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Objectives: In the United States, 25% of people with type 2 diabetes areundiagnosed. Conventional screening models use limited demographic informationto assess risk. We evaluated whether electronic health record (EHR) phenotypingcould improve diabetes screening, even when records are incomplete and data arenot recorded systematically across patients and practice locations. Methods: Inthis cross-sectional, retrospective study, data from 9,948 US patients between2009 and 2012 were used to develop a pre-screening tool to predict current type2 diabetes, using multivariate logistic regression. We compared (1) a full EHRmodel containing prescribed medications, diagnoses, and traditional predictiveinformation, (2) a restricted EHR model where medication information wasremoved, and (3) a conventional model containing only traditional predictiveinformation (BMI, age, gender, hypertensive and smoking status). Weadditionally used a random-forests classification model to judge whetherincluding additional EHR information could increase the ability to detectpatients with Type 2 diabetes on new patient samples. Results: Using apatient's full or restricted EHR to detect diabetes was superior to using basiccovariates alone (p<0.001). The random forests model replicated on out-of-bagdata. Migraines and cardiac dysrhythmias were negatively associated with type 2diabetes, while acute bronchitis and herpes zoster were positively associated,among other factors. Conclusions: EHR phenotyping resulted in markedly superiordetection of type 2 diabetes in a general US population, could increase theefficiency and accuracy of disease screening, and are capable of picking upsignals in real-world records.

Electronic health record phenotyping improves detection and screening of  type 2 diabetes in the general United States population: A cross-sectional,  unselected, retrospective study