Portfolio Accelerator · Life Sciences · Databricks
Pharmacovigilance AI on Databricks: An Implementation Blueprint for Drug Safety Teams
A documented Databricks architecture for FAERS signal detection, ICSR case processing, and GxP-aligned governance as a working implementation partner would build it.
problem
What This Accelerator Proves, and What It Doesn't
This page describes a documented architecture for pharmacovigilance AI on Databricks, not a finished, running system. We're upfront about that distinction because a drug safety team evaluating an implementation partner needs to know exactly what's built versus what's designed, not a blended pitch.
context
The Problem: Adverse Event Signal Detection Is Still Manual at Most Pharma Companies
FAERS, the FDA's public adverse event reporting system, grows by hundreds of thousands of reports every quarter, and most pharmacovigilance teams still run signal detection and case narrative drafting as largely manual processes layered on top of legacy safety databases.
architecture
Architecture: How the Databricks Pharmacovigilance Hub Is Designed
FAERS Lakehouse Ingestion Design (Delta Lake and Unity Catalog)
The design calls for FDA FAERS quarterly data, DrugBank, DailyMed labels, and the MedDRA dictionary to flow into a Delta Lake medallion architecture (Bronze, Silver, Gold), governed by Unity Catalog with PHI tags, column masks, row filters, and full lineage tracking.
A 4-Algorithm Signal Detection Design (MLflow)
The signal-detection layer is designed to run four disproportionality-analysis algorithms in parallel as PySpark jobs, PRR, ROR, BCPNN, and MGPS, confirming a signal only when at least two algorithms independently flag the same drug-event pair, with every run tracked in MLflow for reproducibility.
Designed AI Case Processing: Narrative Drafting, MedDRA Coding, and PBRER Generation
Five Mosaic AI agents are specified in the design (SafetyCoordinator, CaseIntake, MedDRACoder, NarrativeWriter, and a PBRER aggregation agent), intended to draft ICSR case narratives from adverse-event data, suggest MedDRA coding with human review for ambiguous terms, and assemble sections of a Periodic Benefit-Risk Evaluation Report, with Microsoft Presidio running inline to de-identify PHI in free-text narratives before they reach the lakehouse.
status
What's Real Today, and What's Roadmap
Honestly: the setup and reference-repository collection phase is complete (18 reference repositories reviewed, project structure and agent-configuration files drafted). The data acquisition, signal-detection pipeline, Unity Catalog governance, ICSR case processing, and Mosaic AI agents described above are all specified in detail but not yet built and exercised against real FAERS data. We'll update this page with measured signal-detection results once the pipeline is running end to end.
compliance
Compliance Mapping: FDA, EMA, and ICH Requirements Designed Into the Data Model
The architecture is designed against FDA 21 CFR 314.80 (post-marketing adverse drug experience reporting), EMA GVP Module VI (collection and submission of adverse reaction reports), and ICH E2B(R3) (the international electronic ICSR transmission standard), with Unity Catalog access logs and Delta Lake time travel intended to provide the immutable audit trail those rules require. This is a design goal for the architecture, not a claim that the accelerator itself is FDA- or EMA-certified.
differentiation
Why Kriv AI Built This Design Instead of Just Pitching It
Manual ICSR processing is commonly estimated across the industry at $50 to $80 per case; AI-assisted triage and narrative drafting targets $5 to $10 per case, a market estimate, not a Kriv AI-verified benchmark for this specific accelerator. What we can say concretely: this design is built on a real, working pattern from Kriv AI's broader Databricks accelerator portfolio, reviewed against 18 real reference implementations, not assembled from a generic slide template.
engagement
From Accelerator to Your Environment: Implementation Path and Timeline
A scoped engagement builds this design out against your real FAERS feed and Databricks workspace, carrying the same Unity Catalog governance and multi-algorithm signal-detection approach through from day one.
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Straight answers
Frequently asked questions about Pharmacovigilance AI on Databricks: An Implementation Blueprint for Drug Safety Teams
Is the pharmacovigilance AI accelerator built and running on Databricks today?
No, and we want to be direct about that. The setup and reference-repository collection phase is complete, and the architecture (signal detection, case processing, governance) is fully specified, but the pipeline itself is not yet built and exercised against real FAERS data.
What signal-detection approach does the design use?
Four disproportionality-analysis algorithms designed to run in parallel, PRR, ROR, BCPNN, and MGPS, with a signal confirmed only when at least two algorithms independently flag the same drug-event pair.
Is any real patient data used?
No. The design uses FDA FAERS, a public regulatory dataset, not real patient data. Once built, Microsoft Presidio is designed to de-identify PHI in any free-text narrative before it reaches the lakehouse.
How does this map to FDA and EMA pharmacovigilance requirements?
The architecture is designed against FDA 21 CFR 314.80, EMA GVP Module VI, and ICH E2B(R3), using Unity Catalog access logs and Delta Lake time travel for the audit trail those rules require. This describes a design goal, not FDA or EMA certification of the accelerator.
What does the $50-80 per case manual-processing figure refer to?
That is a general industry market estimate for manual ICSR processing cost, not a measured result from this specific accelerator. We cite it as context for the problem size, not a guaranteed outcome.
Can Kriv AI build this out for our drug safety team?
Yes. A scoped engagement can build this design out against your real FAERS feed and Databricks workspace. Contact us to discuss scope and timeline.
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