了解ENACT联邦研究网络中的数据差异
Taowei D Wang1, Darren W Henderson2, Griffin M Weber3
1Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.
medRxiv : the preprint server for health sciences
|January 27, 2025
概括
联合研究网络现在可以使用患者计数来识别数据质量问题. 这种新的,保护隐私的方法有助于网站改进电子健康记录数据,以改善临床试验积累.
科学领域:
- 医疗信息学 医疗信息学
- 临床研究 临床研究
- 数据科学数据科学数据科学
背景情况:
- 联合研究网络通过交换电子健康记录 (EHR) 数据来促进医学研究.
- 电子健康记录数据质量不佳可能会严重阻碍研究目标和患者积累.
- 现有的数据质量解决方案往往依赖于严格的标准,限制了适应性.
研究的目的:
- 开发和实施一种新的以数据为中心的方法,用于在联合研究网络中识别数据质量问题.
- 创建一个保护隐私的管道,利用患者计数和网络统计数据质量调查.
- 建立适用于各种研究网络的灵活和可适应的方法.
主要方法:
- 分布式高性能患者计数脚本 (在床边整合生物学 - i2b2) 在Evolve到Next-Gen患者到临床试验 (ENACT) 站点.
- 汇总了ENACT中心的站点贡献的患者数量,以生成网络统计数据.
- 开发了数据质量探索器 (DQE) 网络应用程序,以获取网络统计数据并促进特定站点的数据质量分析.
主要成果:
- 十三个ENACT站点贡献了患者数量,其中七个站点积极使用数据质量探索器 (DQE) 进行分析.
- 实施的指标允许对网络统计数据的数据质量进行调查.
- 该系统证明了计数的成功聚合,并为数据质量评估提供了一个平台.
结论:
- 在ENACT网络中成功实施了一种使用患者计数和网络统计数据的新型,保护隐私的指标,用于数据质量调查.
- 这种以数据为中心,有机发展的方法为数据质量管理提供了灵活和低障碍的方法.
- 底层管道设计可将其推广到其他联合研究网络,促进更广泛的采用和更好的数据质量.
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