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用于“所有人”研究计划临床数据研究网络的 EHR 数据质量评估工具和问题报告工作流程。

EHR Data Quality Assessment Tools and Issue Reporting Workflows for the 'All of Us' Research Program Clinical Data Research Network.

机构信息

The Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Department of Biomedical Informatics at Columbia University Medical Center, New York, NY, USA.

出版信息

AMIA Jt Summits Transl Sci Proc. 2022 May 23;2022:186-195. eCollection 2022.

Abstract

The All of Us (AoU) Research Program aggregates electronic health records (EHR) data from 300,00+ participants spanning 50+ distinct data sites. The diversity and size of AoU's data network result in multifaceted obstacles to data integration that may undermine the usability of patient EHR. Consequently, the AoU team implemented data quality tools to regularly evaluate and communicate EHR data quality issues at scale. The use of systematic feedback and educational tools ultimately increased site engagement and led to quantitative improvements in EHR quality as measured by program- and externally-defined metrics. These improvements enabled the AoU team to save time on troubleshooting EHR and focus on the development of alternate mechanisms to improve the quality of future EHR submissions. While this framework has proven effective, further efforts to automate and centralize communication channels are needed to deepen the program's efforts while retaining its scalability.

摘要

All of Us(AoU)研究计划聚合了来自 300,000 多名参与者的电子健康记录(EHR)数据,涵盖了 50 多个不同的数据站点。AoU 的数据网络的多样性和规模导致了数据集成的多方面障碍,这可能会降低患者 EHR 的可用性。因此,AoU 团队实施了数据质量工具,以定期评估和大规模沟通 EHR 数据质量问题。系统反馈和教育工具的使用最终提高了站点的参与度,并在程序和外部定义的指标衡量下,导致 EHR 质量的定量改善。这些改进使 AoU 团队能够节省解决 EHR 问题的时间,并专注于开发替代机制来提高未来 EHR 提交的质量。虽然这个框架已经被证明是有效的,但需要进一步努力实现自动化和集中沟通渠道,以深化计划的努力,同时保持其可扩展性。

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