Biases introduced by filtering electronic health records for patients with "complete data"
Biases introduced by filtering electronic health records for patients with "complete data"
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DOI：
10.1093/jamia/ocx071
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发表时间：
2017-11-01
影响因子：
6.4
通讯作者：
Mandl, Kenneth D.
中科院分区：
文献类型：
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作者：
Weber, Griffin M.;Adams, William G.;Mandl, Kenneth D.
在全国范围内采用电子健康记录（EHR）的一个承诺是大规模临床研究数据的可用性。然而，由于同一患者可能在多个医疗机构接受治疗，因此仅来自单个站点的数据可能不包含该患者的完整病史，这意味着可能会丢失关键事件。在这项研究中，我们评估了数据“完整性”的简单启发式检查如何影响结果队列中的患者数量并引入潜在的偏倚。我们从一组16个过滤器开始，检查人口统计学，实验室检查和其他类型的数据，然后系统地应用所有2（16）这些过滤器可能组合到7个医疗保健系统的1200万患者的EHR数据和700万的单独付款人索赔数据库EHR数据显示，各站点之间的数据完整性存在相当大的差异，数据类型之间存在高度相关性。例如，有诊断的患者比例从所有患者的35.0%增加到至少使用1种药物的患者的90.9%。一个不相关的索赔数据集独立地显示，大多数过滤器选择的成员是老年人和更有可能是女性，可以排除大部分的人口，其数据实际上是完整的。作为研究人员设计研究，他们需要平衡他们的信心，在数据的完整性与对数据的要求对结果的患者队列的影响。
One promise of nationwide adoption of electronic health records (EHRs) is the availability of data for large-scale clinical research studies. However, because the same patient could be treated at multiple health care institutions, data from only a single site might not contain the complete medical history for that patient, meaning that critical events could be missing. In this study, we evaluate how simple heuristic checks for data "completeness" affect the number of patients in the resulting cohort and introduce potential biases.We began with a set of 16 filters that check for the presence of demographics, laboratory tests, and other types of data, and then systematically applied all 2(16) possible combinations of these filters to the EHR data for 12 million patients at 7 health care systems and a separate payor claims database of 7 million members.EHR data showed considerable variability in data completeness across sites and high correlation between data types. For example, the fraction of patients with diagnoses increased from 35.0% in all patients to 90.9% in those with at least 1 medication. An unrelated claims dataset independently showed that most filters select members who are older and more likely female and can eliminate large portions of the population whose data are actually complete.As investigators design studies, they need to balance their confidence in the completeness of the data with the effects of placing requirements on the data on the resulting patient cohort.