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Automated Finding Extraction and Compliance Report Generation
A clinical research organization was spending 40+ hours per week manually reviewing study documents, extracting findings (protocol deviations and errors), and generating compliance reports for hospital partners. The manual process was error-prone and delayed critical communications to study sites.
We built an AI-powered document analysis system that automatically extracts findings from clinical study reports, categorizes them by severity and type, generates structured compliance letters, and routes them through an approval workflow. The system includes PII detection, HIPAA-compliant data handling, and full audit trails.
The project was completed in 10 weeks: Week 1-2: Document analysis and finding taxonomy definition Week 3-5: AI model training and extraction pipeline development Week 6-7: Compliance letter generation and approval workflows Week 8-9: HIPAA compliance review and security hardening Week 10: Testing, documentation, and team training Technologies used: Python, spaCy for NLP, AWS Lambda for processing, S3 for secure storage, and custom React dashboard for review and approval.
Within 60 days of deployment, the team reduced manual compliance reporting time by 78%. Finding extraction accuracy reached 94%, with all edge cases routed to human review. The organization now processes 3x more studies with the same team size. Compliance letters are delivered to sites 5 days faster on average.
Key Outcomes: