Diagnostic Accuracy through Advancing EHR displaY, Education and Surveillance (DATA-EYES)

项目总结： 诊断差错(DE)仍然是最昂贵和最普遍的可预防医疗差错形式之一，有近 每年有1200万美国人受到影响，估计损失超过1000亿美元。不幸的是，降低DE的努力已经 在很大程度上仍然没有成功。这在很大程度上是由于DE的病因非常复杂，有多个 影响因素。然而，诊断过程的中心是关键的认知过程，如医生的 能够找到和处理相关信息，根据这些信息进行推理，并做出诊断。95%以上 在采用电子健康记录(EHR)的医疗保健提供者中，这些系统是几乎所有 患者信息，并因此塑造诊断过程。虽然人们认识到电子健康记录对 可持续发展的问题，特别是电子健康记录如何、何时和为什么对可持续发展作出贡献的确定和相对贡献 因为它涉及软件的社会技术领域，所以对用户和系统(工作流)的描述很差。我们有 试图通过医疗事故案例(CRICO)和患者安全事件(PSE)的分析更好地定义这一点 门诊护理中与DE相关的报表。从我们的医疗事故分析来看，近60%的DE病例有 确定的EHR贡献，另有19%不确定。EHR在测试阶段的贡献最大 诊断过程中最常见的EHR风险与数据解释、下单和 计划的执行。然而，这种分析依赖于对非结构化数据的手动评估，这非常耗时， 缺乏针对性，不切实际地被广泛采用。一旦EHR对DE的相对贡献能够 下定决心，卫生系统然后可以部署解决方案来帮助缓解。理想情况下，这将包括使用模拟的能力 指导EHR重新设计和培训，现场观察EHR如何融入日常工作流程和策略 监测这些干预措施的影响。这项提议的目标是建立一个卓越诊断中心 (DATAEYES)专注于确定EHR对DE的贡献，并使用此信息部署一套解决方案，以 完善软件、用户和系统。我们将通过使用国家数据创建一个知情的分类法来实现这一点 纳入机构数据收集工具，以便于在机构范围内收集目标1中的电子病历对远程教育的贡献。 然后，我们将在目标2中开发和验证这些工具，并将这些信息与现场工作流程结合使用 观察，以告知电子健康记录如何、何时和为什么对可持续发展作出贡献。这些信息将被用来创建高- Fidelity模拟EHR图表，以促进针对EHR最佳实践的特定工作流程培训，并指导EHR重新设计 并通过AIM#3中的EHR审计日志监控这些干预措施的影响。参与的3个中心(OHSU、Medstar 健康、布里格姆和妇女医院)将进一步确定正在研究的两家电子病历供应商的影响 (CENER和EPIC)和当地工作流程的具体做法。然后我们将利用我们与患者安全方面的合作 传播这些调查结果的组织和行业，以及在DATAEYES开发的基础设施将作为核心 为其他DCE站点提供资源，以便对未来基于电子病历的解决方案进行快速评估和原型制作。

Project Summary: Diagnostic error (DE) remains one of the most costly and prevalent forms of preventable medical error, with nearly 12 million Americans affected annually at an estimated cost of over $100 billion. Unfortunately, efforts to reduce DE have remained largely unsuccessful. This is in large part due to the fact that etiology of DE is highly complex with multiple contributing factors. However, central to the diagnostic process are critical cognitive processes such as the physician's ability to find and process relevant information, reason with this information, and formulate a diagnosis. With over 95% of healthcare providers adopting electronic health records (EHRs), these systems are the primary source of nearly all patient information and, therefore, shape the diagnostic process. While it is recognized that the EHR contributes to the problem of DE, the identification and relative contribution of how, when and why the EHR contributes to DE, specifically as it relates to the sociotechnical domains of software, user and system (workflow) are poorly described. We have attempted to better define this through the analysis of medical malpractice cases (CRICO) and patient safety event (PSE) report forms related to DE in ambulatory care. From our medical malpractice analysis, nearly 60% of cases of DE had a definitive EHR contribution, with another 19% indeterminate. The EHR contributed most often during the testing phase of the diagnostic process with the most common EHR hazards related to data interpretation, order placement and execution of plan. However, this analysis relies on manual evaluation of unstructured data which is highly time consuming, lacks specificity and is impractical for widespread adoption. Once the relative contribution of EHRs to DE can be determined, health systems can then deploy solutions to help mitigate. Ideally this will include the ability to use simulation to guide both EHR redesign and training, in situ observation of how the EHR integrates into daily workflow and a strategy to monitor the impact of these interventions. The goal of this proposal is to establish a Diagnostic Center of Excellence (DATAEYES) focused on identification of EHR contribution to DE, and use this information to deploy a suite of solutions to improve software, user and system. We will achieve this by using national data to create an informed taxonomy to be integrated into institution data collection tools, to facilitate institution-wide capture of EHR contributions to DE in Aim #1. We will then develop and validate these tools in Aim #2 and use this information, in combination with in situ workflow observations, to inform how, when and why the EHR is contributing to DE. This information will be used to create high- fidelity simulated EHR charts to facilitate both workflow specific training on EHR best practices and guide EHR redesign and monitor the impact of these interventions via EHR audit logs in Aim #3. The 3 centers participating (OHSU, Medstar Health, Brigham and Women's Hospital) will allow further ascertainment of the impact of both EHR vendors being studied (Cerner and Epic) and local workflow specific practices. We will then leverage our collaborations with patient safety organization and industry to disseminate these findings and the infrastructure developed at DATAEYES will serve as a core resource for the other DCE sites, allowing for rapid evaluation and prototyping of future EHR based solutions.