Real-World Example: Phase III Biotech Automates Safety Case Intake and Narrative Drafting with Azure AI Foundry

Real-World Example: Phase III Biotech Automates Safety Case Intake and Narrative Drafting with Azure AI Foundry

1. Problem / Context

In Phase III, safety case volume spikes. A mid-market biotech (~$180M revenue) with a 12-person pharmacovigilance (PV) team faced long hours triaging inbox submissions, importing E2B(R3) ICSRs, suggesting MedDRA terms, and drafting narratives for expedited reports—all under FDA/EMA GxP expectations. Manual effort led to bottlenecks and rework. The team needed to keep pace with 7/15-day SUSAR timelines, maintain traceability, and withstand audits without adding a large headcount or sacrificing quality.

Using Azure AI Foundry and a governed agentic approach, the organization automated intake, standardization, MedDRA code suggestions, and first-draft narratives with citations, while keeping QPPV reviewers in control. The aim wasn’t “black-box AI,” but a validated, auditable system that accelerated cycle time and reduced rework without compromising compliance.

2. Key Definitions & Concepts

• Pharmacovigilance case intake: Aggregating adverse event reports from email, portals, call centers, and E2B(R3) gateways into a safety system.
• MedDRA coding suggestions: Proposing standardized medical terminology (PT/LLT) with confidence scores and evidence links.
• Narrative drafting: Creating a concise, chronological account suitable for expedited reporting and regulatory submission.
• Agentic AI: Coordinated AI agents that perform tasks (ingest, normalize, validate, draft) and hand off to humans at checkpoints.
• Azure AI Foundry: A platform to orchestrate models, evaluations, data connections, and deployment controls under enterprise-grade governance.
• QPPV: Qualified Person Responsible for Pharmacovigilance who oversees safety and release decisions.
• Provenance: The verifiable chain of sources, transformations, and decisions that support auditability.

3. Why This Matters for Mid-Market Regulated Firms

Mid-market biotechs operate under the same regulatory pressure as large pharma but with leaner teams and budgets. Every manual handoff increases cycle time and risk. Agentic automation on Azure AI Foundry helps standardize intake, reduce narrative rework, and provide the transparency auditors expect (citations, versioning, approvals). The result is a practical path to scale without brittle keyword parsing or uncontrolled generative outputs. For firms where a single audit finding can derail timelines, governed automation is a competitive necessity.

4. Practical Implementation Steps / Roadmap

1. Stand up a governed Azure AI Foundry workspace: private networking, role-based access, data encryption, and audit logging. Establish non-prod/prod separation and release gates.
2. Connect sources: monitored mailboxes for safety inboxes, E2B(R3) gateway feeds, and SFTP for attachments. Use OCR for scans and multilingual text support.
3. Normalize and validate: An intake agent harmonizes formats (email, PDF, E2B XML), deduplicates by case keys, and validates GxP-required fields (patient identifiers, suspect product, event, seriousness, dates). Missing or ambiguous fields trigger exception tasks—not silent failures.
4. MedDRA suggestions: A coding agent proposes PT/LLT terms with confidence levels and highlights the exact text spans driving each suggestion. Reviewers can accept, adjust, or reject with reason codes captured for learning.
5. Narrative drafting with citations: A narrative agent assembles a first draft aligned to your SOP templates (expedited vs periodic). It weaves a chronology, embeds citations back to source documents, and flags uncertainties for reviewer attention.
6. Human-in-the-loop and QPPV review: Reviewers edit drafts, confirm codes, and apply case-level decisions. Changes are tracked with timestamps, user IDs, and justifications, enabling clean audit trails.
7. Validated prompts and evaluations: Establish a bank of prompts and test cases with acceptance criteria (factual accuracy, citation coverage, template adherence). Track evaluation scores across releases.
8. Integration and release: Export finalized cases to the safety database (e.g., Argus, ARISg) with structured fields and attachments. Promote changes through controlled releases with quality gates, sign-offs, and rollback procedures.

5. Governance, Compliance & Risk Controls Needed

• GxP validation lifecycle: Plan IQ/OQ/PQ for the full workflow, not just the model. Document intended use, limitations, and change controls.
• Provenance and auditability: Preserve source-to-output lineage, including data versions, model versions, prompts, and human edits.
• Model risk management: Classify model risk, define guardrails, monitor for drift, and set thresholds that trigger human intervention.
• Human-in-the-loop checkpoints: Make QPPV review mandatory for expedited cases; block submission unless approvals are recorded.
• Privacy and security: Minimize PII/PHI exposure, apply masking where possible, and enforce least-privilege access.
• Vendor lock-in avoidance: Keep prompts, evaluation suites, and interface contracts under your control so you can swap models if needed.

