Achieving Diagnostic Excellence through Prevention and Teamwork (ADEPT)

项目概要/摘要 许多因素导致诊断错误，但其中关键是医疗保健中的基础问题： 复杂和分散的护理系统，提供者试图确定确切诊断的时间有限， 以及支持或阻碍诊断性能改进的工作系统和文化。而 存在识别诊断错误的方法，但很少有研究将识别潜在的系统性错误联系起来。 和结构性原因的错误，以现有的质量改进计划在医院。申请的人更少 弹性理论或积极的偏差方法来描述的情况下，诊断的特点， 过程是最佳的，然后使用这些发现来框架卫生系统的改进。 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效用 （UPSIDE）-定义风险因素，潜在原因和患者诊断错误的患病率 参加我们的55家医院研究合作的医院，医院医学重组 网络（HOMERuN）. UPSIDE已经制定了参考标准方法来判定诊断性 错误，定义与错误相关的因素，并与我们的网站和国家 组织，提供了一个独特的强大的机会，改变如何诊断过程评估 可以使用这些程序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变成诊断错误 学习健康系统，将诊断错误评估整合到现有的质量和安全计划中， 提供所需的支持和专业知识，以减少诊断错误，并促进科学，人员， 基础设施的变化，将持续超过此补助金的期限。 为达致我们的整体目标，我们会：1）建立一个个案检讨基础设施， 诊断错误和特征诊断过程中的病人遭受住院死亡，重症监护室 与医院医学重组相关的医院发生的转移或快速反应团队呼叫 2）制定现场级审计和反馈以及集团范围的错误率基准报告， 诊断流程故障，诊断流程弹性功能，并使用这些数据来构建协作 在我们的工厂现有的安全和质量计划之间; 3）使用我们的数据和协作模型来开发 和试点测试干预措施的基础上最优先的结果;和4）发展我们的计划的理解 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保健系统， 一个持续的护理部分，一个也可以成为其他人的模型的系统。

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.