Achieving Diagnostic Excellence through Prevention and Teamwork (ADEPT)

项目摘要/摘要 导致诊断错误的因素很多，但其中的关键因素是医疗保健中的基本问题： 复杂和支离破碎的护理系统，试图确定确切诊断的提供者可用的有限时间， 以及支持或阻碍诊断性能改进的工作系统和文化。而当 识别诊断错误的方法已经存在，很少有研究将潜在的系统性识别联系起来 以及医院现有质量改进计划的结构性错误原因。申请的人更少 恢复力理论或正偏差方法来表征诊断为 流程是最佳的，然后使用这些调查结果来制定卫生系统改进的框架。 这个应用程序直接建立在我们目前资助的研究-预测系统在诊断错误中的应用 (上图)-定义了患者中的风险因素、潜在原因和诊断错误的发生率 获准进入医院参加我们55家医院的研究合作，医院医学再造 网络(本垒打)。Upside已经开发了参考标准方法来判定诊断 错误，定义与错误相关的因素，并与我们的站点和国家创建协作 组织，提供了转变诊断流程评估方式的独特而强大的机会 这些计划可以用来提高患者的安全性。 该中心的总体目标是将我们非常成功的多中心网络转变为诊断错误 学习卫生系统，将诊断错误评估整合到现有的质量和安全计划中， 提供减少诊断错误所需的支持和专业知识，并促进科学、人员和 基础设施变化将持续超过这笔赠款的期限。 为了实现我们的总体目标，我们将：1)实施案件审查基础设施，以准确识别 ICU住院死亡患者的诊断错误和特征诊断过程 转移，或快速反应小组呼叫发生在与医院医学再造相关的医院 网络；2)制定现场一级的审计和反馈以及整个集团的错误率基准报告； 诊断流程故障、诊断流程弹性特征并使用这些数据来构建协作框架 我们现场现有的安全和质量计划之间的关系；3)使用我们的数据和协作模式来开发 和基于最高优先级结果的试点测试干预；以及4)发展对我们计划的理解 覆盖范围、采用、实施和维护，以及试点的可行性和初步经验 干预措施。该项目将建立一个学习型健康系统，以实现卓越的诊断 这是护理的一个持续的部分，一个可以为其他人树立榜样的系统。

PROJECT SUMMARY/ABSTRACT Many factors contribute to diagnostic errors, but key among them are foundational issues in healthcare: complex and fragmented care systems, the limited time available to providers trying to ascertain a firm diagnosis, and the work systems and cultures that support or impede improvements in diagnostic performance. While approaches to identifying diagnostic errors exist, few studies have linked identification of underlying systemic and structural causes of errors to existing quality improvement programs in hospitals. Even fewer have applied resilience theories or positive deviance approaches to characterize the features of cases where the diagnostic process is optimal and then use those findings to frame health system improvement. This application builds directly on our currently funded study - Utility of Predictive Systems in Diagnostic Errors (UPSIDE) - which is defining risk factors, underlying causes, and prevalence of diagnostic errors among patients admitted to hospitals participating in our 55-hospital research collaborative, the Hospital Medicine Reengineering Network (HOMERuN). UPSIDE has developed reference standard approaches to adjudication of diagnostic errors, defined factors associated with errors, and created collaborations with our sites and national organizations, providing a uniquely powerful opportunity to transform how diagnostic process evaluation programs can be used to improve patient safety. The overall goal of this Center is to turn our highly successful multicenter network into a diagnostic error learning health system that will integrate diagnostic error assessments into existing quality and safety programs, provide support and expertise needed to reduce diagnostic errors, and catalyze scientific, personnel, and infrastructure changes which will last beyond the duration of this grant. To achieve our overall goals, we will: 1) Implement a case review infrastructure which can accurately identify diagnostic errors and characterize diagnostic processes among patients suffering inpatient deaths, ICU transfers, or rapid-response team calls taking place at hospitals associated the Hospital Medicine Reengineering Network; 2) To develop site-level audit and feedback and group-wide benchmarking reports of error rates, diagnostic process faults, diagnostic process resilience features and use these data to frame collaboration between existing safety and quality programs at our sites; 3) To use our data and collaborative model to develop and pilot test interventions based on highest priority findings; and 4) Develop understanding of our program’s reach, adoption, implementation, and maintenance, as well feasibility and initial experience with pilot interventions. This project will establish a learning health system which can achieve excellence in diagnosis as an ongoing part of care, a system which can be a model for others as well.