Generic drugs play an important role in the U.S. healthcare system, but new post-marketing strategies are needed for evaluation of the safety and efficacy of generic drugs. This is particularly important for understanding how brand and generic drugs compare with authorized generics, which are a special kind of generic drug that contain the exact same active and inactive ingredients as the branded product, with the only difference being that they are labeled and marketed as generic drugs. A post-market surveillance system to compare usage and switching patterns among branded, authorized generic, and other generic products is necessary for effectively monitoring generic drugs. This proposal uses post-marketing data for selected example products to address three specific aims. Preliminary work uses novel approaches to characterize marketing intervals for relevant products, including creation of a reference list of National Drug Codes for use with claims data. Aim 1 then assesses utilization and outcomes for brand, authorized generic, and other generic drugs, including an evaluation of product switching and switchback rates. This aim is conducted at the person-level using one of the Nation’s most comprehensive and enduring electronic health records integrated with longitudinal patient and claims data. Aim 2 assesses market-level safety associated with entry of authorized generics and independent generics using the U.S. Food and Drug Administration adverse event reporting system database. Novel approaches to the evaluation of adverse event reporting rates for brand, authorized generic, and other generic drugs are tested. Aim 3 creates a pilot system for safety surveillance of generic drugs, again using longitudinal person-level data from a regional electronic health record linked with administrative claims. The pilot safety surveillance system assesses known examples of adverse events for the test set of authorized generic drugs, and also tests a set of adverse events that are known to be drug- related but not necessarily labeled events for the drugs being studied. Algorithms generated for Aim 1 and Aim 3 will be run against 5 additional data sources that represent significantly larger (approximately 75 million people) and more diverse populations. This will provide replication data for further study. Data generated from this post-market generic drug evaluation is important to (1) identify signals indicative of possible differences in efficacy or adverse event rates between specific branded and generic products, (2) provide empirical evidence for patient and provider education when no differences are detected between branded and generic products, and (3) explore whether authorized generics provide a useful control group in the post-market surveillance evaluation for comparing differences in safety and efficacy outcomes (or lack thereof) for specific products.