Solution Accelerator

Scaling Personalized Cancer Coaching with AWS Generative AI

Business Challenge:

Complement 1 delivers personalized lifestyle modification programs for cancer
patients. Manual clinical decision trees limited real-time personalization, requiring 24–48 hour review
cycles and constraining patient scale beyond 200. Adherence rates were 65%, with $180K/month in
medical oversight costs, limiting operational scalability.

Solution Overview

Objective: Enable adaptive, compliant, and real-time personalized health recommendations at scale. 

AWS Services Leveraged: – Amazon Bedrock (Claude 3.5 Sonnet): Real-time generative inference for adaptive patient coaching. – Amazon SageMaker: Continuous learning pipelines and reinforcement learning for patient outcome optimization. – Amazon Textract: OCR extraction from medical PDFs and reports. – AWS Lambda & Step Functions: Orchestration for sub-2-minute inference. – DynamoDB: High-speed storage for patient recommendations. – Security & Compliance: Multi-AZ VPC architecture, KMS encryption, CloudTrail logging, HIPAA-compliant operations. 

Architecture Highlights: – Multi-AZ VPC with public subnets for API Gateway, private subnets for Lambda & SageMaker, and isolated Bedrock inference. – Real-time data flow: S3 → Textract → Bedrock RAG → SageMaker prediction → DynamoDB. – Lambda + Step Functions orchestrate AI workloads with <2-minute latency. – Responsible AI implemented using Bedrock Guardrails and SageMaker Clarify for bias monitoring. 

Agentic AI Principles: – Bedrock Agents SDK for autonomous decision loops. – Continuous feedback from patient interactions for model refinement. 

Key Metrics & Outcomes

  • Personalization Latency: Reduced from 24–48 hours to 1.8 minutes (96% improvement). • 
  • Clinical Oversight: Reduced 75%, decreasing from 8 hours per 200 patients to 2 hours.
  • Patient Adherence: Increased from 65% to over 90%. •  
  • Operational Scale: Enabled 5x patient volume without additional manual intervention.
  • Financial Efficiency: Reduced labor and operational costs by 30%, achieving TCO reduction of  65%. 
  • AWS ARR: Estimated $720,000, with Bedrock 44% and SageMaker 29% of usage. 1

Continuous Improvement & Lessons Learned

  • Initial hallucination rate of 12% reduced by 98% using RAG retraining and prompt optimization. 
  • Bi-weekly retraining and automated drift detection maintain 99% factual accuracy. 
  • Governance framework enhanced to integrate feedback loops and bias validation early in configuration. 
  • Runbooks created for operational management, including scaling, errors, and throttling  scenarios. 

Implementation Timeline

Kickoff: Q1 2025  

Infrastructure Deployment: Multi-AZ VPC, Lambda, Bedrock, SageMaker, Textract (Q2 2025)  

Full Production Launch: Q3 2025 

Impact

  • Sub-2-minute personalized recommendations for 28,000+ daily patient interactions. 
  • CloudWatch and Bedrock telemetry ensured latency SLA compliance. 
  • Fully HIPAA-compliant production environment supporting oncology coaching workflows. 
  • Significant operational cost savings and measurable patient outcome improvements. 

Conclusion

Complement 1 successfully transformed from manual, rule-based processes to an AI driven, generative coaching platform using AWS Bedrock, SageMaker, and Textract. The deployment achieved dramatic reductions in latency and oversight, increased adherence rates, enabled scaling, and demonstrated measurable financial efficiency, while maintaining full HIPAA compliance.