Find the fact that decides the case — in minutes, not weeks.
Medrecords AI organizes large files of PDFs, scans, handwriting, and native DICOM imaging into source-linked chronologies, summaries, and report drafts. Legal, insurance, and IME teams can inspect the supporting record, correct the draft, and keep qualified professionals responsible for every opinion and downstream decision.
The risk isn't slow review.
It's the fact you missed.
Every file hides the one fact that decides it. Manual, page-by-page review is exactly where that fact goes missing, buried in 10,000 unsorted pages under a deadline. It happens the same way every week, in 4 steps:
Unsorted, duplicated, part handwritten, part on a disc nobody opens.
Reviewers skim, sections go untouched, and the disc stays in the envelope.
Buried mid-record, invisible until the other side cites it first.
Outsourced review comes back in weeks, with no citations and no imaging. We charge a flat 10¢.
Cycle time slips, and treatment-gap leakage pays out before anyone catches it.
The claims laneThe demand stalls on sorting, while the ambush fact waits at deposition.
The PI laneMedrecords AI reads all of it, every page and every image, and shows you what matters, with the receipts.
Unstructured records in. Structured, cited reports out.
1 engine reads every page and every image (PDFs, scans, handwriting, and native DICOM imaging), rebuilds the case as a chronology, and locks each line in the output to the exact source page it came from, so what leaves this system is structured, cited, and ready to sign.
From any record to
a defensible answer.
1 product story, told once: upload any format (PDFs, scans, handwriting, or DICOM imaging) and it reads everything; the chronology builds itself from what's found, and the report comes out cited and deposition-ready, without a separate review pass.
Any format in. Nothing skipped, nothing guessed.
Multi-page PDFs, scanned records, handwritten notes, full DICOM studies. Each page routes to the right OCR engine, duplicates are removed, co-mingled claimants are separated, and anything low-confidence is flagged for review, not guessed.
We read the imaging — not just the report.
Multi-page PDFs. Scanned records. Handwritten notes. And full DICOM imaging studies with 3D reconstruction. One platform reads them all and reasons across them — a capability most tools in the category simply don't have.
Each page goes to the right engine; anything low-confidence is flagged for review, not guessed.
A multi-model handwriting pipeline reads what other tools skip.
Ingest imaging studies, view them in an integrated PACS viewer, and place imaging events on the timeline.
A medical timeline that builds itself.
Clinical events extract automatically into a color-coded, searchable chronology, every node synced to the source page or image it came from. Scan a 10,000-page history at a glance.
From record to attested report.
A rich-text studio with inline citations and tracked changes. Load your own template and letterhead, attest, export to PDF or DOCX, or push straight to your CRM.
Ms. Ochoa presented to St. Mary's ED on 03/14/2025 with acute low-back pain following a rear-end collisionp.412. MRI of 03/22/2025 demonstrates a moderate 4.2 mm L4–L5 posterolateral herniationMRI·s3·sl18. No prior injury to the region is documentedp.06. The 6-week gap in careflag is addressed in §4.
If we can't cite it, we don't say it.
Every sentence Medrecords AI writes is locked to its source. Click any citation to jump to the exact page (or the exact DICOM slice) it came from. No black box. An AI evaluation framework scores every output, and low-confidence findings are flagged, not hidden.
"AI does the reading. You make the calls — always."
How verifiable citations workClaimant reports onset of low-back pain on 03/14/2025, with an MRI confirming an L4–L5 disc herniation. No prior injury to the region is documented. A 6-week treatment gap appears between visits.
"Patient presents following MVA with acute onset of low-back pain radiating to the left lower extremity. Onset dated to 03/14/2025."
Every fact span-matched to its source page: mechanical, exhaustive, on every extraction.
A second, independent model checks every finding against the source text: models cross-examining models.
Accuracy is the vanity metric. False-pass rate is the one that matters. Releases are gated on it: in claims and litigation, one uncaught error costs more than a thousand caught ones. No per-page review fees, no sampled QA; your experts still make the calls.
The whole platform. No add-ons.
Every subscription is the full system, not a starter tier — OCR, imaging, chronology, citations, claims triage, and reporting all included at one flat per-page rate, with nothing gated behind a separate upsell or add-on module.
Reads any format — PDFs, scans, faxes — into a clean text layer; imports straight from iManage.
