Life Science — Automated Regulatory Deliverable Generation Pipeline

Overview

The purpose of this DAG is to automate the generation of regulatory deliverables, such as electronic Trial Master Files (eTMF), from structured data sources within the life sciences sector. The primary data sources include internal databases and research documents, which provide the necessary information for compliance and reporting. The ingestion pipeline begins with the collection of data from these sources, ensuring that all relevant information is captured accurately. Following data ingestio

The purpose of this DAG is to automate the generation of regulatory deliverables, such as electronic Trial Master Files (eTMF), from structured data sources within the life sciences sector. The primary data sources include internal databases and research documents, which provide the necessary information for compliance and reporting. The ingestion pipeline begins with the collection of data from these sources, ensuring that all relevant information is captured accurately. Following data ingestion, the processing steps involve the generation of documents based on predefined templates, which incorporate the collected data. Quality controls are implemented at various stages to ensure compliance with regulatory standards, including checks for data accuracy and completeness. Once the documents are generated, they are archived with full traceability to facilitate audits and ensure regulatory compliance. In the event of an error during processing, a recovery process is initiated to rectify issues promptly, minimizing disruption. Key performance indicators (KPIs) for monitoring include document generation time, error rates, and compliance audit readiness. This automated approach not only streamlines the documentation process but also significantly reduces the risk of non-compliance, ultimately delivering substantial business value by improving operational efficiency and ensuring adherence to regulatory requirements.

Part of the Literature Review solution for the Life Science industry.

Use cases

• Increased efficiency in regulatory document preparation
• Reduced risk of non-compliance with regulations
• Enhanced accuracy through automated data processing
• Improved audit readiness with traceable documentation
• Streamlined workflows leading to faster project timelines