Diaz-Garelli Franck, Lenoir Kristin M, Wells Brian J
University of North Carolina at Charlotte. Charlotte, NC.
Wake Forest School of Medicine, Winston Salem, NC.
AMIA Annu Symp Proc. 2021 Jan 25;2020:373-382. eCollection 2020.
Our previous research shows that structured cancer DX description data accuracy varied across electronic health record (EHR) segments (e.g. encounter DX, problem list, etc.). We provide initial evidence corroborating these findings in EHRs from patients with diabetes. We hypothesized that the odds of recording an "uncontrolled diabetes" DX increased after a hemoglobin A1c result above 9% and that this rate would vary across EHR segments. Our statistical models revealed that each DX indicating uncontrolled diabetes was 2.6% more likely to occur post-A1c>9% overall (adj-p=.0005) and 3.9% after controlling for EHR segment (adj-p<.0001). However, odds ratios varied across segments (1.021<OR<1.224, .0001<adj-p<.087). The number of providers (adj-p<.0001) and departments (adjp<.0001) also impacted the number of DX reporting uncontrolled diabetes. Segment heterogeneity must be accounted for when analyzing clinical data. Understanding this phenomenon will support accuracy-driven EHR data extraction to foster reliable secondary analyses of EHR data.
我们之前的研究表明，结构化癌症诊断描述数据的准确性在电子健康记录（EHR）的不同部分（如就诊诊断、问题列表等）有所不同。我们提供了初步证据，在糖尿病患者的电子健康记录中证实了这些发现。我们假设，糖化血红蛋白结果高于9%后，记录“未控制的糖尿病”诊断的几率会增加，并且这一比率在电子健康记录的不同部分会有所不同。我们的统计模型显示，总体而言，糖化血红蛋白>9%后，每个表明未控制糖尿病的诊断发生的可能性增加2.6%（校正P值=0.0005），在控制电子健康记录部分后增加3.9%（校正P值<0.0001）。然而，不同部分的优势比有所不同（1.021<优势比<1.224，0.0001<校正P值<0.087）。医疗服务提供者的数量（校正P值<0.0001）和科室（校正P值<0.0001）也会影响报告未控制糖尿病的诊断数量。在分析临床数据时，必须考虑部分异质性。了解这一现象将有助于以准确性为导向的电子健康记录数据提取，以促进对电子健康记录数据进行可靠的二次分析。
