Analysis of EMR data can pin down cases of undiagnosed diabetes

By applying simple algorithms to EMR data, researchers can identify cases of undiagnosed diabetes, according to a study published in CMAJ Open.

Researchers from Oxford University in the U.K. worked with Ohio-based Statistical Solutions and GE Healthcare, UK, to conduct a cross-sectional study and retrospective observational cohort analysis of 11.5 million EMRs from more than 9,000 U.S. primary care clinics.

“We were able to identify a substantial number of patients with uncoded diabetes and probable undiagnosed diabetes using simple algorithms applied to the primary care electronic records,” according to the study authors. Specifically, of the 10.4 million records for non-diabetic patients, about 40,359 had at least two abnormal fasting or random blood glucose values and 23,261 had at least one documented glycated hemoglobin value of 6.5 percent or higher, the study found. 

“Electronic diabetes registers are underused in U.S. primary care and provide opportunities to facilitate the systematic, structured approach that is established in England,” according to the authors.

The study also concluded that those patients with a coded diagnosis received significantly improved quality of care. “However, the quality of care was generally lower than that indicated in England,” according to the study.