AI Agentic Workflow for Automated Generation of Signal Detection Meeting Packets

This workflow eliminates 8-16 hours of manual data gathering per meeting by automating the extraction and synthesis of data from fragmented sources like the safety database (e.g., ARISg, Veeva Safety), literature surveillance feeds, and statistical signal outputs. An orchestrator, typically built with LangGraph for state management, triggers the process on a scheduled cadence or ad-hoc request. It dispatches specialized data agents to query each system, normalizing outputs into a unified data model for assembly. The primary ROI is the reallocation of highly skilled PV scientists from administrative compilation to higher-value signal assessment and medical review.

Implementation requires tight integration with legacy PV systems via APIs or secure data extracts. The document assembly engine applies branded templates, paginates data, and generates a single PDF or interactive dashboard. A mandatory medical review gate ensures data accuracy and clinical context before distribution via secure portals like Veeva Vault or SharePoint. Controls include version tracking, audit trails of all data queries, and configurable approval workflows to maintain compliance with internal SOPs and regulatory expectations for data integrity in safety decision-making.

This workflow eliminates the manual, multi-day process of gathering data from fragmented sources like Oracle Argus, PubMed, and internal analytics dashboards. By automating packet generation, safety committees receive consistent, up-to-date information for every meeting, compressing preparation from days to minutes. The operational upside is direct labor savings for PV associates and medical writers, plus faster, more informed committee decisions that accelerate risk mitigation actions.

Implementation uses a LangGraph-based orchestrator to manage agent state, data retrieval, and exception handling. Agents query systems via secure APIs, with results validated against business rules before compilation into a templated Word or PDF. A mandatory medical review gate ensures accuracy, with any flagged discrepancies routed back for correction. The final packet is distributed via encrypted email or a portal like Veeva Vault, with full audit trails for compliance.

Phase 1 establishes the data ingestion and normalization layer, connecting to primary sources like the safety database (e.g., Oracle Argus, Veeva Safety), literature surveillance feeds, and internal analytics platforms. This foundational layer uses dedicated agents with validation logic to extract, clean, and stage the latest case counts, disproportionality analysis results, and literature findings in a structured data mart. The immediate ROI is the elimination of manual data pulls and spreadsheet consolidation, saving 10-15 hours of analyst time per meeting cycle while improving data traceability and version control from the outset.

Phase 2 builds the orchestration and assembly logic using a framework like LangGraph to manage the workflow state. An orchestrator agent sequences tasks: triggering data pulls post-analytics run, invoking a document assembly agent to populate pre-approved PowerPoint or Word templates, and routing the draft packet to a secure review queue. A critical control is the configurable human-in-the-loop gate, where a safety scientist reviews, annotates, and approves the packet before distribution. This phase delivers the core automation benefit, compressing a multi-day manual process into a few hours with full auditability. Phase 3 focuses on integration with collaboration tools (e.g., MS Teams, SharePoint for automated distribution) and scaling the workflow to additional product teams.

This workflow automates the labor-intensive, multi-day process of manually pulling case line listings, literature findings, and aggregate analysis from disparate systems like Oracle Argus, Veeva Vault, and PubMed. By deploying orchestrated data agents, it compiles a structured, review-ready document packet, ensuring committees have consistent, up-to-date information. The operational upside is a 60-80% reduction in administrative preparation time per meeting, allowing PV scientists to focus on signal assessment rather than data wrangling, directly compressing safety review cycles.

Implementation requires a phased rollout, starting with a single product team to validate data accuracy and user acceptance. Critical controls include a mandatory medical review gate for the final packet, immutable audit trails of all data queries and agent actions, and configurable approval workflows in ServiceNow for exceptions. The architecture must integrate with existing identity management for access control and include monitoring dashboards in Datadog to track agent performance, data freshness, and any QC failures, ensuring the system operates within defined SLAs for reliability and compliance.

This automation directly targets a high-volume, repetitive operational bottleneck: the manual aggregation of data from fragmented systems like Oracle Argus, Veeva Safety, literature databases, and statistical tools. By deploying orchestrated data agents, it eliminates 10-20 hours of administrative prep per meeting, ensuring committees have consistent, up-to-date information. The business value is faster signal assessment cycles and the reallocation of skilled PV associates from data compilation to higher-value medical analysis and decision-making.

Implementation requires integrating with legacy PV systems via APIs or secure data exports. The orchestrator, built on LangGraph, manages state and handoffs between specialized data-fetching agents. Critical controls include a mandatory human-in-the-loop review gate for medical accuracy, versioning for audit trails, and secure distribution to meeting participants via encrypted channels or portals like Veeva Vault. Rollout sequencing typically starts with a single product line to validate data fidelity and reviewer acceptance before scaling.

Manual compilation of signal detection meeting packets is a significant operational bottleneck, consuming 15-20 hours of analyst time per meeting. This workflow automates data retrieval from fragmented sources—safety databases like ARISg, literature surveillance outputs, and case line listings—using specialized agents. It structures this information into a review-ready document, ensuring data consistency and traceability while freeing PV scientists for higher-value analysis. The architecture must integrate with legacy PV systems and enforce strict version control for auditability.

Implementation requires a stateful orchestrator, like LangGraph, to manage agent execution, handle data validation failures, and route exceptions. Each data-fetching agent integrates via APIs (e.g., to Oracle Argus) or direct database queries. The document assembly logic uses templating engines populated with validated data, followed by automated secure distribution via email or portals like Veeva Vault. Critical controls include a medical reviewer approval gate for low-confidence data, comprehensive audit logging, and version locking upon distribution to prevent post-submission changes.