Modernized Data Platform for Efficient Clinical Trial Data Analysis and Monitoring

The client experiences limitations in their existing monitoring platform, including challenges with scalability, flexibility, and processing large volumes of clinical and operational data. They need a re-architecture to detect data anomalies more rapidly, incorporate new integrations, improve service quality, and support business growth.

A global healthcare organization specializing in clinical trial management and data analysis seeking to enhance their monitoring and analytical capabilities.

• Redesign and implement a highly scalable and flexible cloud-based monitoring platform.
• Enhance data processing efficiency, reducing anomaly detection times from days to minutes.
• Automate data transformation workflows, including format conversion and dataset snapshot creation.
• Implement real-time alerting and action item generation for data discrepancies cited against configurable thresholds.
• Develop an interactive analytics dashboard that enables filtering, comments, and review assignment for clinical data insights.
• Automate creation of data cubes for multidimensional analysis based on key attributes such as patient ID and location.
• Refactor existing codebase to improve performance and maintainability, supporting future feature additions.
• Establish comprehensive automated testing frameworks for system validation, performance testing, and regression testing.

• Microservices architecture for modular development and scalability
• Data ingestion pipeline converting CSV and SAS data formats into optimized storage formats like Parquet
• Data storage solutions utilizing cloud-native object storage (e.g., Azure Blob Storage) instead of traditional relational databases
• Automated data snapshot management for multiple formats applicable across analytical tools
• Interactive review dashboards with filters, commenting, and reviewer assignment
• Automated creation and management of data cubes across multiple dimensions
• Configurable anomaly detection thresholds with automatic notification integration to CRM/ERP systems
• System automation for comprehensive testing, including performance, regression, and system migration validation
• Separate administrative interface for user and study management with synchronization capabilities

• CRM and ERP systems for automatic notification delivery upon anomaly detection
• Third-party data sources for clinical data ingestion
• User authentication and authorization systems
• Existing laboratory or clinical equipment data feeds (if applicable)

• Platform must process large datasets efficiently, achieving data processing speed improvements of up to 14.5 times compared to previous systems
• Support scalability to handle increasing data volumes without performance degradation
• Automate testing and validation processes, reducing test cycle times from days to hours
• Achieve a 90% success rate in automated test pass for continuous integration
• Ensure secure data handling compliant with healthcare data regulations
• Provide real-time alerting for data anomalies within minutes

The modernized data platform is expected to significantly improve data processing speeds, reduce anomaly detection times from days to minutes, and automate workflows resulting in faster insights, higher service quality, and increased customer satisfaction. The project aims to support business growth, expand the client’s customer base, and potentially increase revenue growth by approximately 115%, while boosting platform performance by up to 14.5 times.