Context-aware extraction of handwritten notes, intake forms, and margin annotations — structured, cited, searchable.
Complex medical tables (labs, med lists, billing grids) read by vision AI into structured, queryable rows.
Removes duplicate entries across merged provider files in 1 click, leaving one canonical record.
Detects pages from the wrong patient and quarantines them before analysis — privacy incident prevention.
DICOM-native ingestion, integrated PACS viewer, 3D reconstruction, findings flagged to the page.
Transcribes dictated audio into structured, cited text — the audio sibling of Handwritten Extraction.
Reads and translates foreign-language records with side-by-side original view — every translated line cites the original page.
Extracts clinical events into a color-coded timeline, every entry synced to its source page.
New record batches arrive pre-deduplicated, summarized, and flagged where they agree, conflict, or add.
Ask the record anything — 1 file or 1,000; multi-step investigative answers, every statement cited.
Plain-English semantic search over the whole file, every hit linked to its page.
Auto-categorized, source-linked lists of every diagnosis, medication, procedure, and provider.
Three answer modes — Evidence-Based, Interpretive, Extractive — every line cited.
An AI evaluation framework scores every output and traces citations end to end; unsourced claims get flagged and corrected.
Cross-references treatment history against the file and flags visits, providers, or date ranges that should exist but weren't produced.
Extracts ROM measurements and pain-scale ratings from every visit and lines them up across the file.
Follows one diagnosed condition across every visit that touches it and reads out a trend — worsening, stable, improving.
Extracts stated work/activity restrictions and return-to-work determinations, cited to source.
Inline, cited plain-language definitions for clinical terms across every platform output.
Every treating provider extracted and listed, specialty-enriched, from #11's engine.
Highlights, notes, and tags on record pages, shared across the team — every annotation pinned to its page, work product kept separate.
Rich-text report studio with inline citations, tracked changes, and your letterhead; DOCX export.
Jurisdiction- and line-specific templates; edit in-platform, export DOCX/PDF, deliver via API or SFTP.
Word and HTML exports with live hyperlinks back to every source page.
Configurable rules sort raw claim documents by date, provider, or category, then assemble IME/QME/MSA-ready packets in one click.
Prose-form case narrative, SOAP or plain-language, drafted from the same data as #14/#15.
Merges and sorts a raw batch of PDFs into one ordered file, from #27's engine.
Generates a bookmarked PDF outline from #27's existing sort rules.
9 named litigation chart types as one template library on #15's engine.
Sequential Bates numbers + confidentiality legends, stamped automatically and held stable through dedup, sorting, and re-production.
Auto-detects PHI/PII, other-patient identifiers, and privileged content; suggests redactions for approval; produces a logged, redacted production set.
Color-coded, page-by-page deposition digest plus narrative summary — editable, cited.
Success and value ranges from comparable resolved cases — evidence-derived, work-product separated.
Drafts a cited, jurisdiction-ready demand letter straight from the record — diagnoses, treatment, billing, and future care, no blank page.
Runs chronology, extraction, and qualification criteria across an entire docket at once; flags bellwether candidates.
Flags favorable and unfavorable fact patterns in the record, cited — signals, not a verdict.
When near-duplicate pages differ, the delta is the finding — surfaces late amendments and altered entries, side by side.
Drafts topic-organized deposition question outlines from the digest and record — every question cited to the page it's built on.
Send any imaging study via expiring secure link — no discs, no software installs for the recipient.
Matches peer-reviewed literature and standard-of-care guidelines to the record's diagnoses, cited.
Drafts a cited life care plan by category and cost range from the record — ready for a planner's certification.
Scores case merit or standard-of-care deviation from #34/#35's signals — provisional until a professional signs off.
Drafts the examiner's IME report from exam findings and the case record, via #15's engine.
Bulk-processes claim portfolios against your criteria; only claims needing human judgment reach a human.
Every billed amount traces to a source page — click any dollar figure, land on the document that justifies or contradicts it.
Billing roll-ups and future-care cost tables, ready for life-care plans and specials.
Tracks outstanding record requests with tiered follow-up escalation and a full audit trail.
Checks every diagnosis, treatment claim, and billed figure in an incoming demand against the record behind it.
One-page rolled-up billing summary, grouped by provider/date/category, from #21/#22's data.
