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

Objectives: In the United States, 25% of people with type 2 diabetes are undiagnosed. Conventional screening models use limited demographic information to assess risk. We evaluated whether electronic health record (EHR) phenotyping could improve diabetes screening, even when records are incomplete and data are not recorded systematically across patients and practice locations. Methods: In this cross-sectional, retrospective study, data from 9,948 US patients between 2009 and 2012 were used to develop a pre-screening tool to predict current type 2 diabetes, using multivariate logistic regression. We compared (1) a full EHR model containing prescribed medications, diagnoses, and traditional predictive information, (2) a restricted EHR model where medication information was removed, and (3) a conventional model containing only traditional predictive information (BMI, age, gender, hypertensive and smoking status). We additionally used a random-forests classification model to judge whether including additional EHR information could increase the ability to detect patients with Type 2 diabetes on new patient samples. Results: Using a patient's full or restricted EHR to detect diabetes was superior to using basic covariates alone (p<0.001). The random forests model replicated on out-of-bag data. Migraines and cardiac dysrhythmias were negatively associated with type 2 diabetes, while acute bronchitis and herpes zoster were positively associated, among other factors. Conclusions: EHR phenotyping resulted in markedly superior detection of type 2 diabetes in a general US population, could increase the efficiency and accuracy of disease screening, and are capable of picking up signals in real-world records.