EnhanCed HandOffs (ECHO)

增强型切换 (ECHO)

• 批准号: 10636447
• 负责人: Joanna Abraham
• 金额: $ 39.62万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10636447
• 关键词: EnhanCed; HandOffs; ECHO

ABSTRACT: ECHO PROJECT Patients undergoing complex surgeries are most vulnerable during the immediate postoperative period; thus, handoffs from the OR (operating room) to ICU (intensive care unit) require seamless communication and coordination between surgical, anesthesia, and critical care teams. Postoperative handoffs are a threat to patient safety, causing ~35% of medical errors in the US. To mitigate these errors, the National Patient Safety Goal (2E) necessitated the “standardization” of handoff process and content, which resulted in adoption of information transfer checklists, handoff process-based protocols, or both. Although such strategies have improved handoff quality, our meta-analysis found that such improvements were temporary and had limited sustainability, due to the structured formats imposing “rigid” standardization with limited flexibility and support for interactive and personalized communication. Our central hypothesis is that a flexible standardization approach will lead to not only improvements in information sharing, but also improvements in shared understanding of patient risks, handoff interactivity, and handoff duration. Towards this end, we propose to develop the INTERACT (Intelligent interactive care continuity) handoff bundle, a flexible, standardized, EHR- integrated, and resilient sociotechnical intervention comprised of a: (1) telemedicine-augmented handoff process (i.e., the social component) supported by a (2) machine learning (ML)-augmented handoff report (i.e., the technical component). INTERACT underscores the importance of using a perioperative telemedicine suite as a safety net to support resilience to errors in OR-ICU handoff process and content. The ML-augmented handoff report supports personalized communication of core (i.e., standardized) and tailored (flexible) content based on predicted patient risks for postoperative complications. Aim 1 will focus on updating our current ML models for predicting risks associated with postoperative complications, based on state-of-the-art imputation and feature engineering techniques. We will enhance our model-agnostic explanation framework to support postoperative handoffs and decision-making, which will also be validated with a summative user evaluation study. Aim 2 will follow a user-centered design approach to iteratively develop and test the INTERACT bundle including handoff report design ideation, and usability testing, and lastly, the INTERACT bundle in-situ simulations. Aim 3 will adopt a Hybrid Type 1 trial design and the Care Transitions Framework to evaluate the effectiveness and implementation-potential of the INTERACT bundle. Our primary outcome is information sharing score (i.e., a measure of information completeness), while secondary outcomes include information inaccuracies, realized errors and adverse events, and ICU length of stay. With an integrated multidisciplinary approach to improving perioperative care transitions, the proposed INTERACT bundle will address the stated AHRQ FOA goals of “defragmenting information, improving communication, and assuring care team access to reliable and complete health information; and empowering care teams to improve health outcomes.”

摘要：回声工程 接受复杂手术的患者在术后初期最脆弱;因此， 从OR（手术室）到ICU（重症监护室）的患者需要无缝沟通， 协调手术、麻醉和重症监护团队。术后复发是一种威胁， 患者安全，导致美国约35%的医疗错误。为了减少这些错误，国家患者安全 目标（2 E）要求移交过程和内容的“标准化”，这导致采用 信息传输检查表、基于切换过程的协议、或两者。虽然这些战略 改善切换质量，我们的荟萃分析发现，这种改善是暂时的， 可持续性，由于结构化格式实行“严格”的标准化，灵活性和支持有限 用于交互式和个性化的通信。我们的中心假设是，灵活的标准化 这种方法不仅会改善信息共享， 了解患者风险、切换交互性和切换持续时间。为此，我们建议 开发INTERACT（智能交互式护理连续性）交接包，这是一个灵活的、标准化的EHR- 综合的、有弹性的社会技术干预，包括：（1）远程医疗增强的移交 过程（即，社交组件）由（2）机器学习（ML）增强的切换报告（即， 技术部分）。INTERACT强调了使用围手术期远程医疗套件的重要性 作为安全网，以支持对OR-ICU切换过程和内容中的错误的弹性。ML增强 切换报告支持核心的个性化通信（即，标准化）和定制（灵活）内容 基于预测的患者术后并发症风险。目标1将专注于更新我们当前的ML 基于最新技术插补预测术后并发症相关风险的模型 和特征工程技术。我们将增强我们的模型无关的解释框架，以支持 术后评估和决策，也将通过总结性用户评价进行确认 study.目标2将遵循以用户为中心的设计方法，反复开发和测试INTERACT捆绑包 包括切换报告设计构思和可用性测试，最后是INTERACT现场捆绑包 模拟目标3将采用混合1型试验设计和护理过渡框架，以评估 有效性和执行潜力的互动捆绑。我们的主要成果是信息 共享分数（即，信息完整性的衡量），而次要结果包括信息 不准确性、已实现的错误和不良事件以及ICU住院时间。综合多学科 改善围手术期护理过渡的方法，拟议的INTERACT捆绑包将解决上述问题 AHRQ FOA的目标是“整理信息碎片，改善沟通，并确保护理团队获得 可靠和完整的健康信息;并授权护理团队改善健康结果。