🏥 Medical Document Automation with Google Cloud Document AI & BigQuery

Streamline medical insurance claim processing with advanced AI techniques, reducing manual efforts and improving efficiency with real-time analytics.

📚 Project Overview

This project leverages Google Cloud Document AI (Vertex AI and Generative AI) to automate the processing of medical insurance claim documents. By integrating OCR for digitization and NLP for content categorization, the system efficiently extracts structured data with high accuracy and integrates seamlessly with BigQuery for advanced data analysis.

🚀 Setup and Implementation

1. Create a Processor

• Navigate to the Google Cloud Document AI console.
• Create a custom processor for structured data extraction.
• Select the appropriate region and enable Google-managed storage and encryption.

2. Processor Configuration

• Field Definition: Identify fields such as claim_number, company_name, service_date, amount_billed, amount_paid, and medical_procedure.
• Field Attributes: Define data types (e.g., Number, Date, Currency) and set occurrence parameters (Required, Optional).

3. Document Labeling

• Upload Documents: Import PDFs into the labeling console from Google Cloud Storage.
• Annotation: Use built-in annotation tools to label fields. Leverage the foundation model for initial suggestions and manually correct any inaccuracies.

4. Model Training

• Build Processor Version: Start with a pretrained foundation model and fine-tune it for medical document extraction.
• Auto-Labeling: Utilize Generative AI to automate the labeling process, ensuring efficient and accurate training data.

5. Training Custom Model

• Ensure a balanced dataset with at least 10 examples per field.
• Setup Training: Configure and train a custom processor for optimal performance.

6. Deployment

• Deploy the trained model for real-time processing.
• Manage processor versions for easy updates and scalability.

7. Evaluation and Testing

• Model Evaluation: The model achieved a performance accuracy (96.1%), precision (95.5%), and recall (96.8%).
• Document Testing: Evaluate with new claim documents and make adjustments as necessary.

8. Integration with BigQuery

• Connect the Document AI output to BigQuery for comprehensive data analysis.
• Use SQL queries to extract insights and support data-driven decision-making.

🌟 Key Insights and Summary

• Automated Document Processing: Reduced manual data entry by 80%, achieving 96.1% accuracy in entity extraction.
• NLP & OCR Integration: Enhanced data digitization and content categorization for efficient handling.
• Real-time Analytics: Reduced processing time by 60%, providing instant insights.
• Error Reduction: Decreased processing errors by 75%, improving data reliability.
• Scalable Solution: Supports easy scaling for increased document volumes.
• Generative AI Utilization: Optimized training with automated labeling processes.

💻 Technology Stack

• Google Cloud: Document AI, Vertex AI, Generative AI
• Data Processing: OCR, NLP
• Database: BigQuery
• Data Analytics: SQL, AI/ML

📈 Impact

This project significantly improved the efficiency of processing medical insurance claims. It automated data entry, reduced errors, and enabled real-time analytics, making it a highly effective tool for healthcare data management.

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Feel free to contribute, open issues, or suggest improvements to this project!