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. DISCLOSURES This study received unrestricted partial funding from the Pharmaceutical Research and Manufacturers of America (PhRMA) Foundation Adherence Research Starter Award. Serna is employed by Cigna-HealthSpring. Mhatre is now employed with Genentech. The authors report no other potential conflicts of interest. The material in this study is based on work supported (or supported in part) by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, and the Center for Innovations in Quality, Effectiveness and Safety (CIN 13-413). The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the U.S. government. The abstract for this article was presented at the Academy of Managed Care Pharmacy's 28th Annual Meeting & Expo, April 2016, San Francisco, California, with the title "Development and Validation of a Tool to Predict Nonadherence to Oral Antidiabetic Drugs in Medicare Beneficiaries." Study concept and design were primarily contributed by Sujit Sansgiry and Mhatre, with assistance from the other authors. Mhatre, Serna, and Sujit Sansgiry took the lead in data collection, assisted by the other authors, and data interpretation was performed by Mhatre, Shubhada Sansgiry, and Essien, assisted by the other authors. The manuscript was written by Mhatre and Fleming, assisted by the other authors, and revised primarily by Mhatre, along with Sujit Sansgiry and assisted by the other authors.