Pharmacovigilance on the Lakehouse: Agentic Safety Signal Detection for Mid-Market Pharma

Pharmacovigilance on the Lakehouse: Agentic Safety Signal Detection for Mid-Market Pharma

1. Problem / Context

Mid-market pharmaceutical companies carry the same post-market safety obligations as large enterprises—periodic reporting, signal detection, and rapid response—without the same depth of staff or budget. Safety teams are asked to detect adverse events earlier across a growing volume of sources: EHR extracts, CRM interactions, and spontaneous reports. Manual case triage and spreadsheet-driven signal reviews slow everything down. The result: delayed time-to-signal, inconsistent narratives, and audit risk if traceability isn’t airtight.

A modern Lakehouse approach provides a governed backbone for pharmacovigilance (PV): ingest diverse sources at scale, maintain lineage and role-based access, orchestrate signal detection pipelines, and use agentic automation to prioritize and draft cases while keeping humans firmly in the loop. The outcome isn’t hype—it’s fewer manual hours and faster signals with auditable controls.

2. Key Definitions & Concepts

• Lakehouse: A unified data architecture that combines the reliability of data warehouses with the flexibility of data lakes. For PV, it centralizes EHR extracts, CRM notes, and spontaneous reports in governed Delta tables.
• Delta Live Tables (DLT): Declarative pipelines for building reliable ingestion and transformation jobs with built-in testing and quality expectations.
• MLflow: End-to-end ML lifecycle management—experiment tracking, model registry, deployment stages with approvals—supporting repeatable signal detection models.
• Agentic Automation: Goal-driven AI agents that coordinate steps across systems. In PV, agents can auto-prioritize cases, suggest follow-up queries, and draft narratives for human review.
• Unity Catalog: Centralized governance for data, models, and AI artifacts, providing lineage, role-based access, and policy enforcement.
• GxP Readiness: Validation, traceability, change control, and approvals aligned to regulated expectations for systems impacting safety processes.

3. Why This Matters for Mid-Market Regulated Firms

Safety groups of 5–20 people cannot scale manual reviews to meet rising data inflows and regulators’ expectations for proactive monitoring. The mid-market reality includes:

• Resource constraints: Limited data engineering and PV operations bandwidth.
• Compliance burden: GxP-aligned validation, audit trails, and documented approvals.
• Data fragmentation: EHR, CRM, and spontaneous sources in inconsistent formats.
• Cost pressure: Need to show ROI within quarters, not years.

A Lakehouse with governed agentic workflows lets lean teams improve time-to-signal, standardize narratives, and preserve traceability—without standing up a sprawling toolset.

4. Practical Implementation Steps / Roadmap

1. Ingest and standardize data with DLT
2. Build signal detection pipelines
3. Introduce agentic case handling with human-in-the-loop
4. Integrate with safety operations and reporting
5. Deliver production reliability

• Sources: EHR extracts, CRM notes, spontaneous reports/intake forms.
• Apply DLT expectations for schema checks, deduplication, and PII handling (tokenization or masking for non-safety reviewers).
• Normalize terms (e.g., map to MedDRA), harmonize product dictionaries, and maintain a golden case view.

• Start with transparent methods (e.g., disproportionality calculations) and add ML classifiers for seriousness/expectedness as evidence accumulates.
• Use MLflow to track experiments, register models, and promote from Staging to Production with documented approvals.
• Schedule batch or near-real-time runs that output candidate safety signals and confidence scores.

• Auto-prioritize cases using severity, product relevance, reporter type, and recency.
• Draft case narratives from structured fields and free text; flag missing information; generate follow-up questions for medical review.
• Route to safety officers with pre-filled summaries, links to source evidence, and rationale for prioritization.

• Publish curated outputs to downstream safety systems or case management tools.
• Maintain clear provenance for every data element and model decision so reviews and audits can trace “what, who, when, and why.”

• Implement runbooks, observability for pipelines, data quality SLAs, and fallback procedures.
• Document and test change control—especially for models—before promotion.

Kriv AI, as a governed AI and agentic automation partner for mid-market organizations, helps teams operationalize this end-to-end: data readiness, MLflow practices, and controlled agentic orchestration that respects PV workflows and approvals.

5. Governance, Compliance & Risk Controls Needed

• Unity Catalog lineage and access: Register datasets, notebooks, models, and agents with lineage. Enforce role-based access controls and row/column-level policies for PII.
• GxP Validation: Define and execute validation plans (IQ/OQ/PQ equivalents). Store evidence, test results, and approvals tied to specific pipeline/model versions.
• Change Control & Approvals: Require multi-party sign-offs for model promotions and pipeline changes; capture electronic approvals and timestamps.
• Auditability & Traceability: Persist run metadata (parameters, data versions, model hashes, prompts) so a case decision can be reconstructed.
• Human Oversight: Mandate human review for agentic narrative drafts and signal escalations; design clear override and rollback paths.
• Vendor Lock-in Risk Management: Favor open formats (Delta), portable models, and documented interfaces, reducing switching risk.

