Dodon.ai

Your firm is overpaying for medical record summaries. Here's the math. Outsourced vendors charge $5-15/page with 5-10 day turnaround. A mid-size PI firm processing 3,000 pages/month pays ~$288,000/year when you factor in QA review time, revision cycles, and formatting inconsistencies. AI-powered summarization costs ~$0.02/page with turnaround in minutes, not days. The annual savings by firm size: → Solo/small firm (500 pages/mo): ~$47,700 saved → Mid-size PI firm (3,000 pages/mo): ~$275,400 saved → High-volume practice (10,000+ pages/mo): ~$780,000 saved And speed matters beyond cost — every day waiting on a chronology is a day demand packages, case evaluations, and settlement negotiations stall. The practical move: start with one case type, run AI alongside your vendor for a month, and let the numbers speak. https://lnkd.in/gE4cN6HE #LegalTech #MedicalRecords #PersonalInjury #LawFirm #AIforLaw #MedicalChronology #LegalOps

Workers' comp medical records aren't like standard personal injury files. The decisions that matter — MMI dates, impairment ratings, return-to-work restrictions — all come from specific clinical milestones buried in the records. Here's what attorneys and adjusters should be extracting: → MMI determination: who declared it, when, and whether providers disagree → Impairment ratings: methodology used, body parts rated, and objective findings → Return-to-work status: restrictions at each visit mapped against employer accommodations → Treating physician vs. IME conflicts: side-by-side comparison of opinions → Pre-existing conditions: apportionment percentages and pre-injury baseline Start with the milestones, not the timeline. The treatment details organize themselves around the events that actually determine the outcome. https://lnkd.in/gc66NZWZ #WorkersCompensation #MedicalRecords #LegalTech #WorkersComp #PersonalInjury #IME #MedicalChronology

Before litigation teams trust deposition summary software, they’re all asking the same thing: “Can I verify this in under 5 minutes?” Here’s what reviewers actually look for before they rely on AI-generated summaries: page-line citations on key testimony, one-click jump backs to the transcript, and exports that keep citations intact for motion and trial prep. If your current tools make you download, search, scroll, and guess, this post will show you what a verification-first workflow looks like—and why it matters more than vague “accuracy” claims. Read the full breakdown: https://lnkd.in/gxMHTAqe #LegalTech #Litigation #DepositionPrep #AIforLaw #DepositionSummaries

Before your team relies on AI deposition summaries, they're checking one thing: Can I verify this in 5 minutes or less? Here's what litigation teams verify before trusting deposition summary software: ✓ Page-line citations on admissions, timeline facts, and liability points ✓ One-click verification (not download → search → scroll → guess) ✓ Export to Word/PDF with citations preserved ✓ Issue-based formatting for motion prep The tools that get adopted make verification a habit, not a special effort. Read what reviewers actually check (and why page-line citations matter more than "accuracy claims"): https://lnkd.in/gMxsydiQ #LegalTech #Litigation #DepositionPrep

Ctrl+F only finds exact matches. Medical records rarely use exact language. Across providers and time, the same fact shows up as a diagnosis, a shorthand note, or a symptom description. Keyword search treats those as unrelated, which is how important details slip through review. This piece breaks down why keyword search falls short in medical records, and how meaning-based search helps case reviewers and IME teams surface facts faster, with citations back to the source. Read the full post: https://lnkd.in/ge9zeWTs

NEW ARTICLE: Search "prior knowledge" across 5 depositions using Dodon.ai. Get cited testimony from every witness in a table, ready for your motion outline. No narrative summaries needed. https://lnkd.in/g3mWSn8m

Happy New Year! A quick read for teams starting the year with active medical chronologies: where review time is really lost and how first-pass review gets faster. https://lnkd.in/ghRngWAd

Medical records can look complete and still miss providers. ✖️ Referrals live inside notes. ✖️ Facilities don’t align. ✖️ Timelines hide gaps. Why this matters before expert review ↓ https://lnkd.in/gASpNFCh

Teams lose hours rereading deposition transcripts just to confirm whether testimony actually conflicts. Most contradictions aren’t obvious. They’re small shifts in timing, scope, or phrasing across witnesses, and they get missed when review turns into page-by-page scanning. This piece walks through the search-first workflow we see teams use to surface inconsistencies, compare answers with citations, and verify directly on the transcript before moving into analysis. Read: https://lnkd.in/gn7udTYd

Most ISSUES with AI deposition summaries aren’t about accuracy but about VERIFICATION. If a summary can’t show page-line citations and source pages, it shouldn’t be relied on. Here’s how review teams actually verify summaries before they circulate: https://buff.ly/1Pw8pmb