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

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任务的全国性争议所证明的那样，普遍的自杀风险急诊科 (ED) 的筛查不会得到广泛采用，因为仍然存在混乱围绕哪些具体风险指标进行评估，暴民担心这种筛查会导致大规模的为了解决这两个实施障碍，这项研究得到了资助。得出临床决策规则以支持普遍风险检测并优化成人患者护理工作流程。调查人员：项目团队在基于急诊科的自杀风险筛查和评估方面拥有丰富的专业知识（Boudreaux，Larkin），临床决策规则设计（Boudreaux，Stiell），预测分析（Wang，Liu，Simon），机器学习和信息学（Liu，Simon）、工业工程（Johnson）和医疗保健经济学（克莱门茨）。临床顾问小组确保该提案立足于急诊室的实际情况。创新：该研究将首次应用行业标准来得出自杀风险和自杀风险的决策规则。将直接告知有关普遍性与普遍性的相对优势和劣势的争论我们将开创规则推导的新统计创新，并将整合模拟。使用工业工程建模和经济分析来评估潜在的工作流程影响。方法：我们开发了一系列经经验支持的临床医生可接受的候选者自杀风险这些候选指标的数据正在由经过培训的成人医学研究人员收集。以及来自大型急诊室的精神病患者都面临着全面的自杀风险。由研究临床医生进行评估，对指标不知情，并为参与者分配一个标准参考风险组：可忽略、轻度至中度或高风险的参与者将被跟踪 24 周。评估自杀行为的访问是我们的次要结果，在目标 1 中，我们将进行普遍筛查。针对“所有来者”的决策规则，以及用于与精神科主任就诊的患者的变体在目标 2 中，我们将测试是否使用先前验证的风险分层算法。电子健康记录中的数据提高了决策规则的性能。在目标 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来确定规则的潜在运营影响并在 100 名 ED 临床医生-患者二人组的新样本中评估临床医生和患者的可接受性。环境：麻省大学已经通过几个关键的初步展示证明了其支持这项研究的能力研究，包括 ED-SAFE 研究、安全系统以及 ED 中设置的其他自杀相关研究。影响：通过提供关于通用筛查和优化工作流程的清晰、基于证据的建议使用紧急人群接受的标准，这项研究将解决普及的两个关键障碍自杀风险筛查，将“正确的事情”转变为“容易的事情”，使其成为“平常的事情”。