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Letting Data Speak, AI Act!
Case Study
Transforming Healthcare Data Extraction with AI-Powered Document Processing
About the Client
A healthcare organization struggled with inefficient clinical documentation workflows and manual data extraction from patient records. Staff spent excessive time on paperwork, struggled to locate critical patient information quickly, and faced challenges with regulatory compliance reporting. The organization recognized the need to streamline their document management processes to reduce administrative burden on clinical staff and improve access to vital patient data for better care delivery.
Challenge
Healthcare providers face significant challenges in efficiently and accurately extracting structured data from complex medical documents. Traditional optical character recognition (OCR) methods struggled with:
Interpreting complex medical terminologies
Handling varied document layouts
Maintaining context and accuracy in medical information extraction
Integrating extracted data into existing healthcare information systems
Key Results
100% accuracy in medical data extraction
Reduced manual document processing time
Seamless integration with existing healthcare databases
Enhanced data reliability and consistency
Solution
JashDS implemented a comprehensive AI-powered medical document processing system leveraging cutting-edge cloud technologies and advanced language models:
Technical Architecture
Serverless infrastructure using AWS cloud services
Utilized AWS Lambda for event-driven processing
Integrated AWS Bedrock with Claude 3.5 Sonnet AI model
Implemented secure VPC configuration for data processing
Deployed Aurora PostgreSQL for structured data storage
Key Technical Innovations
Advanced PDF preprocessing and image enhancement
AI-powered contextual data extraction
Automated confidence scoring mechanism
Flexible data integration strategy
Comprehensive error handling and recovery
Technical Approach
The solution addressed critical challenges through a multi-stage approach:
Document Preprocessing
Converted PDFs to high-resolution images (150 DPI)
Applied advanced image enhancement techniques
Removed interactive document elements
Prepared images for AI processing
AI-Powered Extraction
Utilized Claude 3.5 Sonnet model through AWS Bedrock
Extracted complex medical information with high accuracy
Captured critical data points:
Social Security Numbers
Medical Record Numbers
Insurance plan details
Medical status indicators
Behavioral health keywords
Data Validation
Implemented automated confidence assessment
Conducted manual validation of extracted data
Achieved 100% accuracy across all processed documents
Database Integration
Designed a two-table relational database structure
Implemented selective update mechanism
Preserved existing client information
Ensured data integrity and consistency
Technologies Used
AWS Serverless Components
AWS Lambda
AWS S3
AWS Bedrock
Claude 3.5 Sonnet AI Model
Aurora PostgreSQL
Python
Pydantic
Advanced Image Processing Libraries
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