MedDRA auto-coding
Trained on millions of case narratives, our models propose MedDRA terms with every suggestion traceable to the source text that supports it.
Case processing
Vigintake reads, extracts, codes and drafts every incoming adverse event, then hands a reviewable case to your safety scientists. Less rework. Consistent timelines. Human judgement where it counts.
“The patient reported persistent nausea following the initiation of treatment. The event was reported by a healthcare professional.”
A healthcare professional reported persistent nausea in a patient following initiation of treatment.
Ready for clinical reviewEvidence and source available
The challenge
Safety teams at service providers and biotechs face growing pressure: higher case volumes, tighter timelines, increased regulatory scrutiny, and constrained resources.
Staff manually reading PDFs, emails, and faxes to extract case data. The process is time-consuming and susceptible to human error at every step.
QC rejections, MedDRA mis-coding, and incomplete narratives require multiple rounds of review before a case reaches submission readiness.
Case volumes are unpredictable, but 15-day SUSAR deadlines are not. Managing this gap introduces compliance risk and operational strain.
How it works
One pipeline from any source document to an approved E2B(R3) case, with your reviewers making the final call.
Cases arrive from any channel: email, fax, phone calls and audio recordings, e-commerce platforms, portals, and call centres. Vigintake consolidates every source into a single, structured intake queue.
Language models parse unstructured text, extract every ICSR field, apply MedDRA coding, and classify the case for regulatory triage in minutes.
AI drafts the full case narrative and populates the E2B(R3) structure, covering patient data and seriousness criteria, ready for medical review.
Your safety scientists approve. Structured export to Argus, ARISg, VigiFlow, or E2B XML follows, with a full audit trail and version history at every stage.
Core capabilities
Every capability is built to be traced. Each extraction, code and sentence points back to the evidence that produced it.
Trained on millions of case narratives, our models propose MedDRA terms with every suggestion traceable to the source text that supports it.
Adverse events are received in any language. Our models translate, extract, and code while preserving clinical nuance and regulatory context.
Structured, ICH E2B-compliant narratives generated in seconds. Consistent style, complete content, and significantly reduced time to authorisation.
AI-assisted causality classification using WHO-UMC criteria. Borderline cases are flagged for human review; clear-cut cases are routed automatically.
Probabilistic matching across sources ensures follow-up cases are correctly linked to the master case, preventing duplicate submissions.
Every decision, edit, and override is timestamped and immutable. Inspection readiness is embedded in the workflow, not added as an afterthought.
Who it's for
Managing case volumes across multiple clients, each with different databases, SLAs, and reporting requirements, requires exceptional operational discipline. Vigintake’s multi-tenant architecture and configurable workflows support higher throughput per FTE.
Establishing a compliant pharmacovigilance operation from the ground up requires both regulatory expertise and scalable infrastructure. Vigintake accelerates GVP compliance and reduces dependence on manual headcount through intelligent automation.
Integrations
Vigintake integrates with industry-standard safety databases via HL7 FHIR, E2B XML, and REST APIs, without requiring disruptive changes to your existing systems.
Get started
See Vigintake process a real adverse event case from intake to E2B(R3), using your own data.