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LSSUS05-BP01 Design sustainable clinical trial architecture - Life Sciences Lens
Implementation guidanceResources

LSSUS05-BP01 Design sustainable clinical trial architecture

Implement decentralized clinical trial architectures that minimize data movement and reduce environmental impact through regional data processing strategies and edge computing solutions. Design clinical trial systems that process data closer to trial populations, reducing network requirements while maintaining regulatory adherence and data integrity. Use standardized data management services and smart archiving strategies to optimize resource utilization throughout the clinical trial lifecycle.

Desired outcome: Achieve significant reduction in data transfer requirements and environmental impact of clinical trials while improving data accessibility, patient experience, and operational efficiency through decentralized architectures and edge computing solutions.

Common anti-patterns:

  • You centralize your clinical trial data processing without considering regional or edge processing alternatives.

  • You don't implement data filtering and aggregation at collection points, transferring raw data.

  • You use proprietary data formats instead of standardized formats like FHIR for clinical data.

  • You store your clinical trial data in the same storage tier regardless of access patterns.

  • You don't implement automated data archiving strategies aligned with regulatory retention requirements.

  • You don't consider patient travel reduction opportunities through remote monitoring and virtual visits.

Benefits of establishing this best practice:

  • Reduce data transfer costs and energy consumption through edge processing and regional architectures.

  • Improve patient experience and reduce travel-related emissions through decentralized trial designs.

  • Lower operational costs through optimized data management and automated archiving strategies.

  • Enhance data accessibility and analysis capabilities through standardized data formats.

  • Support regulatory adherence through automated retention and archiving policies.

Level of risk exposed if this best practice is not established: Medium

Implementation guidance

Clinical trials generate vast amounts of data from diverse sources including electronic health records, patient-reported outcomes, wearable devices, and medical imaging. Traditional centralized approaches require significant data movement and processing resources, contributing to environmental impact while potentially creating bottlenecks in data analysis. Decentralized clinical trial architectures address these challenges by processing data closer to its source and implementing intelligent data management strategies.

The shift toward decentralized clinical trials is driven by both sustainability goals and improved patient outcomes. By implementing edge computing solutions for patient monitoring and regional data processing strategies, organizations can significantly reduce data transfer requirements while maintaining the high standards of data quality and regulatory adherence required in clinical research. This approach also enables more inclusive trial designs by reducing patient travel requirements and supporting remote participation.

Implementation steps

  1. Implement decentralized data collection and processing architecture:

    • Deploy AWS IoT Greengrass at clinical sites for local data filtering and aggregation.

    • Implement AWS Direct Connect for secure, dedicated connectivity between clinical sites.

    • Configure AWS PrivateLink for secure communication between decentralized components.

  2. Establish edge computing solutions for patient monitoring:

    • Deploy AWS IoT Core for device connectivity and data ingestion from wearable devices.

    • Use AWS IoT Greengrass for local processing of vital signs and patient-reported outcomes.

    • Implement Amazon Kinesis Data Streams for real-time data processing at the edge.

    • Configure AWS Lambda@Edge for lightweight data transformation and filtering.

  3. Implement standardized clinical data management:

    • Use AWS HealthLake for FHIR-based clinical data management and standardization.

    • Implement Amazon S3 with intelligent tiering for clinical trial data storage.

    • Use AWS Glue for data integration and transformation across different clinical data sources.

    • Configure Amazon Redshift for clinical trial data analytics and reporting.

  4. Deploy smart data archiving and lifecycle management:

    • Implement Amazon S3 lifecycle policies for automated data archiving based on regulatory requirements.

    • Use Amazon Glacier for long-term archival of completed trial data.

    • Configure AWS Config for monitoring and automated policy enforcement.

    • Implement AWS CloudTrail for comprehensive audit trails of data access and modifications.

  5. Establish monitoring and optimization processes:

    • Use Amazon CloudWatch to monitor data transfer volumes and processing efficiency.

    • Establish regular reviews of decentralized architecture performance and optimization opportunities.

Resources

Related best practices:

Related documents: