Enhancing Adverse Event Management with Agentic AI: A Paradigm Shift in Pharmacovigilance

Adverse Event (AE) reporting and case management are among the most critical components of pharmacovigilance. They ensure patient safety, regulatory compliance, and the scientific integrity of clinical research and post-marketing surveillance. However, the increasing complexity, volume, and regulatory expectations associated with AE management pose substantial operational challenges to life sciences organizations.

In this context, Agentic AI—a new generation of self-directed, context-aware artificial intelligence—offers significant potential to transform how organizations manage adverse events. Unlike conventional automation tools, Agentic AI systems can independently make context-informed decisions, continuously learn from feedback, and dynamically adapt to evolving processes, thereby augmenting human capabilities across the pharmacovigilance value chain.

Challenges in Adverse Event Management Today

Pharmacovigilance professionals routinely encounter a range of challenges, including:

• Disparate and unstructured data sources (e.g., spontaneous reports, call center notes, EHRs)

• Manual, time-consuming data entry and narrative drafting

• Inconsistent causality assessments and case classification

• Frequent reconciliation issues between clinical and safety databases

• Increasingly complex global regulatory requirements

These challenges not only impact operational efficiency but also elevate the risk of compliance deviations, particularly in the context of high case volumes or limited staffing.

The Role of Agentic AI in Modernizing AE Workflows

Agentic AI addresses these challenges by introducing a layer of intelligent autonomy into AE case management. Below are key areas where it delivers measurable value.

1. Automated and Intelligent Case Intake

Agentic AI can ingest and process AE reports from multiple channels—emails, structured forms, call transcripts, EHR notes—and transform unstructured inputs into structured, MedDRA-coded data entries. The system identifies missing or ambiguous information and flags it for user review, ensuring both efficiency and accuracy at the intake stage.

2. Narrative Generation and Standardization

Drafting AE narratives is a labor-intensive process that varies significantly by region, reviewer, and therapeutic area. Agentic AI generates preliminary narratives using contextual understanding of the case, standard medical terminology, and prior accepted narratives, while remaining aligned with local regulatory expectations. This allows pharmacovigilance professionals to focus on clinical nuance and final validation.

3. Evidence-Based Causality Support

Assessing the likelihood that a product caused an adverse event requires significant clinical judgment. Agentic AI supports this process by providing evidence-based recommendations drawn from historical case data, literature references, product safety profiles, and similar reported cases. This augments consistency in decision-making while preserving the reviewer’s autonomy.

4. Real-Time Regulatory Cross-Verification

With the regulatory environment continuously evolving, Agentic AI ensures alignment with the latest global safety regulations. It automatically cross-checks AE case attributes against regulatory thresholds (e.g., seriousness, relatedness, reportability) and suggests appropriate actions, such as expedited reporting or safety label review.

5. Systematic Reconciliation and Audit Readiness

Agentic AI facilitates real-time reconciliation between clinical and safety data systems. It identifies discrepancies, offers traceable justifications, and maintains a comprehensive audit trail, significantly reducing the burden of manual reconciliation and ensuring inspection readiness.

Quantifiable Benefits of Agentic AI Adoption

Organizations that have piloted or adopted Agentic AI in their pharmacovigilance functions are reporting substantial operational improvements:

• 50–60% reduction in average case processing time

• Improved narrative quality and uniformity

• Greater regulatory accuracy and reduced compliance risks

• Enhanced productivity and reduced cognitive burden on PV teams

These benefits translate into faster reporting timelines, improved oversight, and increased capacity for strategic safety monitoring activities.

Conclusion: Empowering Human Expertise with Intelligent Automation

Agentic AI represents a meaningful advancement in pharmacovigilance technology. It complements, rather than replaces, the expertise of safety professionals by managing repetitive, data-intensive tasks with precision and learning capability. In doing so, it enables human experts to focus on complex decision-making, medical evaluation, and regulatory strategy.

As the volume and complexity of safety data continue to grow, integrating Agentic AI into AE management processes offers a scalable, compliant, and forward-looking solution for life sciences organizations committed to patient safety and operational excellence.