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Modernizing Clinical Trial Monitoring Platform for Scalability and Automated Analytics

n-ix.com

Healthcare

Information technology

Current Platform Limitations

Existing monitoring platform suffers from scalability issues, manual data processing workflows, and outdated architecture that hinder efficient clinical trial data analysis. Legacy systems create bottlenecks in anomaly detection, data storage costs, and integration capabilities.

About the Client

Software solutions provider for clinical trial data quality and integrity with 10+ years experience

Modernization Goals

Implement cloud-native microservices architecture
Enhance data pipeline efficiency for large-scale clinical datasets
Automate anomaly detection and reporting workflows
Improve platform performance and scalability
Develop interactive analytics dashboards for clinical insights

Core System Requirements

Microservices-based Data Storage service with Parquet format optimization
Automated data pipeline for CSV/Python/SAS to Parquet conversion
Interactive dashboards with filtering and collaborative review capabilities
Threshold-based anomaly detection with automated CRM/ERP notifications
Automated data cube generation for multi-dimensional analysis

Technology Stack

MS Azure

Angular

Flask

Pyramid

FastAPI

Celery

MongoDB

SQLAlchemy

Kubernetes

System Integrations

CRM systems
ERP systems
Third-party clinical data sources
Automated testing frameworks

Performance Requirements

90%+ test automation pass rate
Scalable storage architecture for petabyte-scale datasets
Sub-3 minute data analysis setup times
Real-time anomaly detection performance
High-availability cloud architecture

Business Impact Projections

Expected 100%+ revenue growth through enhanced service offerings, 14x performance improvements in data processing, and 90% reduction in manual quality assurance efforts. Automated workflows should reduce anomaly detection times from days to minutes while supporting Fortune 500 client acquisition.