Claims Adjudication & Fraud Detection

Inspiration Manual processing bottlenecks in healthcare High error rates in traditional systems Fraud detection challenges Scalability issues in growing healthcare systems What I Learned Technical Skills: AWS serverless architecture, AI/ML integration, modern frontend development, backend architecture Domain Knowledge: Healthcare industry understanding, medical coding systems, fraud patterns, compliance requirements AI/ML Skills: Natural language processing, pattern recognition, confidence scoring, explainable AI How I Built It Phase 1: Architecture design and planning (2 weeks) Phase 2: Infrastructure development with AWS CDK (1 week) Phase 3: AI agent development (3 weeks) Phase 4: Frontend development with React/TypeScript (2 weeks) Phase 5: Integration and testing (1 week) Challenges Faced Technical: AWS Bedrock access, document processing complexity, Lambda cold starts AI/ML: Prompt engineering for healthcare, knowledge base management Integration: API Gateway timeouts, CORS and authentication Domain: Healthcare compliance understanding, fraud detection accuracy Performance: Concurrent processing, cost optimization Key Achievements Full-stack serverless architecture AI integration for healthcare decision-making Scalable, cost-effective design Modern, responsive user interface The document provides a detailed narrative of your development journey, technical challenges overcome, and the valuable learning experience gained from building this sophisticated AI-powered healthcare claims processing system.

Built With

• 18
• ai/ml
• amazon
• analysis
• anthropic
• api
• apis
• as
• bases
• bedrock
• cdk
• claude
• cli
• cloudwatch
• configuration
• cors
• css
• development
• dynamodb
• embeddings
• gateway
• git
• iam
• knowledge
• ocr
• python
• rag
• react
• rest
• roles
• router
• s3
• semantic
• serverless
• tailwind
• textract
• toastify
• tools
• typescript
• vector
• vite