在多中心临床数据集中优化数据质量保证的效率和有效性
Anne Fu1,2, Trong Shen1, Surain B Roberts1,3
1Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON M5C 3G7, Canada.
概括
通过结合半自动化和手动方法,有效评估电子健康记录 (EHR) 数据质量. 尽量减少手动审查,优化在大型EHR研究数据库中的错误检测.
科学领域:
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
- 临床研究 临床研究
背景情况:
- 电子健康记录 (EHR) 对研究至关重要,但在大型数据库中对其数据质量的评估仍然具有挑战性.
- 有限的经验证据表明,可以有效地结合自动化和手动方法来确保电子健康记录数据的质量.
研究的目的:
- 评估一个合并的半自动化和手动方法来评估大型多站点电子健康记录数据库 (GEMINI) 中的数据质量.
- 为了确定手动记录审查和准确的数据错误检测之间的最佳平衡.
主要方法:
- 利用半自动评估来确定32家医院的462,226名患者入院的初始数据质量问题.
- 在记录子集上进行手动验证,并执行模拟实验以分析手动审查量和错误检测率之间的关系.
主要成果:
- 半自动评估标记了79个数据质量问题,其中14个数据显著影响数据.
- 手动审查了2,676次遭遇,发现了4个新的有意义的错误,但也产生了365个虚假阳性,这些错误随着审查量线性增加.
- 模拟显示高灵敏度检测输血数据错误与最小的手动审查 (5记录).
结论:
- 在GEMINI方法提供了一个可扩展的框架,用于多站点EHR数据质量评估和改进.
- 手动数据审查是必不可少的,但可以尽量减少以提高效率和减少错误的阳性.
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