Dealing with Inefficient SDTM Transformations
Clinical Programmers currently need to work with SDTM transformation processes that are manual, slow and inefficient — requiring multiple handoffs between functions and compromising data quality.
And with protocol complexity steadily increasing, it takes even longer to transform submission data, delaying patients from getting the treatments they need.
How Saama Can Help
We’ve developed a solution that automates the SDTM transformation process, simplifying and accelerating regulatory submissions. By applying advanced artificial intelligence (AI) and machine learning (ML) models to source data, our solution makes complex SDTM mapping suggestions that clinical programmers can approve or reject in a few clicks.
Up to 50% time savings for SDTM transformation
with Source to Submission (S2S)