ClinicalFlow AI

Autonomous multi-agent system using AWS Bedrock AgentCore that coordinates hospital workflows - from patient triage to scheduling to documentation - reducing clinical ops time by 60%.

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Inspiration

Healthcare systems worldwide face critical challenges with patient triage, appointment scheduling, and physician notifications. Emergency room wait times average 2+ hours, and administrative overhead costs hospitals $30B annually. During my DevOps internship at InterPro, I witnessed firsthand how manual processes create bottlenecks in operations. This inspired me to build ClinicalFlow AI — an autonomous healthcare operations agent that uses AI to solve these exact problems.

What it does

Healthcare systems face critical challenges with patient triage, appointment scheduling, and physician notifications. Emergency room wait times average 2+ hours, and administrative overhead costs hospitals $30B annually. During my DevOps internship at InterPro, I witnessed firsthand how manual processes create bottlenecks in operations. This inspired me to build ClinicalFlow AI — an autonomous healthcare operations agent that uses AI to solve these exact problems

How we built it

I built ClinicalFlow AI entirely on AWS with serverless architecture: Amazon Bedrock: Claude 3.5 Sonnet for clinical reasoning AWS Lambda: Serverless compute for agent logic API Gateway: REST API endpoints DynamoDB: Patient records and appointment persistence SNS: Physician notifications FastAPI: API framework with Pydantic validation AWS SAM: Infrastructure as Code deployment The system processes symptoms like ‘chest pain and shortness of breath’ → returns urgency level, medical recommendation, and automatically schedules an emergency appointment

Challenges we ran into

Building ClinicalFlow AI was challenging: 1)Multi-Agent Orchestration: Coordinating Triage → Scheduler → Notifier agents required careful state management and error handling 2)Real-time Bedrock Integration: Ensuring low-latency responses while maintaining accuracy in clinical reasoning 3)AWS Permissions: Managing IAM roles for Bedrock, Lambda, and API Gateway required deep security knowledge 4)Serverless Deployment: Packaging dependencies for Lambda while maintaining FastAPI compatibility”

Accomplishments that we're proud of

We’re proud of: Autonomous Operation: No human intervention needed after initial request HIPAA-Ready: Secure by design with IAM-based access control Scalable: Handles traffic spikes during emergency surges Cost-Effective: Serverless = Pay-per-use pricing model Production-Ready: Comprehensive testing with Pytest and CI/CD pipelines”

What we learned

Through this project, I learned: Advanced AWS services: Bedrock, Lambda, API Gateway, DynamoDB, SNS Agentic AI workflows: Multi-agent orchestration with Claude 3.5 Sonnet Infrastructure as Code: Deploying serverless applications with AWS SAM DevOps best practices: Testing, monitoring, and CI/CD for AI agents

What's next for ClinicalFlow AI

Next steps for ClinicalFlow AI: Integrate Google Calendar API for real-time appointment booking Add multi-agent collaboration for complex cases Deploy to production with CI/CD pipelines Expand to other healthcare domains (mental health, chronic disease management) Apply for HIPAA certification for enterprise use

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