使用临床数据仓库的数字基础设施来确定尿病例的挑战和解决方案
Martin Schönthaler1, Noah Hempen2, Maria Weymann3
1Department of Urology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
PloS one
|January 23, 2026
概括
使用临床数据仓库 (CDWH) 进行尿病研究的数据提取显示性能数据产生了准确的结果. 手动和报销数据提取方法有显著的偏差,突出显示需要仔细选择数据源.
科学领域:
- 医疗信息学 医疗信息学
- 泌尿病学研究 泌尿病学研究
- 在医疗保健中的数据科学.
背景情况:
- 德国全国性复发性尿结石病 (RECUR) 登记册是利用当地临床数据仓库 (CDWH) 建立的,以加强尿结石病研究.
- 确保数字化临床数据的可靠性对于RECUR.com等注册表的科学有效性至关重要.
- 本研究评估了从CDWH内的各种来源对尿病例的数据提取方法.
研究的目的:
- 为了比较不同数据提取方法的准确性,以识别尿病例.
- 评估算法提取数据与手动提取数据的可靠性.
- 确定从临床数据仓库中提取数据的潜在差异及其原因.
主要方法:
- 数据从弗赖堡大学医学中心 (MCUF) 医院信息系统 (HIS),绩效数据和报销数据中提取.
- 用算法提取urolithiasis病例使用性能和报销数据.
- 使用手动提取的HIS数据和算法提取的数据进行比较,编制了一个参考组.
主要成果:
- 从性能数据中进行算法提取,与参考组相比,准确识别了尿病例.
- 手动HIS数据提取显示出14%的偏差,算法补偿数据提取显示出与参考数据的12%的偏差.
- 偏差归因于人为错误,HIS中的数据可用性问题,以及报销数据中的案例合并.
结论:
- 清晰地定义目标变量 (例如,尿病例) 和了解数据源对于可靠的研究至关重要.
- 该研究表明,当从不同的数据源中提取数据时",一个案例不是一个案例不是一个案例".
- 结果可转移到具有类似CDWH结构的其他医疗中心,支持德国医疗信息化倡议 (MII).
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