Columbia University, New York, NY, USA.
IQVIA, Cambridge, MA, USA.
AMIA Annu Symp Proc. 2021 Jan 25;2020:983-992. eCollection 2020.
Multi-center observational studies require recognition and reconciliation of differences in patient representations arising from underlying populations, disparate coding practices and specifics of data capture. This leads to different granularity or detail of concepts representing the clinical facts. For researchers studying certain populations of interest, it is important to ensure that concepts at the right level are used for the definition of these populations. We studied the granularity of concepts within 22 data sources in the OHDSI network and calculated a composite granularity score for each dataset. Three alternative SNOMED-based approaches for such score showed consistency in classifying data sources into three levels of granularity (low, moderate and high), which correlated with the provenance of data and country of origin. However, they performed unsatisfactorily in ordering data sources within these groups and showed inconsistency for small data sources. Further studies on examining approaches to data source granularity are needed.
多中心观察性研究需要识别和协调由于基础人群、不同编码实践和数据采集细节而产生的患者表现差异。这导致了代表临床事实的概念的不同粒度或详细程度。对于研究特定感兴趣人群的研究人员来说，重要的是要确保正确级别的概念用于这些人群的定义。我们研究了 OHDSI 网络中 22 个数据源中的概念粒度，并为每个数据集计算了综合粒度得分。三种基于 SNOMED 的替代方法在将数据源分类为低、中、高三个粒度级别方面表现一致，这与数据的来源和原产国相关。然而，它们在对这些组内的数据源进行排序时表现不佳，并且对小数据源的表现不一致。需要进一步研究检查数据源粒度的方法。
