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

通过推进 EHR 显示​​、教育和监视来提高诊断准确性 (DATA-EYES)

• 批准号: 10640782
• 负责人: DAVID W., MD,Msc BATES
• 金额: $ 100万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2026-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10640782
• 关键词: Diagnostic; Accuracy; through; Advancing; EHR

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.

项目概要： 诊断错误（DE）仍然是最昂贵和最普遍的可预防医疗错误形式之一， 每年有1200万美国人受到影响，估计成本超过1000亿美元。不幸的是，减少DE的努力 但基本上没有成功。这在很大程度上是由于DE的病因非常复杂，有多种病因。 促成因素。然而，诊断过程的核心是关键的认知过程，例如医生的 发现和处理相关信息的能力，利用这些信息进行推理，并制定诊断。超过95% 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，从而形成诊断过程。虽然人们认识到EHR有助于 DE的问题，EHR如何、何时以及为什么对DE做出贡献的识别和相对贡献，特别是 由于它涉及软件的社会技术领域，因此对用户和系统（工作流）的描述很差。我们有 试图通过对医疗事故案例（CRICO）和患者安全事件（PSE）的分析来更好地定义这一点 与动态护理中DE相关的报告表。从我们的医疗事故分析来看，近60%的DE病例有一个 明确的EHR贡献，另有19%不确定。EHR在测试阶段的贡献最大 的诊断过程与最常见的EHR危险有关的数据解释，订单安排， 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， 缺乏特异性并且对于广泛采用是不切实际的。一旦EHR对DE的相对贡献可以 一旦确定，卫生系统就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战略 监测这些干预措施的影响。该提案的目标是建立一个卓越诊断中心 （DATAEYES）专注于识别EHR对DE的贡献，并使用此信息部署一套解决方案， 改进软件、用户和系统。我们将通过使用国家数据来创建一个明智的分类来实现这一目标， 整合到机构数据收集工具中，以促进机构范围内获取EHR对目标1中DE的贡献。 然后，我们将在目标#2中开发和验证这些工具，并结合现场工作流程使用这些信息 观察，以告知如何，何时以及为什么EHR有助于DE。这些信息将被用于创建高- 保真度模拟EHR图表，以促进EHR最佳实践的特定工作流程培训，并指导EHR重新设计 并通过目标3中的EHR审计日志监控这些干预措施的影响。参与的3家中心（OHSU、Medstar 健康，布里格姆和妇女医院）将允许进一步确定的影响，这两个电子病历供应商正在研究 （Cerner和Epic）和本地工作流程特定实践。然后，我们将利用我们与患者安全的合作 组织和行业传播这些发现和基础设施开发的数据眼将作为核心 资源的其他DCE网站，允许快速评估和原型的未来EHR为基础的解决方案。