A key differentiator in this program was the reliance on evaluated prompts, explicit provenance, and human checkpoints—avoiding brittle keyword parsing that breaks on real-world variability. Kriv AI, as a governed AI and agentic automation partner, helped the team formalize these controls and operate them reliably on Azure AI Foundry.

6. ROI & Metrics

The biotech realized measurable gains within the first release train:

• 40% faster case processing time, driven by automated intake, validation, and narrative first drafts.
• 25% fewer narrative reworks after QPPV review, thanks to citations and template adherence.
• 0 critical audit findings across internal QA and external inspections, supported by traceable lineage and approvals.

How to measure:

• Cycle time: Start at case arrival, end at QPPV approval. Baseline vs. post-automation median and P90 times.
• Coding accuracy: Agreement rates on MedDRA suggestions vs. final reviewer selections.
• Narrative quality: Rework rate and reason codes (missing chronology, unclear causality, incomplete citations).
• Compliance: On-time expedited submissions, change-control adherence, and audit observations.

Illustratively, for 2,000 annual cases with an average of 5 hours per case pre-automation, a 40% reduction returns 4,000+ hours annually—without sacrificing quality. With lean teams, that repurposed capacity is the difference between treading water and scaling Phase III.

7. Common Pitfalls & How to Avoid Them

• Treating the solution as “just an LLM”: Without validation plans, evaluation suites, and change control, you risk audit exposure.
• Brittle keyword parsing: Replace with structured normalization plus evaluated prompts and evidence-linked outputs.
• Skipping QPPV-centric design: Ensure reviewers see citations, confidence, and diffs—otherwise edits spike and trust falls.
• Ignoring MedDRA updates: Automate dictionary refreshes and re-evaluate suggestions after updates.
• One-step pilots: Avoid one-off prototypes; design end-to-end release trains with rollback and monitoring.

30/60/90-Day Start Plan

First 30 Days

• Discovery and governance boundaries: Confirm intended use, SOP alignment, and regulatory scope (expedited vs periodic).
• Inventory workflows and data: Map inboxes, E2B sources, safety system fields, and narrative templates.
• Data checks: Validate data quality, PII/PHI handling, and dictionary versions (MedDRA, WHO Drug).
• Architecture on Azure AI Foundry: Define environments, access, networking, and audit logging.

Days 31–60

• Pilot workflows: Implement intake normalization, validation, MedDRA suggestions, and narrative drafting for a defined product/region.
• Agentic orchestration: Configure agents with evaluated prompts, confidence thresholds, and exception routing.
• Security controls: Enforce RBAC, masking, and private networking; enable full lineage capture.
• Evaluation and review: Run test cases, compare against baselines, and refine templates with QPPV feedback.

Days 61–90

• Scaling and integration: Connect to the safety database, expand products/regions, and automate dictionary refreshes.
• Monitoring and metrics: Track cycle time, rework, coding accuracy, and submission timeliness; set alerts.
• Stakeholder alignment: Train PV reviewers, QA, and IT; finalize change control and release cadence.

9. Industry-Specific Considerations

• Regulatory pathways: Ensure alignment with FDA/EMA expectations for expedited SUSARs and E2B(R3) ICSR submissions; maintain region-specific narrative templates.
• Dictionaries and multilingual: Keep MedDRA and WHO Drug current; support translation for source narratives where needed.
• Safety systems: Plan clean handoffs to Argus/ARISg with structured fields and attachments; preserve case keys for deduplication.
• Documentation: Maintain validation deliverables (requirements, risk assessments, test scripts, deviation logs) ready for inspection.

10. Conclusion / Next Steps

This Phase III biotech proved that governed agentic automation can accelerate PV operations while strengthening compliance. By combining Azure AI Foundry with validated prompts, provenance, and human checkpoints, the team achieved faster throughput, fewer reworks, and clean audits.

If you’re exploring governed Agentic AI for your mid-market organization, Kriv AI can serve as your operational and governance backbone. As a mid-market–focused partner, Kriv AI helps with data readiness, MLOps, and the governance disciplines that keep AI practical, auditable, and ROI-positive.