How GeBBS Healthcare Solutions Processes 50,000 Charts Daily Using GenAI

What is AI Medical Coding?

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AI medical coding uses machine learning and large language models to read clinical documentation and automatically assign standardized billing codes such as ICD-10 and CPT.

It replaces manual interpretation of unstructured medical notes with contextual, automated code generation, improving speed, accuracy, and scalability in Revenue Cycle Management (RCM).

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How Does GenAI Improve Medical Coding in RCM?

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GenAI improves medical coding by understanding clinical context instead of relying on rules or keywords.

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• Interprets unstructured physician notes
  
  
• Extracts diagnoses, procedures, and conditions
  
  
• Maps clinical meaning to ICD-10 and CPT codes
  
  
• Reduces manual intervention
  
  
• Improves turnaround time for billing cycles
  
  

‍About the Customer

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GeBBS Healthcare Solutions is a leading provider of Revenue Cycle Management (RCM) and Health Information Management services.

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• Serves hospitals, health systems, and physician groups
  
  
• Processes millions of charts annually across 100+ healthcare organizations
  
  
• Maintains 95%+ coding accuracy
  
  
• Recognized in Everest Group PEAK Matrix and Inc. 5000
  
  

Core function: converting clinical documentation into accurate billing codes to ensure timely reimbursement.

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The Problem: Why Medical Coding Does Not Scale

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Medical coding becomes inefficient at scale due to dependence on manual interpretation.

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Key constraints:

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• Large volumes of unstructured clinical notes
  
  
• Rule-based automation unable to capture clinical nuance
  
  
• Heavy reliance on certified coders
  
  
• Increasing turnaround time as volumes grow
  
  

Impact on RCM:

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• Delayed billing cycles
  
  
• Revenue leakage from missed codes
  
  
• Operational bottlenecks during volume spikes
  
  

The Solution: GenAI-Powered Medical Coding System

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GeBBS designed an AI-driven pipeline on Amazon Bedrock to automate medical coding at scale, enabled by Shellkode for implementation.

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1. Automated Chart Ingestion

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• Charts stored in Amazon S3
  
  
• Continuous ingestion pipeline
  
  
• No manual triggers or queue buildup
  
  

2. Clinical Understanding with GenAI

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• Multi-level LLM processing of physician notes
  
  
• Extracts:
  
  • Diagnoses
    
    
  • Procedures
    
    
  • Clinical context
    
    

3. Structured Codeable Output

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• Converts unstructured notes into billing-ready entities
  
  
• Aligns physician language with payer requirements
  
  

4. Semantic Code Mapping

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• Embedding-based similarity search
  
  
• Matches clinical meaning to ICD-10 and CPT
  
  
• Eliminates keyword dependency
  
  

5. Human-in-the-Loop Validation

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• Complex cases flagged automatically
  
  
• Routed to certified coders
  
  
• Ensures accuracy and compliance
  
  

6. Audit and Compliance Layer

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• Data stored in PostgreSQL
  
  
• Full traceability and audit readiness
  
  

Benefits of AI Medical Coding

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Faster Processing : 5–6x reduction in time per chart, accelerating billing cycles.

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Higher Throughput : Processes 50,000 charts daily without backlog.

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Reduced Manual Effort : 40-50% of charts coded without human intervention.

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Improved Accuracy : Context-aware coding reduces missed diagnoses and errors.

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Scalable Operations : Handles volume spikes without increasing headcount.

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Business Impact

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• 50,000  charts processed daily
  
  
• Up to 20,000  charts fully automated per day across multiple specialties
  
  
• Significant reduction in turnaround time
  
  
• Elimination of operational bottlenecks
  
  
• Improved revenue realization through better coding completeness
  
  

How AI Medical Coding Changes RCM Operations

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Before:

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• Manual coding limited throughput
  
  
• Backlogs during high volume
  
  
• Inconsistent coding quality
  
  

After:

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• AI processes routine charts automatically
  
  
• Human coders focus on complex cases
  
  
• Consistent, scalable, high-speed coding