A governance-first approach is essential. Kriv AI helps mid-market PV teams implement pragmatic guardrails—Unity Catalog policies, MLflow gates, and human-in-loop checkpoints—so automation never outruns oversight.

6. ROI & Metrics

Executives need measurable impact, not pilots that linger. Track a balanced scorecard:

• Time-to-signal: Days from data arrival to candidate signal surfaced. Target reductions of 30–60% as pipelines mature.
• Case cycle time: Average hours from intake to medically reviewed narrative; agentic drafting often trims 20–40%.
• Manual hours eliminated: Reduction in repetitive triage and documentation work.
• Signal quality: Precision/recall of candidate signals vs. baseline reviews; trend false positives down as models learn.
• Compliance indicators: Number of audit findings, validation deviations, and unapproved changes—should move toward zero.
• Cost-to-serve: Cost per case processed and per signal investigated.

Example: A specialty pharma with a 12-person safety team connected EHR extracts, CRM notes, and spontaneous reports through DLT, added MLflow-tracked models for expectedness and severity, and introduced agentic drafting. Within two quarters, they reduced time-to-signal from 10 days to 4–6 and cut manual documentation time per case by ~30%, while improving audit readiness through lineage and approvals.

7. Common Pitfalls & How to Avoid Them

• Skipping data contracts: Without clear source schemas and MedDRA alignment, pipelines drift. Use DLT expectations and schema versioning.
• Black-box models: Regulators and auditors expect explainability. Start with transparent metrics and add interpretable ML features.
• Over-automation: Agents must never finalize case decisions. Keep human approval steps and clear escalation paths.
• Weak change control: Untracked notebook edits or model swaps create audit risk. Use MLflow registry stages and electronic approvals.
• Ignoring privacy boundaries: Apply row/column policies and tokenization so only the right roles see identifiable data.
• Underestimating validation effort: Treat validation as part of delivery, not an afterthought; store evidence and test artifacts centrally.

30/60/90-Day Start Plan

First 30 Days

• Discovery: Inventory PV workflows (intake, triage, narrative drafting, signal review).
• Data Checks: Identify EHR, CRM, and spontaneous report sources; define schemas, MedDRA versions, and quality thresholds.
• Governance Boundaries: Set Unity Catalog workspaces, roles, lineage, and data masking policies.
• Validation Plan: Draft IQ/OQ/PQ-aligned test plans for pipelines and models.

Days 31–60

• Pilot Workflows: Stand up DLT pipelines for intake and normalization; implement disproportionality calculations and a baseline classifier tracked in MLflow.
• Agentic Orchestration: Introduce an agent to auto-prioritize and draft narratives with human-in-the-loop review.
• Security Controls: Enforce role-based access, row/column-level policies; enable run metadata capture.
• Evaluation: Measure time-to-signal and case cycle time vs. baseline; gather medical reviewer feedback.

Days 61–90

• Scaling: Add additional sources, expand to near real-time jobs, and harden SLAs and runbooks.
• Monitoring: Establish dashboards for pipeline health, model drift, and agent interventions; schedule periodic validation re-tests.
• Metrics & Payback: Quantify manual hours saved and time-to-signal improvements; prepare a scale-out business case.
• Stakeholder Alignment: Align PV, QA, IT, and compliance on change control cadence and release process.

9. (Optional) Industry-Specific Considerations

• Coding Standards: Maintain MedDRA versioning and change impact analysis for term updates.
• Regulatory Interfaces: Design for downstream reporting (e.g., E2B(R3)) and ensure evidence trails for periodic reports.
• Case Systems: Integrate with your safety database via governed connectors, preserving provenance and approvals.
• Rare Events: For low-signal products, rely more on rule-based triage plus expert review; use ML conservatively with transparent features.

10. Conclusion / Next Steps

For mid-market pharma, a Lakehouse foundation with governed agentic automation can speed safety signal detection while reinforcing compliance. By unifying data through DLT, managing models with MLflow, enforcing governance via Unity Catalog, and keeping humans in the loop, teams gain measurable improvements in time-to-signal and case throughput without sacrificing auditability.

If you’re exploring governed Agentic AI for your mid-market organization, Kriv AI can serve as your operational and governance backbone—helping you move from scattered pilots to production PV workflows with clear ROI and verifiable controls.