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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要超过 80% 的急诊科 (ED) 急性心力衰竭 (AHF) 患者入院治疗医院中，只有 10% 的人面临院内事件的高风险。我们开发并验证了预测规则 (STRATIFY)，用于识别可能安全出院的患有 AHF 的 ED 患者。如果成功实施，将节省大量资源而不牺牲患者的治疗效果并帮助机构实现负责任的护理目标。现实世界中预测规则的采用对于急性心力衰竭 (AHF) 和在急诊室治疗的其他病症来说，面临着时间压力等障碍的挑战工作流程、实时数据可用性和质量。解决方案将产生重大影响用于实施任何临床决策算法。这笔赠款的核心目标是发展采取多层次方法和必要的统计方法来缩小执行差距我们的 AHF 风险预测工具，作为其他自动风险预测方法的模型电子健康记录系统。通过 ED 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基于临床决策支持的方法包括 1) 强有力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整合挑战，以及 3) 详细的有效性评估并在多个中心实施。我们的建议将对双方产生广泛影响通过缩小科学发现和风险模型之间的差距来实施护理实践和风险模型使用风险预测工具提供医疗保健。重要的是，这里开发的方法将可推广到其他风险预测工具，并可轻松转化为其他复杂的基于 ED 的工具肺栓塞、中风和慢性阻塞性肺病等疾病，从而最大限度地提高治疗机会对科学和患者护理都有影响。