Reducing Manual Review in Medical Records Processing

Claims adjusters spent 4 to 6 hours daily manually reviewing medical records attached to claims. Documents arrived in inconsistent formats including PDFs, faxes, and scanned images.

Extracting relevant clinical information for claim decisions was tedious and error-prone. Adjusters frequently missed relevant details buried in lengthy documents.

Processing backlogs grew during peak periods, delaying claim decisions and impacting member satisfaction. The manual approach could not scale with volume increases.

We started with a document inventory to understand the types of records received and their relative volumes. Lab results, physician notes, and imaging reports made up 80% of incoming documents.

We built a classification system to automatically sort incoming documents by type. This allowed specialized extraction models for each document category rather than a one-size-fits-all approach.

Extraction models were trained on the organization's specific document formats. We worked with the claims team to identify the key data points needed for coverage decisions.

Rather than fully automating decisions, we created a review interface where adjusters verify AI extractions. This keeps humans in the loop while dramatically reducing reading time.

The system was deployed via API integration with their existing claims platform, requiring no changes to adjuster workflows outside of the new review interface.