UCSF

Within the CTSA (Clinical Translational Sciences Awards) program academic medical centers are tasked with the storage of clinical laboratory data within an Integrated Data Repository (IDR) and the subsequent exposure of that data over grid computing environments for hypothesis generation and cohort selection. Lab data that is collected from multiple machines over long periods of time from many labs and across multiple institutions requires normalization before data sets can be aggregated and compared. However, lab data normalization is difficult when published reference intervals are not always reliable and when the lab data collected is not always normally distributed. This paper sets forth a proposed solution to the challenge of generating derived aggregated normalized views from large, distributed data sets of clinical lab data intended for re-use within clinical translational research.