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Databases of medical records contain a wealth of information critical to many areas of research including drug safety, health outcomes, clinical epidemiology and translational medicine. Through commercially available databases, researchers can gain a better understanding of the impact of exposure to drugs and medical devices, identify populations at risk for adverse effects, estimate the prevalence and natural history of medical conditions, and assess drug utilization across different demographic groups. However, the daunting size and complexity of these databases as well as lack of convenient tools to mine them have made this information largely inaccessible to all but a few experts with advanced data management and statistical programming skills. Using a combination of a relational data management strategy and a graphical user front-end, we have developed an approach that allows any medical researcher to perform a number of common searches and analyses in a consistent, intuitive and interactive manner, without the assistance of an expert programmer. Moreover, the optimization work done on the database and application sides have dramatically reduced the time needed to analyze the data and, thus, increased the number of studies that can be performed. A crucial part of any such study is the selection of code lists for diseases, procedures, medications, etc., and we have supported this effort by allowing definitions to be queried using common ontologies and shared conveniently across the organization.