Pharmaceutical Companies who conduct clinical trials, looking to get new treatments to market as quickly as possible, possess a high volume of documents. Millions of documents can be created as part of one trial and are stored in a document management system. In case migrating these documents to a new system is needed – for example, when a pharma company acquires the rights to a drug or trial – all these documents must often be read manually in order to classify them and extract metadata that is legally required and must be accurate. Traditionally, this migration is a long, complex, and labor-intensive process.
We present a solution based on the natural language processing (NLP) system which provides:
- Speed – 80% reduction of manual labor and migration timeline, proven in major real-world projects
- State of the art accuracy – based on Spark NLP for Healthcare, integrated in a human-in-the-loop solution
- End-to-end, secure and compliant solution – Air-gap deployment, GxP and GAMP 5 validated
We will share lessons learned from an end-to-end migration process of the trial master file in Novartis.
