Catch Me if You Can: Acute Events Hidden in Structured Chronic Disease Diagnosis Descriptions Show Detectable Recording Patterns in EHR.

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. ©2020 AMIA - All rights reserved.