A leading legal firm was experiencing significant delays due to the manual organization of multi-page patient records. Legal teams spent as much as three days per batch sorting documents by provider, service date, and report type before they could begin case reviews. As caseloads increased and patient records expanded into hundreds of pages, the firm needed a more efficient and accurate way to structure medical documents to support litigation workflows.
To streamline document processing, the firm implemented the Cognitive Data Extractor (rannsCDE), an AI-powered solution designed to automate document classification and organization. Leveraging advanced natural language processing (NLP) and layout-aware AI models, rannsCDE analyzes each page, extracts critical metadata such as provider names, report types, and dates, and automatically groups records into structured folders. This intelligent automation eliminated the need for time-consuming manual sorting while ensuring consistency and accuracy across large document sets.
The implementation of rannsCDE transformed the firm’s document management process. Grouping accuracy improved to 96%, while processing time per batch was reduced from three days to just four hours. With the capability to process more than 500,000 pages each month, the firm significantly enhanced its operational efficiency and gained the scalability needed to support growing litigation demands.
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