Compare
Medrecords AI vs.
Medrecords AI vs.
the alternatives.
Reading every page — and never putting patient records where they legally can't go — is what separates purpose-built review from everyone else.
The options →
Medrecords AI
AI records review ·
built for PHI
built for PHI
In-house nurse
Legal nurse on staff
Freelancer
Outsourced reviewer
BPO / Agency
Offshore review shop
OTHER SPECIALIZED AI TOOLS
Other AI tools
Generic AI
ChatGPT · Claude · Gemini
What it does
how the record actually gets read
Reads the whole file
10,000+ page records
Every page — in minutes
Reads it all, but slowly
Manual, page by page
Teams read in shifts
Most do — queued & tiered
Chokes past ~1,000 pages
Imaging (DICOM)
the scan, not the report
Reads the MRI/CT + 3D view
Radiology report only
Radiology report only
Radiology report only
None read the actual study
Can’t open DICOM
Handwriting & bad scans
messy source records
Multi-model OCR, flagged
Human-read, slow
Varies by reviewer
Varies by shift
Single OCR pass, unflagged
Skips or guesses
Multi-model engine
never 1 model’s word
Several frontier models, cross-checked
1 reader, 1 read
1 reader, 1 read
Many readers, no cross-check
Single-model stack
1 model, 1 view
Medical chronology
a timeline of care
Auto-built, synced to source
Typed by hand, hours
Typed by hand
Typed by hand
Flat list, citations vary
Partial & uncited
Every line cited
traced to the exact page
Click to the page or slice
Usually, if noted
Sometimes
Rarely itemized
Some — page-level at best
No source trail
Gaps & contradictions
the fact that gets missed
Flagged, with sources
Caught if spotted
Caught if spotted
Often missed at volume
Basic flags, unsourced
Not reliable
Consistency
same file, same result
Same rules every time
Varies by the nurse
Varies by contractor
Varies by shift
Silent model swaps
Drifts run to run
You stay the author
the final call is yours
Edit, regenerate, attest
They write it for you
They write it for you
They write it for you
Often locked output
Copy-paste by hand
The outcomes
why teams actually switch to us
Turnaround
time to a usable review
Minutes to hours per file
A day or more per file
Days to weeks
Days, plus a queue
Hours–days, queued
Hours of prompting, still partial
Cost
what you pay per page
A flat 10¢ per page
~$1.30–1.95/page
fully loaded
$100–200/hr — $2–4/page
20–75¢/page +
monthly minimums
10–40¢/page
or $400+/mo plans
Subscription
+ your hours
Cost per 1,000-page
the all-in number
$100, flat
~$1,300–1,950 in staff time
$2,000–4,000
$200–750
$100–800
“Free” — plus ~20 hrs of your time and the HIPAA risk
Minimums & lock-in
what you commit up front
None — pay per page, cancel anytime
A full-time salary
Retainers $750–3,000
$100/case floor; $4,500/mo minimums
Annual contracts common; some waitlisted
Seat minimums; enterprise floors
Volume & scale
when the files pile up
Any volume, files in parallel
One reviewer, one file
Capped by headcount
Scales, but slowly
Plan caps & seat walls
One chat at a time
PHI compliance
the legal line you can’t cross
HIPAA controls, under a signed BAA
Depends on their setup
BAA often missing
Offshore PHI exposure
BAA varies by vendor
No BAA — not permitted for PHI
Your data & IP
where records end up
Never trains a public model
Stays in your control
Varies by contract
Often offshore-hosted
Training opt-outs vary
May train on what you paste
Deployment
where it runs is your call
SaaS, private cloud, or on-prem
On-site by definition
Their laptop
Offshore facilities
Their cloud only
Vendor cloud only
Audit trail
who touched what, when
Every PHI access logged
Manual notes
Little to none
Inconsistent
Rarely surfaced
None
Missed-fact risk
the cost of one miss
Full-page accounting
Human fatigue
Quality varies
Volume errors
No page accounting
High — silent gaps
A note on generic AI and PHI. Consumer ChatGPT, Claude, and Gemini are not HIPAA-eligible without a signed Business Associate Agreement. Uploading patient records to them can breach HIPAA and expose your practice to real liability. Medrecords AI processes every file under a signed BAA, with PHI access logging — and never trains a model on your data.