[Webinar] Automating Clinical Trial Master File Migration & Information Extraction

This video explores the significant challenges and an AI-powered solution for automating Clinical Trial Master File (TMF) migration and information extraction within pharmaceutical companies. It highlights that TMF migration is a complex, labor-intensive process due to the massive volume of unstructured documents (scanned, handwritten), lack of standardization, and bespoke rules, rendering manual methods and traditional Robotic Process Automation (RPA) ineffective. The presented solution leverages Natural Language Processing (NLP) and Artificial Intelligence, including advanced OCR and machine learning, to classify documents, extract critical metadata, and ensure data accuracy and regulatory compliance. A detailed case study with Novartis demonstrates the real-world application, showcasing substantial reductions in manual effort and migration timelines while adhering to stringent industry standards like GxP and GAMP 5.

Key Takeaways:

• TMF Migration Complexity: Clinical Trial Master File migration is inherently complex due to the lack of content standardization, high volume of unstructured data (scanned, handwritten), and non-explicit, bespoke rules, making manual or basic automation approaches impractical.
• AI-Driven Efficiency & Accuracy: AI and NLP-based systems, incorporating advanced OCR and machine learning, can achieve an 80% reduction in manual labor and migration timelines for TMFs, significantly improving efficiency and accuracy in metadata extraction and document classification.
• Critical Solution Components: Successful AI solutions for TMF migration rely on meticulously defined annotation guidelines, sophisticated post-processing rules (which can boost accuracy by 30-40% by handling ambiguities like multiple dates or names), and machine learning-driven false positive detectors to ensure data quality.
• Regulatory Compliance & Enterprise Readiness: Solutions must be designed for enterprise deployment, offering scalability (e.g., Apache Spark-based), on-premise (air-gapped) security, and rigorous validation for regulatory compliance, including GxP and GAMP 5.
• Collaborative Expertise: Effective implementation requires a multi-disciplinary team combining deep AI/data science expertise with extensive subject matter knowledge in TMF, document management, IT security, quality, and validation from both the solution provider and the client.