data on these candidate indicators are being collected by trained research staff on adult medical and psychiatric patients from a large ED. Participants are undergoing a comprehensive suicide risk assessment by a research clinician, blinded to the indicators, who assigns the participant to a criterion reference risk group: Negligible, Mild-Moderate, or High risk. Participants are being followed for 24 weeks after the visit to assess suicidal behavior, our secondary outcome. In Aim 1, we will derive a universal screening decision rule for “all comers,” as well as a variant to be used with patients presenting with a psychiatric chief complaint (targeted). In Aim 2, we will test whether a previously validated risk stratification algorithm using data from the electronic health record improves the performance of the decision rules. In Aim 3, we will model the potential operational impact of the rules through dynamic modeling of clinical workflow and economic costs and assessing clinician and patient acceptability in a new sample of 100 ED clinician-patient dyads. Environment: UMass has demonstrated its capability to support this study through several key preliminary studies, including the ED-SAFE studies, System of Safety, and other suicide-related studies set in the ED. Impact: By providing clear, evidence-based recommendations on universal screening and optimized workflow using standards accepted by emergency clinicians, this study will address two pivotal barriers to universal suicide risk screening, transforming the “right thing” into the “easy thing” so it becomes the “usual thing.”

抽象的意义：正如最近关于联合委员会的国家争议所证明的那样，普遍的自杀风险在紧急部门（ED）中进行筛查将无法获得广泛的采用，因为混乱仍然存在围绕哪些特定风险指标进行评估，临床医生担心这种筛查会导致大规模精神病评估的激增。为了解决这两个实施障碍，这项研究得到了资助得出一项临床决策规则，以支持普遍的风险检测并优化成人的患者护理工作流程。调查人员：项目团队在基于ED的自杀风险筛查和评估方面拥有广泛的专业知识（Boudreaux，Larkin），临床决策规则设计（Boudreaux，Stiell），预测分析（Wang，Liu，Simon），机器学习和信息范围（LIU，SIMON），工业工程（Johnson）和医疗保健经济学（Clements）。临床咨询小组可确保该提案以ED的实际现实为基础。创新：该研究将是第一个将行业标准应用于自杀风险和将直接告知有关普遍与普遍性相对优点和劣势的争议有针对性的筛选。我们将开拓规则推导的新统计创新，并将集成模拟使用工业工程建模和经济分析的潜在工作流程影响。方法：我们已经开发了一系列经验支持的临床可接受的候选自杀风险指标。这些候选指标的数据已由训练有素的成人医学研究人员收集和来自大型ED的精神病患者。参与者正在承担全面的自杀风险研究临床评估，对指标视而不见，后者将参与者指定为标准参考风险组：可以忽略不计，轻度中度或高风险。参与者在24周之后进行评估自杀行为的访问是我们的次要结果。在AIM 1中，我们将获得通用筛查 “所有来者”的决策规则，以及与精神科酋长的患者一起使用的变体投诉（针对性）。在AIM 2中，我们将测试先前验证的风险分层算法来自电子健康记录的数据改善了决策规则的绩效。在AIM 3中，我们将建模通过临床工作流程和经济成本的动态建模，规则的潜在运营影响并评估100个ED临床患者二元组的新样本中的临床和患者可接受性。环境：UMass通过多个关键初步表明了其支持这项研究的能力研究，包括ED中的ED-SAFE研究，安全体系和其他与自杀有关的研究。影响：通过为通用筛查和优化工作流提供明确的，基于证据的建议使用紧急临床医生接受的标准，这项研究将解决两个关键的环球障碍自杀风险筛查，将“正确的事物”转变为“简单事物”，因此成为“通常的事物”。