MedClaimX

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  MedClaimX

Inspiration

Healthcare claim processing is one of the most time-consuming and error-prone tasks in the medical industry. A single mistake in ICD-10 or CPT coding can lead to claim rejection, delays, and financial loss. We were inspired to solve this inefficiency by building an intelligent system that automates the entire workflow—from medical notes to approved claims—reducing manual effort and improving accuracy.

What it does

MedClaimX is an AI-powered autonomous healthcare agent that processes medical notes (PDF or text) and performs:

• ICD-10-CM compliant diagnosis coding
  
• CPT/HCPCS procedure selection
  
• Prior authorization generation
  
• Insurer rule interpretation
  
• Claim submission and tracking
  
• Appeal generation for rejected claims
  

It reduces a 45-minute manual workflow to under 40 seconds while maintaining high accuracy and compliance.

How we built it

We designed MedClaimX as an end-to-end intelligent pipeline:

• OCR Engine to extract structured text from medical PDFs
  
• Gemini 2.5 Pro for structured clinical data extraction
  
• RAG (Retrieval-Augmented Generation) using Qdrant vector database
  
• Knowledge base enriched with ICD-10 guidelines and historical claim data
  
• Rule-based + AI hybrid system for CPT/HCPCS mapping and insurer logic
  
• Backend APIs for workflow orchestration and automation
  

The system integrates multiple AI components to deliver a seamless, autonomous workflow.

Challenges we ran into

• Handling unstructured and noisy medical documents
  
• Ensuring ICD and CPT coding accuracy
  
• Simulating real-world insurer rules and authorization logic
  
• Maintaining compliance and explainability in AI decisions
  
• Integrating multiple components into a smooth pipeline
  

Accomplishments that we're proud of

• Built a fully automated end-to-end claim processing system
  
• Reduced processing time from 45 minutes to under 40 seconds
  
• Achieved high accuracy in coding using RAG-based knowledge retrieval
  
• Designed a scalable architecture that can integrate into real healthcare systems
  
• Created a solution with real-world impact in healthcare operations
  

What we learned

• Importance of combining LLMs with structured knowledge (RAG)
  
• Real-world healthcare systems require both AI and rule-based logic
  
• Data quality plays a crucial role in AI performance
  
• Building explainable AI is essential for trust in healthcare
  
• End-to-end system design is more complex than individual models
  

What's next for MedClaimX

• Integrate real-time EHR/FHIR data for live hospital use
  
• Improve coding accuracy with larger medical datasets
  
• Add human-in-the-loop validation for critical decisions
  
• Deploy as a SaaS platform for hospitals and clinics
  
• Enhance prediction models for claim approval probability
  

Built With

• a2a
• docker
• fastapi
• fhir
• google-cloud-(vertex-ai)
• google-gemini-2.5-pro
• mcp
• mongodb
• ocr-(tesseract)
• python
• qdrant
• rag
• react.js
• rest-apis