RTSM Solution: Data Ingestion Improvement

Our client is a specialized CRO focusing on Randomization and Trial Supply Management (RTSM). The company offers technology and services for complex clinical trials, and supports clinical study sponsors and other CROs in managing drug supplies and patient randomization efficiently.

Within the client’s RTSM platform, patient data is ingested through multiple API endpoints that evolve over time and serve different operational and analytical needs.

Still, there were several impediments that reduced the platform’s ability to scale safely while meeting high data quality and compliance expectations. The key challenges included:

• lack of a standardised data architecture for handling patient data across ingestion, transformation, and consumption layers,
• difficulty ensuring data lineage, traceability, and auditability, which are critical for RTSM and clinical trial data,
• tight coupling between endpoint ingestion logic and downstream business transformations,
• limited scalability when introducing new endpoints or handling schema changes,
• increased risk for downstream consumers (analytics, reporting, regulatory use cases) due to inconsistent data contracts.

Software Country’s team designed and implemented the Medallion Architecture (Bronze / Silver / Gold) using an endpoint-centric ingestion model for patient data within the RTSM. 

Bronze layer

• Raw ingestion of patient data directly from RTSM API endpoints.
• Preservation of original payloads for full reproducibility and audit purposes.
• Clear separation of ingestion from business logic.

Silver layer 

• Data cleansing, normalization, and schema alignment across endpoints.
• Handling of data changes and versioning for patient entities.
• Creation of consistent, structured patient datasets.

Gold layer 

• Business-ready patient data models optimized for analytics and reporting.
• Stable data contracts for downstream systems.
• Clear alignment with RTSM operational and analytical use cases.

This approach ensured that endpoint changes could be absorbed in lower layers without impacting business consumers, while providing a scalable foundation for future growth. 

The implementation delivered the following outcomes: 

• end-to-end traceability of patient data from RTSM endpoints to analytical consumption
• improved data quality, consistency, and reliability across the platform
• reduced impact of API and schema changes on downstream consumers
• faster onboarding of new endpoints within RTSM
• stronger readiness for compliance, audit, and regulatory requirements
• a reusable architectural pattern applicable across other client's data domains.

The solution strengthened the RTSM platform’s data foundation and enabled scalable, compliant, and analytics-ready patient data management.