OBJECTIVES: Millions of Americans encounter access barriers to medication due to cost. However, to date, there is no effective screening tool that identifies patients at risk of cost-related medication non-adherence (CRN).  By utilizing a big-data approach, we aimed to develop a novel method of identifying patients at risk of CRN. METHODS: By matching the dates of patients’ receipt of monthly social security (SS) payments and the dates of prescription orders for 559 Medicare beneficiaries who were primary SS claimants at high risk of hospitalization in an urban academic medical center, we identified patients who ordered their outpatient prescription within two days of receipt of monthly SS payments in 2014. We assessed the predictive power of this information on CRN, using multivariate logistic regression analysis.    RESULTS: Among the 559 Medicare patients at high risk of hospitalization, 137 (25%) reported CRN. Among those with CRN, 96 (70%) had ordered prescriptions on receipt of SS payments one or more times in 2014. The area under Receiver Operating Curve was 0.70 using the predictive model in multivariate logistic regression analysis.   CONCLUSIONS: Ordering prescription upon receipt of SS check is informative of cost-related medication non-adherence. The big-data approach is a valuable tool to screen patients at risk of CRN, and can be further expanded to the general population and subpopulations, providing a meaningful risk-stratification for CRN, and facilitating physician-patient communication to reduce CRN.