AffirmDME's AI reads your documentation, checks it against Medicare's requirements item by item, shows what's missing — with the citation — before you submit, and drafts your ADR (Additional Documentation Request) responses. So you stop reworking denials.
Prior auth means chasing chart notes, re-keying orders, and hoping nothing's missing. When something is, the claim comes back — and reworking a single denial costs about $118. Multiply that across your monthly volume.
Enter the patient, prescriber, and items — we handle the Medicare MAC routing.
Drop in chart notes, the order, test results. AffirmDME reads them automatically.
See a per-item checklist of what Medicare requires and exactly what's still missing.
Send a clean packet. If an ADR comes back, AffirmDME drafts your response.
Sign up free and switch on sandbox mode to explore AffirmDME with realistic sample cases. See the AI readiness checks, document extraction, and the full workflow end to end — without entering any real patient data. When you're ready, run your first real case.
AffirmDME checks every document against Medicare requirements and flags the gaps — with the regulatory citation — before you submit. Not on the PA list? The same checklist makes oxygen, CPAP, and hospital-bed claims audit-ready before an ADR arrives, and drafts the response if one does.
A live view of readiness and status across your team — so nothing stalls and nothing slips.
Affirmation rate, volume, and what's stuck — at a glance.
Reads chart notes, orders, and results — no manual re-keying.
Per-item Medicare requirements — catches gaps before submission, not after a denial.
Fax documentation requests and track responses.
Send the Standard Written Order for signature via DocuSign.
One click assembles the signed order, clinical docs, and checklist into a submission-ready packet.
AI drafts your Additional Documentation Request response.
Affirmation rate, exemption tracking, audit trail.
Per-location PTAN/NPI, billing, and reporting.
Checklists map to real Medicare requirements (LCD/NCD, 42 CFR, CMS PA checklists) for the DME MACs — not a generic "summarize this PDF."
The value isn't reading a document — it's telling you what's missing while you can still fix it, not after a denial.
Upload what you already collect. No new system to learn, no change to how your team works.
At $75 a case, AffirmDME costs less than reworking one denial. See what denials cost you today:
AffirmDME runs on HIPAA-eligible AWS services under a Business Associate Agreement, and a BAA is available for your practice. Document AI runs inside AWS (Amazon Bedrock) — your data isn't used to train models or shared with third parties.
Pay $75 per case while you're getting started. Per-location plans bring that to as low as about $15 a case at volume — we show you when the switch pays.
Overages billed per location on your next invoice. Extra e-signature envelopes $10 each.
We run on HIPAA-eligible AWS services under a Business Associate Agreement, with encryption in transit and at rest and a full audit log. A BAA is available for your practice.
Get started at $75 per case with no subscription. Per-location plans bring that to as low as about $15 a case at volume: Starter is $1,500 a month per location with 50 cases included, Professional $2,200 with 150. The app shows you when a plan would have been cheaper.
Medicare DME prior authorization through the DME MACs (Noridian and CGS).
AffirmDME extracts and checks documentation against Medicare requirements and surfaces what's missing — you review and approve every case before submission.
No. Create a case, upload what you already collect, and submit. AffirmDME fits around how you work today.
Your documents stay within AWS. Document AI runs in Amazon Bedrock and your data isn't used to train models or shared with third parties.
Questions or Enterprise pricing — a person replies within one business day.
CMS adds codes, the DME MACs revise LCDs, cycle deadlines arrive. We send a short note the week it happens, with what to do about it. No drip campaign. Unsubscribe in one click.