Within the CTSA (Clinical Translational Sciences Awards) program, academic medical centers are tasked with the storage of clinical formulary data within an Integrated Data Repository (IDR) and the subsequent exposure of that data over grid computing environments for hypothesis generation and cohort selection. Formulary data collected over long periods of time across multiple institutions requires normalization of terms before those data sets can be aggregated and compared. This paper sets forth a solution to the challenge of generating derived aggregated normalized views from large, distributed data sets of clinical formulary data intended for re-use within clinical translational research.