Flags intra-record contradictions and cross-claim provider patterns (cloned notes, upcoding signals) — cited signals for SIU review, not accusations.
Compares every billed amount to UCR/geographic benchmarks and flags outliers with percentile context.
Tags each visit and charge as related/unrelated/disputed to the covered incident, cited — adjudicator signs off.
Drafts the WCMSA allocation from the record at Medicare fee-schedule rates — ready for a certified MSA specialist's sign-off.
REST API, CRM token exchange, webhooks — per-case usage metering for bill-back.
Source, timeline, and report on one screen — a synced 3-pane workspace.
Immutable log of every ingest, access, edit, and export — exportable custody report per production.
Expiring, watermarked, view-only links to any cited output — no login or install for the recipient.
Cross-claim provider pattern view benchmarked against specialty, geography, and claim-type peer cohorts, drillable to the underlying evidence.
Aggregates cited billing, provider, and inconsistency signals into an estimated recoverable-value range with a confidence level and executive-ready report.
Builds tenant-separated, privacy-preserving baselines from your own processed cases to sharpen duplicate detection, anomaly signals, and benchmarks over time.
Plots injuries, symptoms, diagnoses, and imaging findings on an interactive anatomical map, with laterality and click-to-source evidence.
Normalizes recurring clinical measurements across providers and visits, plots them over time, and keeps every point linked to its source.
Surfaces explicit negatives and expected-but-undocumented facts, keeping 'not documented' clearly separate from 'did not happen.'
Extracts social, family, occupational, allergy, prior-accident, prior-surgery, and pre-existing-condition history into one evidence-linked profile.
Groups conditions by body system with provisional High/Medium/Low review priority, and lets reviewers confirm or override every tag.
Builds a dated trajectory of work status, functional limitations, rehab goals, assistive devices, and standardized outcome measures.
Parses demanded actions, deadlines, policy requests, allegations, injuries, treatments, and billing assertions into cited fields ready for verification.
Converts raw pages into a structured evidence graph of documents, entities, events, and relationships, with provenance at every layer.
Configures evidence checks, routing, exception queues, deadlines, and mandatory approval steps without letting AI make the final decision silently.
Blocks outcome valuation, settlement ranges, and advocacy-toned language from ever entering the evaluator's workspace.
Maps the causal chain from injury event through symptoms, imaging, treatment, priors, and alternative causes, cited at every link.
Drafts impairment-rating logic against a jurisdiction-specific checklist, every input cited, the final rating the examiner's own.
Reviews a drafted opinion for unsupported jumps, missing exam findings, thin causation, and overreach, the same scrutiny a deposing attorney would apply.
Generates a focused physical-exam checklist, targeted questions, and prior-treatment probes from the record before the exam starts.
Routes the same file to two or three evaluators and shows exactly where their reads diverge, useful for high-value or disputed files.
Links causation statements in a drafted opinion to matched peer-reviewed literature, always shown apart from the record's own facts.
Turns a dictated exam recording directly into a structured IME report draft, built on the platform's existing dictation and drafting engines.
Converts the record into structured plaintiff, defense, causation, and damages theory outlines, each point cited, ready for counsel to shape.
Assembles a complete demand package, cited chronology, damages narrative, exhibit set, and a benchmark-supported value range, building on Settlement Demand Letter.
Generates targeted deposition question sets for treating providers, the plaintiff, and opposing experts, each cited to the record.
Builds likely cross-examination paths from inconsistencies, gaps, priors, and imaging ambiguity in the record, a rehearsal tool, not a script.
Compares the documented care timeline against the expected diagnostic and treatment pathway, flagging departures, labeled provisional until a clinician reviews.
Extracts pain, functional loss, work impact, and activities-of-daily-living evidence from the record into a structured, cited damages narrative.
Organizes cited evidence, benchmark comparisons, and open questions relevant to a settle, litigate, or investigate-further decision, the analysis is drafted, the call stays counsel's.
Reads the disputed clinical issues in the file and suggests the medical specialties most relevant to retain as an expert, a starting list, not a referral service.
Compares a claim's current reserve to a benchmark range built from medical severity drivers and comparable resolved claims, a reference point, not a verdict.
Flags claims trending toward high-severity thresholds based on early medical records, treatment intensity, injury type, and comparable trajectories.
