From Manual Document Entry to Scalable Healthcare Automation
Workflow and Compliance Discovery
We mapped how medical documents moved between facilities, administrative teams, clinical staff, billing departments, and insurance workflows. This identified extraction requirements, processing bottlenecks, security risks, validation rules, and audit trail needs.
Medical Document Preprocessing
A preprocessing layer was designed to improve the readability of scanned, handwritten, and inconsistently formatted documents. Images are prepared for OCR before extraction, improving recognition across medical forms, clinical notes, prescriptions, and diagnostic files.
AI OCR and API Development
We integrated Google Cloud Vision API with a RESTful API architecture for real-time and batch document processing. The system extracts medical information, detects handwriting and checkboxes, validates the output, and converts it into structured data for downstream use.
Security, Storage and Quality Assurance
Amazon S3 encryption and versioning were implemented to protect documents and preserve file history. Testing covered OCR accuracy, handwriting recognition, checkbox detection, data validation, concurrent processing, bulk uploads, storage reliability, and recovery scenarios.
Deployment and Performance Optimisation
The cloud infrastructure was optimised for high-volume healthcare document workflows. Performance monitoring, scalable processing, and ongoing improvements enabled the platform to maintain 99.9% uptime while managing changing document volumes across multiple facilities.
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