Appendix q — Data Storage Costs for Decentralized Clinical Trials

Data Storage Costs for Decentralized Clinical Trials

Storage Requirements Per Patient

Basic Data Components

  • Patient demographics and basic information: < 1MB
  • Trial-specific forms and data entries: 1-5MB
  • Medical imaging (when applicable): 50-500MB
  • Continuous monitoring data: 1-10MB/month
  • Total typical storage: ~1GB per patient

Cost Analysis

Infrastructure Costs (Per Patient/Month)

Component Estimated Cost Notes
Raw Storage $0.02 Based on standard cloud storage rates
Compute/API $0.20 For data processing and access
Database $0.30 For structured data storage
Backup/Redundancy $0.20 For data safety and compliance
Total $0.72 Per patient/month

Cost Comparisons

  • Electronic Health Records (EHRs): 1-2GB per patient
  • Google Workspace: $12/user/month (includes many additional services)
  • Medical imaging platforms: 100s of GB per patient

Why Costs Are Lower Than Traditional Platforms

  1. Focused use case with optimized data types
  2. No need for collaboration tools or email storage
  3. Efficient data structures for clinical trial data
  4. Economies of scale with increased adoption

Additional Considerations

Fixed Costs

  • Security and compliance infrastructure
  • Data validation systems
  • API management
  • Development and maintenance

Cost Optimization Strategies

  1. Implement efficient data compression
  2. Use tiered storage for older/less accessed data
  3. Optimize image and continuous monitoring data storage
  4. Leverage serverless computing for cost-effective scaling

Scaling Benefits

  • Fixed costs decrease per patient as platform grows
  • Bulk storage rates improve with volume
  • Automated systems become more cost-effective at scale

Conclusion

The actual per-patient storage cost ($0.72/month) is significantly lower than traditional systems due to focused use case, modern cloud infrastructure, and optimization for clinical trial data. This enables a cost-effective decentralized platform that can scale efficiently while maintaining high data quality and compliance standards.