BACKGROUND: Low adherence to oral antidiabetic drugs (OADs) in the Medicare population can greatly reduce Centers for Medicare & Medicaid Services (CMS) star ratings for managed care organizations (MCOs). OBJECTIVE: To develop and validate a risk assessment tool (Prescription Medication Adherence Prediction Tool for Diabetes Medications [RxAPT-D]) to predict nonadherence to OADs using Medicare claims data. METHODS: In this retrospective observational study, claims data for members enrolled in a Medicare Advantage Prescription Drug (MA-PD) program in Houston, Texas, were used. Data from 2012 (baseline period) were used to identify key variables to predict adherence in 2013 (follow-up period). Members aged 65 years and older with a diabetes diagnosis, at least 1 prescription for OADs (biguanides, sulfonylureas, thiazolidinediones, dipeptidyl peptidase-4 inhibitors, or meglitinides), and continuously enrolled for both years were included in the study. Patients with insulin prescriptions were excluded from the cohort. The study outcome, nonadherence in 2013, was defined as proportion of days covered (PDC) < 80%. Multivariable logistic models using 200 bootstrap replications (with replacement) identified factors associated with nonadherence. The final model was tested for discrimination and calibration statistics and internally validated using 10-fold cross-validation. Using weighted beta coefficients of the predictors, the RxAPT-D was created to stratify nonadherence risk and was tested for sensitivity, specificity, positive prediction value, and negative prediction value. The predictive ability of the tool was compared with that of past PDC values using net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. RESULTS: Data from 7,028 MA-PD members were used for tool development. Seven predictors (age, total OAD refills, total OAD classes filled, days supply of last filled OAD, pill burden, coverage of last filled OAD, and past adherence) statistically significant in ≥ 50% of the bootstrapped samples were identified from the logistic models. The final model demonstrated good discrimination (c-statistics = 0.75) and calibration (Hosmer-Lemeshow goodness-of-fit P > 0.05) statistics, with good internal validity (area under the curve = 0.73). The RxAPT-D demonstrated adequate sensitivity statistics: sensitivity = 0.73, specificity = 0.63, positive prediction value = 0.74, and negative prediction value = 0.62. Compared with use of past adherence measures, the RxAPT-D had higher prediction ability, relative IDI = 2.09, and user defined NRI = 0.16 with 24% events correctly reclassified. CONCLUSIONS: The RxAPT is an effective tool to identify patients who are likely to become nonadherent to OADs in the follow-up year. Pharmacists in MCOs can use this tool to identify patients expected to be nonadherent to OADs and develop targeted intervention programs to assist in improving MCO CMS star ratings.