Medical record review is one of the most time-consuming parts of preparing a personal injury, medical malpractice, or workers' compensation brief. A single treatment history can span hundreds of pages of provider notes, imaging reports, billing codes, and discharge summaries — most of which never make it into the final pleading.
Over the past two years, AI-assisted review has quietly become viable for solo and small firms. The workflows below reflect what we've seen work in real cases, not vendor demos.
1. Structured intake before ingestion
The largest gains happen before a single page is uploaded. A clean intake — mechanism of injury, treating providers, dates of service, prior conditions — turns record review from an open-ended search into a targeted query.
2. Chronology generation
Once records are ingested, the first useful output is a treatment chronology: date, provider, complaint, objective findings, plan. A well-prompted model produces a draft chronology in minutes that would take a paralegal a full day.
3. Causation and gap-flagging
The higher-value output is the gap analysis: treatment gaps longer than 30 days, prior symptoms that predate the incident, inconsistent pain scales, missing diagnostic studies the defense will ask about.
4. Draft brief sections
With a verified chronology and a gap list, drafting the Statement of Facts and the Damages section becomes a rewrite rather than a blank page.
What to watch out for
- Hallucinated citations. Never let a model cite a page number or record date without a human verifying it against the source.
- PHI handling. Use a vendor with a BAA and a clear data-retention policy.
- Model drift. Re-run the same prompt every few months — outputs change as models are updated.
This article is for general information and does not constitute legal advice. Reading it does not create an attorney-client relationship.