GMS Co-Create Integration

Our client is a global pharmaceutical company operating within a regulated pharmacovigilance environment, using the Global Medical Safety (GMS) platform to manage safety investigations, late cases, and compliance workflows across multiple regions and health authorities.

Deviation Memo (DM) management is a critical but complex component of the safety investigation process. The system supports multiple DM types, each governed by different workflow rules, approval cycles, and conditions.

Key challenges included:

• Manual, time-consuming processes
  Users must create, update, review, and rework DMs across multiple workflow steps and roles
• Inconsistent data entry
  Free-text fields and fragmented data structures lead to variability and reduced data quality
• Complex workflow logic
  DM requirements differ depending on workflow type and conditions (e.g., optional vs required, exclusions based on quality flags)
• Compliance risks
  Manual handling increases the likelihood of missed steps or incorrect application of business rules

Overall, the process created operational inefficiencies, delayed case closures, and increased compliance burden, making obvious a need for secure and scalable integration with AI/LLM-powered workflow assistance.

The proposed solution is a special integration layer that uses GMS Web APIs to automate the most repetitive DM activities. It is also positioned as a separate AI-enabled tool that provides recommendations for required investigation fields and can request creation of a DM with the two required fields: description of process delay and correction. This moves the user experience from manual form completion toward guided, API-driven workflow completion.

Core elements:

• API-based automation
  • Automated creation, attachment, and update of DMs
  • Batch processing for multiple investigations
  • Structured JSON payloads ensuring consistent data handling
• AI-powered co-creation tool
  • Recommendations of assignable cause categories and descriptors
  • Generation of required narrative inputs using LLM-assisted recommendations (process delay and correction)
  • User guidance through correct workflow using agent-based automation
• Workflow alignment
  • Business rules enforcement
  • Role-based approvals and signature flows
• Standardized data integration
  • Centralized dictionaries for categories, causes, and process points
  • Consistency across all investigations
  • Support for secure MCP-style integration patterns between APIs, workflows, and AI assistants

This solution will transform DM handling into a guided, semi-automated process embedded within existing workflows.

By combining GMS APIs with AI-assisted recommendations, the solution turns DM handling from a manual, role-heavy workflow into a guided and partially automated operating model. The result is not just efficiency; it is improved consistency, better compliance execution, and a more scalable pharmacovigilance platform.