Aushadhi
AI-Powered Medical Coding, Built for Speed, Consistency, and Audit Readiness
Built for Intelligent Medical Coding Workflows
Aushadhi is an AI-powered medical coding platform built to modernize the coding lifecycle acrossclinical trials and pharmacovigilance workflows. It helps teams process verbatim terms faster,improve coding consistency, and reduce manual effort through intelligent automation and governedreview workflows.
Using AI, NLP, and contextual learning, Aushadhi generates high-confidence coding suggestions forMedDRA and WHO Drug while supporting study- and sponsor-specific coding rules. This helps reducevariability, minimize rework, and improve coding quality across studies and programs.
From intake through approval, Aushadhi supports role-based review, decision traceability, andcontrolled workflow execution. With interoperability across EDC, CTMS, and safety systems, it helpsteams accelerate review cycles, support audit readiness, and improve downstream efficiency.
Core Features
Built to modernize medical coding from verbatim intake to approved, traceable outputs.
AI-Assisted Coding
Generate high-confidence coding suggestions for MedDRA and WHO Drug using AI, NLP, and contextual learning.
Dual Dictionary Support
Work seamlessly across MedDRA and WHO Drug with controlled dictionary governance and version management.
Rule-Based Validation
Apply study- and sponsor-specific coding rules automatically to improve consistency and reduce rework.
Reviewer Workflows
Support role-based review, bulk actions, overrides, and approval workflows with clear ownership and control.
Clinical & Safety Interoperability
Connect with EDC, CTMS, and safety systems to streamline intake, review, and downstream coding workflows.
Traceability & Compliance
Maintain audit trails, version control, and decision traceability to support compliant, inspection-ready coding operations.
Ready to Transform Your Medical Coding Operations?
See how Aushadhi AI automates WHODrug & MedDRA coding, from verbatim intake to approval, in a personalized demo.