Flags claims that settled above the comparable cohort without medical drivers that explain the gap, a pattern signal for review, not an accusation.
An opt-in internal QA tool: compares a sample of the carrier's own claim decisions against the record evidence and its own playbook, for training and process improvement.
Surfaces which providers, venues, and treatment patterns correlate with higher severity or cost across a claim portfolio, benchmarked and cited with sample size shown.
Analyzes a carrier's own historical outcomes by the defense counsel it has engaged, adjusted for claim severity, venue, and injury mix, a management view of the carrier's own data.
Models the likely cost, time, and evidence implications of different claim-handling paths side by side, the adjuster picks the path.
Scans the record for signals of third-party liability, duplicate coverage, and Medicare, Medicaid, or ERISA recovery opportunities, flagged for your recovery team.
Builds a structured fingerprint for every case, injury, venue, treatment, gaps, priors, and outcome, powering cohort matching across benchmarks and pattern features.
Upload historical closed files and see retrospectively where missing records, severity signals, benchmark variances, or documentation gaps would have surfaced earlier.
An opt-in program where participating carriers and firms contribute de-identified, aggregated closed-case data to build stronger shared benchmarks.
A dedicated API surface exposing the platform's structured evidence graph, entities, relationships, and provenance, so your own tools can consume cited medical facts directly.
Splits the case workspace into distinct evidence, work-product, valuation, IME-neutral, and audit views so privileged analysis never leaks into neutral opinion work.
Routes specific case work to vetted human specialists, IME physicians, legal nurse consultants, coders, life-care planners, when the file calls for expertise beyond the platform.
A single, permissioned case room where counsel, the adjuster, a retained expert, and a mediator each see only what their role is entitled to.
Exports a mediation-ready package: the benchmark-based neutral range, the specific drivers in dispute, and the cited evidence behind each, built for the settlement conference.
1 platform, 3 lanes.
The attorney, the adjuster, and the examining physician read the same 10,000-page file for opposite reasons — the fact that wins a case, the charge that reveals leakage, the finding that supports an opinion. Pick your lane below to see what Medrecords AI pulls from that same file for your work.
Build a stronger case, faster — every fact cited.
For the adjuster with 14 files in the queue (Insurance & Claims)
Cut review time and leakage — with an audit trail.
- Reach determinations on the full record: a cited summary and chronology per claim, in minutes.
- Catch leakage before it pays out — treatment gaps, duplicate billing, and unsupported charges, flagged with sources.
- Defensible for the file: every finding traceable to its page, every PHI access logged.
Minutes to a cited claim summary — leakage flagged with sources, not after it pays out.
For the examiner who signs the opinion (IME / Medical-Legal)
Triple your throughput — without risking your opinion.
- Ground your opinion in the evidence itself: DICOM studies, PACS viewer, and 3D — not just the radiologist's report.
- Causation and apportionment support, surfaced and sourced — the conclusions stay yours.
- Reports on your letterhead, in your voice — templates load from case context and export clean.
3× files per week with the same staff — and an opinion that holds up under cross.
The work you were about to outsource.
Chronologies, demand letters, deposition summaries, IME reports: the deliverables review vendors sell in days, drafted here in minutes, every line cited. Pick the one on your desk.
Chronologies & Summaries
Timelines, treatment history, and case-ready record reviews — cited to the page, for attorneys and insurers.
Compare the evidence, not the category labels.
In-house review, outsourced services, specialized software, and general-purpose AI vary by provider, contract, configuration, and date. Use the same due-diligence questions for every option and verify material claims directly.
The practical questions, answered.
Capabilities, review boundaries, security, pricing, deployment, and the safe evaluation path.
How do I know it isn’t making things up?
Every answer and chronology entry is locked to its source page or DICOM slice; you verify by clicking through to the original. If we can’t cite it, we don’t say it, and low-confidence findings are flagged, not hidden.
Will it skip or silently drop pages?
No. Every page is accounted for (read, classified, or quality-flagged), and you get a coverage report per case showing pages received, processed, and included or excluded with a reason.
Does it interpret, or just extract?
You choose the mode: Extractive (facts and citations only, zero inference), Interpretive (a clinical synthesis), or Evidence-Based (each statement citation-backed, source shown).
