Here's the math that keeps claims teams stuck. Right now, most IME physicians, case managers, and adjusters spend roughly 85% of their time on extraction work: reading pages, finding dates, copying diagnoses, organizing timelines, hunting for pre-existing conditions buried on page 347.
Only about 15% goes to what actually matters: clinical judgement, causation analysis, decision-making, communicating with stakeholders. That ratio is backwards.
The extraction trap
Primary care physicians spend over 50% of their workdays on their EHRs — about 4.5 hours per day in the clinic, and 1.5 hours after hours each day at home (Mcpdigitalhealth).
The same pattern shows up in claims work. Medical records arrive as hundreds or thousands of unstructured pages. Someone has to read them, extract the relevant information, organize it chronologically, and flag what matters. That work is necessary. But it's not where expertise adds value.
What AI changes
AI-powered innovation can cut medical records review time by up to 90% (Verisk). AI-powered medical chart review can identify relevant medical evidence up to 60% faster than a purely manual review (Mosmedicalrecordreview).
When AI handles extraction, the ratio flips. Instead of 85% extraction / 15% decision-making, you get closer to 15% review / 85% clinical work. Same number of claims. Same expertise. Dramatically different output.
What this looks like in practice
Before AI
- 3 hours reading a 500-page WCB file
- 30 minutes organizing findings
- 15 minutes writing the actual causation analysis
After AI
- 20 minutes reviewing AI-extracted data
- 15 minutes asking follow-up questions
- 90 minutes on detailed causation analysis
The AI didn't make the decision. The physician did. But the physician had 90 minutes for the work that matters instead of 15.
The compound effect
When clinicians spend more time on clinical work, outcomes improve:
- Faster turnaround on reports
- More thorough causation analysis
- Better documentation for litigation risk
- Fewer errors from fatigue and rushing
- Higher capacity without hiring
Platforms can reduce processing time for medical records by up to 72% while achieving 97% accuracy or higher (DigitalOwl).
How OctopusLM approaches the 15/85 flip
We built OctopusLM to handle the extraction work so Canadian healthcare professionals can focus on what they're trained for.
- AI extracts diagnoses, treatments, symptoms, and timelines
- Page-level citations for quick verification
- You review, edit, and finalize — keeping clinical judgement where it belongs
- PIPEDA compliant. Enterprise-grade security.
Sources:
Mayo Clinic Proceedings: Digital Health 2024
Verisk Discovery Navigator Research 2022
MOS Medical Record Review Industry Analysis