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: 10707197
• 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/10707197
• 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.

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