Client context
A global leader in early cancer detection with wide clinical studies across regions. The team collects structured health and lifestyle data, connects laboratory results, and runs follow ups that depend on medical outcomes.
Life sciences capability
Automation that speeds early detection research
We help research teams turn complex study design into working systems. The example on this page shows how SequoiaAT automated electronic case report forms, rule checks, alerts, and laboratory data sync for a large early cancer detection program.
Explore related services in AI and ML, automation, and quality engineering.
What was needed
- Reliable collection of structured data across locations
- Secure entry of laboratory results linked to participant records
- Personalized follow ups and alerts based on outcomes
- Strong validation and rule checks to protect study quality
SequoiaAT contribution
Automated form generation
We built a Python utility that converts Google Sheet questionnaires to JSON. The backend ingests these JSON files and writes database records. A React frontend renders dynamic forms from the database.
Study rule engine
We added database triggers and validation logic. The team can configure checks for gender specific questions, leap year date validation, and time order. Medical results can trigger new forms or alerts.
Notification system
Email alerts keep participants on schedule. For example, follow ups at ninety days after treatment are created and tracked.
Lab data sync and workflows
Laboratory results are tied to participant records and can branch the workflow for treatment steps or new surveys.
Results
- Manual work reduced by more than eighty five percent through the parser and deployment tool
- A thirty to forty minute update cycle now takes under five minutes
- Faster roll out of questionnaires to production environments
- Improved data quality through consistent rule checks
Technology
- Python utilities
- React interfaces
- Relational database with triggers and validation
- Email service for alerts
Why SequoiaAT
- Product thinking in a services model
- Teams across time zones for speed
- Focus on maintainable systems, not one time builds
- Strong delivery in regulated domains
See our work in embedded systems, internet of things, and mobile apps.
Engagement options
- Discovery and planning
- Build and validate
- Operate and improve
We align to your study cadence and quality process. Our testing practice supports verification at each release. See testing services.
Ready to discuss your study platform
Tell us about your program. We will reply with a short plan and a suggested path to a working pilot.