An enterprise London health-tech company managing 12 concurrent clinical studies found MHRA adverse-event reporting slow and error-prone.
The full workflow from physician notes to submission took 72 hours per event, a contractor team cost £320,000 a year, and MedDRA coding accuracy ranged only 89–93% given the difficulty of mapping clinical language to 80,000+ terms.
Dextra Labs built a two-agent pipeline: a clinical-extraction agent using a fine-tuned medical NER model (trained on 12,000 annotated notes) to pull structured data from narratives; and a compliance-and-submission agent that maps entities to MedDRA preferred terms, checks drug relatedness against protocols via RAG, and generates MHRA-compliant XML. A confidence-scoring system routes only uncertain fields to human reviewers.
Adverse-event cycle time to review-ready status
Contractor team eliminated within four months
MedDRA coding accuracy, up from 89–93%; CRAs reclaimed 60% of their time
- How One E-Commerce Co Cut Processing Cost 84%Fractional AI · Retail & E-Commerce
- How One Eyewear Brand Lifted Checkout 10% to 15%DevDash Labs · Retail & E-Commerce
- How One Gov Data Firm Cut Doc Time 6 Hours to 15 MinMachine & Partners · Government & Public Sector
- How Cando Rail Cut Risk-Assessment Time 83% With AIFractional AI · Transportation & Logistics