AI-driven pharmacovigilance intake case processing platform revolutionizing how pharmaceutical companies handle safety reports, product quality issues, and adverse event communications.
Intelligent email classification and data extraction system powered by fine-tuned Claude Sonnet LLM, automatically processing incoming correspondence and extracting critical information including drug details, adverse event descriptions, and patient conditions.
End-to-end AWS-hosted solution featuring automated follow-up email generation for missing information, human-in-the-loop validation, and seamless integration with larger case processing and reporting systems, delivering 40% reduction in case processing times and substantial cost savings.
Pharmaceutical companies faced overwhelming volumes of safety-related emails requiring manual review, classification, and data extraction, creating bottlenecks in critical pharmacovigilance workflows and regulatory compliance timelines.
Manual information extraction from unstructured emails led to inconsistent data quality, human errors, and missed critical details about adverse events, drug interactions, and patient conditions requiring immediate attention.
Follow-up communications to collect missing case information involved repetitive manual drafting and tracking, consuming significant resources while delaying case completion and regulatory reporting obligations.
AI-Powered Email Classification - Intelligent categorization of incoming emails into safety reports, product quality issues, and other categories
Fine-Tuned Claude Sonnet LLM - Custom-trained language model optimized for pharmaceutical terminology and pharmacovigilance data extraction
Automated Information Extraction - AI-driven extraction of drug names, adverse events, patient conditions, dosages, and critical case details
Follow-up Email Generation - Automated drafting of personalized follow-up requests identifying missing information required for case completion
Human-in-the-Loop Validation - Review and approval workflow ensuring AI accuracy while maintaining regulatory compliance and quality standards
AWS End-to-End Infrastructure - Secure, scalable cloud deployment ensuring data security, HIPAA compliance, and enterprise reliability
Case Processing Integration - Seamless connectivity with downstream case management and regulatory reporting systems
40% Processing Time Reduction - Significant acceleration of intake workflows through intelligent automation and information extraction
Cost Savings Optimization - Substantial reduction in manual labor costs and resource requirements for intake processing
Error Reduction System - Minimized human errors through consistent AI-powered extraction and validation workflows
Our development approach focused on building a secure, compliant AI platform that automates pharmacovigilance intake while maintaining the rigor and accuracy demanded by pharmaceutical regulations.
We fine-tuned Claude Sonnet on pharmaceutical and medical terminology, then integrated human oversight to ensure AI decisions meet regulatory standards and quality requirements.
Fine-tuned Claude Sonnet language model on pharmaceutical safety data, adverse event reports, and medical terminology for accurate email classification and information extraction.
Developed multi-class classification system distinguishing safety reports, product quality issues, and general correspondence with high precision for appropriate routing and processing.
Built intelligent information extraction pipeline identifying and structuring drug names, adverse events, patient demographics, dosages, and temporal relationships from unstructured email text.
Implemented automated follow-up email generation analyzing extracted data, identifying information gaps, and drafting personalized requests for missing case details.
Deployed complete solution on AWS infrastructure with encryption, access controls, and compliance features ensuring pharmaceutical data security and regulatory requirements.
Integrated human-in-the-loop validation workflows and connected intake platform to downstream case processing systems enabling end-to-end pharmacovigilance automation.
Successfully deployed iRxSafe platform achieving 40% reduction in pharmacovigilance case processing times through intelligent email classification and automated information extraction.
The fine-tuned Claude Sonnet model delivers high-accuracy extraction of drug details and adverse events while automated follow-up generation significantly reduces manual communication workload and accelerates case completion.
Human-in-the-loop validation ensures regulatory compliance and data quality while substantial cost savings, error reduction, and scalable AWS infrastructure position iRxSafe as a transformative intake solution for pharmaceutical safety operations.
