Medical record review has gone through four generations of technology. Understanding where we've been helps explain where we're going — and why the current moment matters for claims professionals.
Generation 1: Keyword search
The earliest digital approach: search for "diabetes" and get every mention across a file. Better than flipping pages, but limited. No context. No connections. No understanding of what actually matters for the claim.
Generation 2: Entity extraction
AI learned to identify specific data types: diagnoses, medications, procedures, dates. Medical records became structured data instead of raw text. This was a major step forward. But extracted entities without context don't tell a story. Knowing a patient was prescribed metformin doesn't explain why, or what it means for the claim.
Generation 3: Generative summaries
Large language models added a narrative layer. AI could now write coherent summaries that connected extracted data points into readable reports. But there was still a gap: users had to determine which information was relevant to their specific case. The AI summarized everything — adjusters and physicians still had to connect dots.
Generation 4: AI agents and Q&A
The current evolution. Instead of reading vendor-defined summaries, users can ask specific questions and get targeted answers.
- "Was the claimant compliant with prescribed treatment?"
- "What's the progression of the lumbar condition over the past 3 years?"
- "Are there any pre-existing conditions related to this injury?"