Is there a 1,000-page limit?
No wall. It’s built for real charts — 10,000+ page records with pagination and lazy loading, plus full DICOM studies. Generic chat tools reject files that size; this doesn’t.
How long does a large case take?
Typically minutes to hours end-to-end, even for a very large file — versus days or weeks for a manual reviewer.
Does it read handwritten notes?
Yes. Handwritten and low-quality pages escalate through a multi-model pipeline, and anything uncertain is quality-flagged for your review rather than silently guessed.
What if it misreads a date?
You correct it and regenerate — the change cascades to the timeline and report. The expert is always the source of truth.
Which document types does it read?
Clinical notes, operative and radiology reports, labs, pharmacy, billing and EOBs, legal documents, and native DICOM imaging — with per-page quality flags where legibility is poor.
Is my PHI safe, and will you train on it?
No training, ever. Your records stay in your control under HIPAA controls, SOC 2 audit-ready documentation, encryption in transit and at rest, and PHI access logging on every event.
Why is this safer than pasting into ChatGPT?
Consumer ChatGPT isn’t a protected environment, and general AI tools aren’t built for medical-record work: no citation back to the source page, no HIPAA-grade access controls or BAA, and no handling for 10,000-page charts or native DICOM imaging. Medrecords AI is an access-controlled, HIPAA-grade platform purpose-built for exactly that, with case-level permissions and full audit logging.
Is using AI defensible in court?
Transparency is the answer: every statement traces to a source page or slice, you remain the author and attester, and Extractive mode keeps output free of interpretation when you want zero inference.
Do I have to share records with third parties?
No. Records are processed within our platform, and a de-identified export is available when you need to share without exposing PHI.
How is it priced?
Transparent per-page credits — no per-seat lock-in, and credits don’t expire. Run your own numbers in the ROI calculator above.
Will I get a surprise bill?
No. Pricing is usage-based and predictable, with credit packages you control — no open-ended monthly charges.
Does asking the record questions cost extra?
No per-question fee — interrogating the record is part of the workspace, not a metered add-on.
Can I edit output and have it update everything else?
Yes — correct a finding or date and regenerate; the change cascades through the chronology and report, and version history is preserved.
Can I use my own template, branding, and signature?
Yes — custom templates that load from case context, your logo and signature attestation, and export to PDF and DOCX with a clean citation-free option.
Can I ask the record questions in plain English?
Yes — interrogate the whole chart, chart values across date ranges, and get cost math, each answer cited. Threads persist across sessions.
Will it fit the systems I already run?
Connect links Medrecords AI to your case-management and claims tools through a secure REST API and CRM token exchange (no shared credentials), with outbound webhooks.
Can it digest depositions, not just medical records?
Yes — a color-coded, page-by-page deposition digest plus a narrative summary, both editable and exportable to Word with live hyperlinks back to the transcript.
What happens to duplicate records across providers?
AI deduplication removes duplicate entries across merged files in 1 click — and duplicate billing lines are flagged, not silently merged, so leakage stays visible.
Can I share imaging without burning discs?
Yes — send any study via a secure link. The recipient views it in the browser: no software, no discs, and every access is logged.
Do exports keep the citations?
Every timeline, digest, and report exports to Word or HTML with live hyperlinks back to the original source pages — trial-ready without reformatting.
Can it support future-care cost projections?
Yes — billing roll-ups and future-care cost tables assemble from the record with sources attached, ready to drop into a life-care plan or specials calculation.
Can it support demand packages and deposition prep?
Yes — cited chronologies, demand support with every injury, treatment, and dollar traced to its page, and Extractive mode when you need zero inference for exhibits and cross.
Can it manage the IME → deposition → trial workflow?
Imaging, causation support, and templated attested reports are here today; the full lifecycle CRM (intake, scheduling, invoicing) is on the roadmap.
Will the narrative reflect my rehab lens, not acute care?
Yes — steer it toward function, rehabilitation, and future needs, with Life-Care-Plan specifics and editable, code-transparent cost tables.
Can it flag fraud, waste and abuse — not just summarize?
Yes — audit claim samples, flag patterns like treatment gaps or tampered dates, and turn physical files into structured data. For full TPA claims workflows we pair with your coding and rules.
Can I white-label it and resell to attorneys?
