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.

Related Products

Source to Submission (S2S)

Saama Llama Says

Chief Efficiency Officer

Up to 50% time savings for SDTM transformation - with Source to Submission (S2S)

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