Tailored dissemination and implementation of emergency care clinical decision support to improve emergency department disposition

定制传播和实施急诊临床决策支持，以改善急诊科处置

• 批准号: 10182207
• 负责人: SUNIL KRIPALANI
• 金额: $ 84.99万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 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: https://reporter.nih.gov/project-details/10182207
• 关键词: Accident and Emergency department; Acute; Address; Admission activity; Adoption; Adverse event; Algorithms; Beds; Biometry; Caregivers; Caring; Characteristics; Chronic Obstructive Airway Disease; Clinical; Clinical Research; Complex; Consolidated Framework for Implementation Research; Data; Decision Making; Disease; Disease Management; Dissemination and Implementation; Electronic Health Record; Emergency Care; Emergency Department Physician; Emergency Department evaluation; Emergency Department patient; Emergency Medicine; Event; Face; Focus Groups; Geography; Goals; Grant; Health Expenditures; Health Status; Heart failure; Home; Hospitalization; Hospitals; Hour; Institution; Interruption; Leadership; Medical; Methods; Modeling; Motivation; National Heart, Lung, and Blood Institute; Patient Care; Patient-Focused Outcomes; Patients; Pattern; Penetration; Positioning Attribute; Process; Provider; Publishing; Pulmonary Embolism; Qualitative Research; Recommendation; Research; Resources; Risk; Risk Factors; Scientist; Series; Site; Statistical Algorithm; Statistical Methods; Stroke; System; Testing; Time; Update; Validation; Work; acute care; base; biomedical informatics; clinical decision support; clinical practice; clinical research site; data integration; design; effectiveness evaluation; electronic structure; health care delivery; high risk; hospital readmission; implementation barriers; implementation outcomes; implementation process; implementation science; implementation strategy; implementation study; improved; innovation; insight; interdisciplinary collaboration; multidisciplinary; novel; pandemic disease; patient-clinician communication; practice setting; predictive modeling; pressure; response; risk prediction; risk prediction model; safe patient; shared decision making; success; support tools; tool; usability; user centered design; virtual

PROJECT SUMMARY/ABSTRACT Over 80% of emergency department (ED) patients with acute heart failure (AHF) are admitted to the hospital, with only 10% at high-risk for in-hospital events. We developed and validated a prediction rule (STRATIFY) that identifies ED patients with AHF that may be safe to discharge. If successfully implemented, it will save substantial resources without sacrificing patient outcomes and help institutions achieve goals for accountable care. Real-world adoption of prediction rules for AHF and other conditions treated in the ED is challenged by barriers such as time-pressured workflow, real-time data availability and quality. A solution would have considerable implications for implementing any clinical decision algorithm. The central objective of this grant is to develop a multilevel approach and the necessary statistical methods to close the gap in implementation of our AHF risk prediction tool, as a model for other automated risk prediction approaches within an electronic health records system. Through inter-disciplinary collaboration among ED physicians, biostatisticians, qualitative research experts, implementation scientists and bioinformaticians, we propose to rigorously develop and test a clinical decision support-based approach including 1) robust stakeholder engagement to participate in user-centered design and identify approaches to overcome barriers to implementation, 2) overcoming real-time data integration challenges through statistical methods, and 3) a detailed evaluation of effectiveness and implementation in multiple centers. Our proposal will have a broad impact on both acute care practice and risk model implementation by closing the gap between scientific discovery and health care delivery using risk prediction tools. Importantly, the methods developed here will generalize to other risk prediction tools and be readily translatable to other complex ED-based diseases such as pulmonary embolism, stroke, and COPD, thereby maximizing opportunity for impact both scientifically and on patient care.

项目总结/摘要 超过80%的急诊科（艾德）急性心力衰竭（AHF）患者入院治疗， 医院，只有10%的高风险为院内事件。我们开发并验证了 预测规则（STRATIFY），用于识别可能安全出院的AHF艾德患者。如果 如果成功实施，它将节省大量资源，而不牺牲病人的结果 并帮助机构实现负责任护理的目标。预测规则的实际应用 在艾德治疗的AHF和其他条件受到诸如时间压力等障碍的挑战 工作流程、实时数据可用性和质量。一个解决方案将产生相当大的影响 用于实现任何临床决策算法。这项补助金的主要目标是发展 采取多层次办法和必要的统计方法，以缩小执行方面的差距 我们的AHF风险预测工具，作为其他自动化风险预测方法的模型， 电子健康记录系统通过艾德之间的跨学科合作 医生、生物统计学家、定性研究专家、实施科学家和 生物信息学家，我们建议严格开发和测试临床决策支持为基础的 方法包括：1）强大的利益相关者参与以用户为中心的设计 并确定克服实施障碍的方法，2）克服实时数据 通过统计方法解决一体化挑战，3）详细评估有效性 并在多个中心实施。我们的建议将对两个严重的 通过缩小科学发现和风险管理之间的差距， 使用风险预测工具提供保健服务。重要的是，这里开发的方法将 推广到其他风险预测工具，并易于转换到其他复杂的基于ED的 肺栓塞、中风和COPD等疾病，从而最大限度地增加 对科学和病人护理都有影响。