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

• 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
• 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

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.”

摘要 意义：正如最近关于联合委员会授权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，因为混乱仍然存在 围绕哪些特定的风险指标进行评估，临床医生担心这样的筛查将导致大规模 精神病学评估的激增。为了解决这两个实施障碍，这项研究的资金来源是 得出临床决策规则，以支持普遍的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ED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)临床顾问团确保有关建议切合教育署的实际情况。 创新：这项研究将首次将行业标准应用于自杀风险和自杀风险的决策规则 将直接引出关于宇宙与宇宙相对强弱的争论 有针对性的筛查。我们将在规则推导方面开创新的统计创新，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：我们已经开发了一组经验支持的临床医生可接受的候选自杀风险 指标。这些候选指标的数据是由训练有素的成人医学研究人员收集的 以及来自大型急诊室的精神病患者。参与者正在经历全面的自杀风险 研究临床医生的评估，对指标视而不见，给参与者分配一个标准 参考风险组：可忽略、轻度-中度或高度风险。参与者将在24周后接受跟踪 这次访问是为了评估自杀行为，这是我们的次要结果。在目标1中，我们将推导出一种普遍筛查 “所有来者”的决定规则，以及一个变种，用于患者向精神病科主任提交报告 投诉(有针对性)。在目标2中，我们将测试先前验证的风险分层算法是否使用 来自电子健康记录的数据改进了决策规则的性能。在目标3中，我们将建模 通过对临床工作流程和经济成本进行动态建模，这些规则可能产生的操作影响 以及评估临床医生和患者对100名ED临床医生-患者二人组的新样本的可接受性。 环境：马萨诸塞州大学通过几个关键的初步研究证明了它有能力支持这项研究 研究，包括ED-SAFE研究、安全系统，以及在ED设置的其他与自杀有关的研究。 影响：通过提供关于普遍筛查和优化工作流程的清晰、循证的建议 使用急诊医生接受的标准，这项研究将解决普及的两个关键障碍 自杀风险筛查，把“正确的事情”变成“容易的事情”，这样它就变成了“平常的事情”。