A government institution responsible for managing public health programs faced increasing challenges processing large volumes of handwritten Medical Examination Report Forms. These forms contained critical information such as patient demographics, diagnostic results, and physician observations, but variations in handwriting and document formats made manual data entry slow and resource-intensive.
As submission volumes grew, processing delays created a significant backlog, limiting the institution’s ability to monitor public health trends and respond to emerging needs in a timely manner.
Infognana partnered with the institution to deploy its Cognitive Data Extractor (rannsCDE), an AI-powered document processing solution designed to automate the extraction of data from complex medical forms.
The solution combined multilingual OCR technology with advanced machine learning and rule-based validation models to accurately capture information from handwritten and structured documents. rannsCDE automatically identified key patient details, medical test results, and physician remarks while adapting to multiple form layouts and document variations.
This enabled the institution to digitize medical records at scale while maintaining high levels of accuracy.
The implementation achieved 95% AI extraction accuracy and reduced processing costs by 80% compared to traditional manual data entry methods. The solution successfully processed more than 50,000 medical forms per day, helping eliminate data backlogs and accelerate public health reporting.
By automating the extraction and digitization of medical examination forms, the institution improved its ability to monitor public health programs, respond more quickly to critical insights, and build a more efficient, data-driven healthcare infrastructure.
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