This study will focus on the cohort of 20,000 established patients cared for by 31 attending physicians in the outpatient, adult primary care practices at UT Southwestern (two general internal medicine one family medicine and one geriatric practice). The investigators will develop and implement an automated Diabetes Detection Tool (DDT) that does data mining on electronic medical record (EMR) lab data to systematically identify all primary care patients with elevated random plasma glucose results (RPGs) who are at high risk of diabetes and thus in need of further testing. In a cluster-randomized trial, primary care providers will be randomized to either the intervention/DDT arm or usual care. Providers in the intervention arm will receive visit-based, EMR-enabled case identification and real-time decision support. Outcomes will be tracked at a patient level. All subjects will be followed for 12 months to assess rates of follow-up diabetes testing, time to testing, rates of subsequent diabetes diagnosis, and time to diagnosis. The investigators hypothesize that the visit-based provider decision support will be superior to usual care.