Yes — your logo, editable pricing, and case-scoped document organization; reliability and full-page accounting make it demo-able to attorneys.
Can I use it just for my own records?
Yes — understand your own records privately at per-page pricing, without hiring anyone. HIPAA-grade, never consumer ChatGPT.
What is medrecords.ai?
Medrecords AI turns medical records, claims documents, and imaging into structured chronologies, cited summaries, and searchable answers. It is built for law firms, IME and QME providers, insurers, TPAs, and claims teams who need to review large record sets fast without giving up defensibility. Every answer traces back to its source page or DICOM slice.
Who is medrecords.ai built for?
Legal teams and paralegals, IME and QME providers, expert witnesses, insurers, TPAs and claims adjusters, life-care planners, and rehab professionals: anyone who has to turn a large, messy medical file into a defensible chronology, summary, or answer, fast.
What’s the difference between a medical summary and a medical chronology?
A medical summary condenses the most clinically relevant facts from a file. A medical chronology arranges every event in date order so you can see treatment progression, causation issues, and gaps in care at a glance. Medrecords AI generates both, plus an Extractive mode with zero inference when you need citation-only output for exhibits.
How do I get started? Is there a free trial?
The fastest start is a guided file test. Book a short scoping call first; we confirm fit and complete the required agreements before providing an approved secure intake route for any file containing PHI. Do not email or upload medical records through the public website.
How should I evaluate medrecords.ai against other tools before buying?
Pilot it on two or three of your own real files: one simple case, one complex one. Check citation quality (can you click through to the source page for every claim), measure turnaround at your typical page count, confirm BAA and security terms, and get sign-off from the reviewers who will actually use it daily before rolling out broadly.
What support do I get as a customer?
Support is founder-led: you talk to the people who build the platform, not a ticket queue, with replies within one business day. For security incidents we are reachable 24/7 and follow HIPAA incident-management rules end to end.
Does medrecords.ai replace my legal nurse consultant, paralegal, adjuster, or physician reviewer?
No. It removes the manual page-turning, not the judgment. Your reviewer still makes the clinical and legal calls; Medrecords AI gets them to a cited, organized record faster so more time goes to analysis instead of extraction. The expert is always the source of truth: correct a finding and regenerate, and the change cascades through the chronology and report.
How long does setup take? Does my IT team need to be involved?
Self-Service starts the same day, in our HIPAA-eligible cloud, with no IT ticket needed. AI Enablement (your own keys and cloud account) is typically about a week, and Enterprise on-prem deployments take roughly four weeks. SSO and API setup are part of the guided enterprise path.
Can it flag missing or incomplete records, not just duplicates?
Yes. Beyond deduplication, Missing Records Identification cross-references the treatment history against the file and flags visits, providers, or date ranges that should exist but were not produced, so you can request them before writing a report, demand, or reserve rather than finding out at deposition.
How does per-page pricing compare to outsourced medical record review?
Medrecords AI publishes a 10¢ per-page Self-Service rate. Compare that with your fully loaded current workflow, including preparation, review, corrections, management time, vendor minimums, implementation, and switching costs. The ROI calculator is an estimate based on the inputs you provide, not a guaranteed savings claim.
Does medrecords.ai support role-based access control (RBAC)?
Case-level permissions and full audit logging are in place today: you control who can access each record set, and every access is logged. Full role-based access control (RBAC) is coming soon; if RBAC is on your procurement checklist, ask us about the rollout timeline.
A transparent per-page rate, without a promised ROI.
Self-Service is listed at 10¢ per processed page. Your economic result depends on file mix, review requirements, corrections, management time, implementation, and the cost of your current workflow. Model those inputs directly; do not treat a marketing benchmark as a guaranteed saving.
Examples show platform processing charges only. They exclude taxes, implementation, professional review, rework, and other workflow costs. Confirm current pricing and terms before purchase.
See the workflow. Then scope a safe file test.
Book a product walkthrough or a guided file-test intake. When PHI is involved, Medrecords AI confirms fit, completes the required agreements, and provides the approved secure transfer route before any record changes hands.
Start with fit, agreements, and the right intake route.
Describe the record type and intended output without sending PHI. If the evaluation is a fit, we complete the required agreements and provide approved secure intake instructions.
Prefer email? [email protected]