While electronic data capture brought modern computer front ends to clinical trials, back-end processing of clinical data remains relatively medieval. Trial designs for new products evolve rapidly, forcing downstream analysis teams to stovepipe each uniquely structured source data set into separate schemas and hand-adapt the extract, transform and load (ETL) code. Because this tactical approach is cumbersome, time-consuming and non-repeatable, it does not scale well, and many clinical data warehouse initiatives by even the biggest pharma/biomed companies implode under the pressure of mushrooming costs and elusive ROI.

The solution to warehousing the ever-changing structures of clinical data will require 1) a single, multi-trial repository based upon abstract rather than directly representational data models, and 2) adaptive, meta data-driven ETL programming.

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