P314: Accelerating EU IDMP Data Migration: AI-Powered Risk Mitigation Strategies for Improved Compliance
Poster Presenter
Joan Cheung
Principal Consultant
NNIT United States
Objectives
This case study explores AI-driven data extraction and migration strategies for improved EU IDMP compliance for an international pharmaceutical company with 7 products and 17 product variants and over 17,000 records updated.
Method
To address these challenges, an AI-driven data migration framework was implemented, utilizing:
1. AI-Powered Document Parsing
2. Automated Language Translation
3. AI-Based Data Mapping
4. Regulatory Compliance Synchronization to EMA’s SPOR databases
Results
In the case study, the company identified 17,450 records, encompassing approximately 85,844 fields requiring remediation. The results included:
• 94% of fields manually reconciled with high accuracy through expert regulatory review.
• 6% of fields reconciled using a semi-automated process, reducing manual workload.
• Completion of the remediation process in under three months, significantly faster than the industry norm of 6 to 12 months.
• Integration with Veeva Vault RIMS enabled the successful mapping of the majority of required IDMP data points, streamlining PMS data submissions.
• Regulatory alignment ensured compliance with EMA’s evolving IDMP roadmap and the anticipated business rules from the 2024/2025 regulatory cycle.
Leveraging New Tech for Ongoing Compliance:
• Digital Adoption Platforms (DAPs): The value of DAPs to facilitate data validation, support organizational change management, and enhance ongoing compliance with new data fields and processes was considered. DAPs can provide contextual guidance, real-time feedback, and automated error detection, helping users navigate complex system changes and regulatory requirements.
• Embedded AI Agent within RIMS: The AI agent can streamline IDMP data extraction, remediation, and compliance by automating data retrieval, validation, and structuring across Veeva Vault RIM and external sources. AI agent can enhance document classification, translation, and comparison, ensuring IDMP fields align with SPOR databases and EMA guidelines while reducing manual effort. The simple chat-style interface and functionality facilitates end user adoption and increases understanding of updated business processes for improved compliance.
Conclusion
Implementing AI and automation for IDMP data remediation and submission preparation accelerates compliance, reduces manual effort, and improves data accuracy across pharmaceutical regulatory systems. Regulatory Affairs expertise remains indispensable, particularly amidst evolving guidance and pending business rules. By aligning with EMA’s SPOR database, utilizing semi-automated data synchronization, and adopting digital platforms for enhanced user compliance, companies can streamline data exchange with PMS and future-proof their regulatory submissions for upcoming IDMP enforcement phases.
