The client is a premier medical diagnostics company delivering a wide range of laboratory testing services to healthcare providers. 

High variability in lab report layouts made traditional template-based parsing impossible.

• Handwritten Overlaps: Critical test data and notes were often handwritten, sometimes overlapping with printed text.
• Scalability Issues: Manual data entry was resource-intensive, slow, and prone to human error, hindering large-scale processing.
   

We implemented a robust Intelligent Document Processing (IDP) solution that treats every report as a unique data set. By combining Computer Vision with OpenAI’s language capabilities, the system understands the context of the medical data it extracts.

• Advanced OCR & HTR: Used image-based models to handle skewed scans and sophisticated algorithms to recognize handwritten text at varying angles.
• Pluggable Domain Layer: A flexible architecture allows for custom extraction logic tailored to specific healthcare domains or report types.
• Precision Logic: Built-in error rejection mechanisms identify and filter out low-confidence outputs to maintain medical-grade data integrity.
   

• Multi-source data extraction: Capable of extracting personal details, test information, and handwritten data from scanned PDF lab reports.
• Robust image processing: Utilizes image-based models to adjust for skewed images and minor orientation issues, ensuring accurate data extraction.
• Domain-specific customization: Offers a pluggable domain-specific layer allowing customization for different domains, enhancing accuracy and relevancy.
• Handwritten text recognition: Specialized algorithms to extract handwritten data from scanned documents, handling varying angles, formats, and overlaps with printed text.
• Predefined data coordinates support: Ability to define and extract data from specific areas within documents, ensuring higher accuracy in extracting targeted information.
• Adaptability and customization: Customizable to handle multiple forms and domains, ensuring flexibility in data extraction processes.
• Error rejection logic: Incorporates logic to reject inaccurate or less reliable outputs, improving overall data accuracy and integrity.

The IDP solution improved efficiency and resulted in a drastic reduction in manual effort required for analyzing and entering lab report data.

• Enhanced Accuracy: Domain-specific logic and automated validation significantly increased the reliability of patient records.
• Cost Savings: Repetitive clerical tasks were eliminated, allowing the workforce to focus on high-value diagnostic tasks.
• Versatile Scalability: The customizable framework easily adapts to new forms and healthcare domains as the company grows.