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Case Study: The “Easy” Button for eTMF Classification Using AI to Streamline Classification of Clinical Trial Documents

Posted by
Keith

358 days ago

SUMMARY

A growing CRO recognized that the amount of time spent to ingest documents from multiple clinical sites over multiple trials significantly increased costs and project delays. As the number of trials expanded, the increase in costs and delays became even more troubling. The problem was compounded by a dwindling number of CRAs who possessed knowledge to classify these documents due to workforce challenges. The CRO set out to find a solution to more effectively utilize the people within the company.

ABOUT THE CLIENT

A growing CRO recognized that the amount of time spent to ingest documents from multiple clinical sites over multiple trials significantly increased costs and project delays. As the number of trials expanded, the increase in costs and delays became even more troubling. The problem was compounded by a dwindling number of CRAs who possessed knowledge to classify these documents due to workforce challenges. The CRO set out to find a solution to more effectively utilize the people within the company.

THE CHALLENGES

The first challenge was to ensure that the personnel reviewing the documents from the clinical sites were able to process those documents. Often, multiple documents were combined, scanned, saved in a single PDF and emailed to the CRAs. Extracting, separating, and determining what to do with each document was a very manual effort. CRAs needed to manually break up large files into several individual documents. After the documents were separated, CRAs needed to apply a standard naming convention.

Often clinical sites provided files with non-conforming file names. Once renamed, each document was filed into the appropriate location in the DIA eTMF file/folder structure. This labor-intensive process required knowledge of the overall eTMF structure and for the CRAs to read each document and determine the appropriate folder to store the document based on the content within.

THE SOLUTION



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