From patient encounter to recovered revenue — AI + VMA support at every step
From patient visit to payment — showing where AI and VMAs plug into the existing workflow
Patient Arrives
Verify Insurance
VMA: Prior Auth
1–3 hrs manually
Clinical Encounter
Doctor sees patient
Clinical Notes
Documented in EHR
AI: Notes Analysis
Documentation check + CPT/ICD suggestions
VMA: Code Claim
Using AI-suggested codes
AI: Claim Audit
Flags errors before submission
VMA: Fix + Submit
Clean claim sent to payer
Payer Processes
Insurance reviews claim
AI: Denial Inbox
Denied claim detected
AI: Appeal Letter
50 templates — instant draft
VMA: Review + Send
Customizes + submits appeal
AI: Underpayment
Detects vs. contracted rate
VMA: Dispute
Recovers underpaid amount
Revenue Recovered
Case closed
Analytics Updated
Win rates tracked by payer
💡 Where PayerLoom Pays for Itself
Three money moments: (1) Catching coding errors before submission — prevents denials at the source. (2) Detecting underpayments vs. contracted rates — 3–8% of revenue typically leaked here. (3) Generating specialty-specific appeal letters in seconds — VMAs spend hours writing these manually today.
Today this takes 1–3 hours per case. AI collapses this to minutes with VMA oversight.
Doctor Orders Procedure
System detects prior auth is required based on payer + CPT code
AI Reads EHR
Pulls diagnosis, procedure codes, patient history, payer plan
AI Checks Payer Policy
Real-time payer policy library — looks up approval criteria for this CPT + plan
AI Drafts PA Request
Full prior auth with clinical justification generated in seconds
VMA Reviews in 90 Seconds
Instead of 90-minute manual process — reviews AI draft, adjusts if needed
Submitted to Payer
Via portal, fax, or API — AI tracks and sets follow-up reminders
AI Monitors Decision
Alerts VMA if no response within CMS-mandated 7-day window
Auth Approved
Service scheduled — outcome logged to improve future PA accuracy
🚀 Why Prior Auth Is the Wedge Feature
CMS-0057-F (effective Jan 1, 2026) mandates payers respond to PAs within 7 days / 72 hours expedited. A practice doing 20 PAs/week × 2 hours each = 40 staff hours saved per week. At $25/hr = $52,000/year saved per practice. At $999/mo, the ROI is 4×. This sells itself.
What the PayerLoom VMA sees when they log in each morning — their AI-powered workqueue
VMA logs in — 8AM Manila / 7PM ET
PayerLoom has pre-worked everything overnight
Denials Inbox
3 new
AI drafted appeal letters for each — VMA reviews + sends
Claims Flagged
7 claims
AI flagged coding errors with fix suggestions — VMA corrects
Notes Gaps
2 alerts
Documentation issues from yesterday — doctor needs to add notes
Prior Auth Queue
5 pending
AI tracking decisions — 1 expiring tomorrow
Every VMA correction teaches the system — the moat gets deeper over time
AI flags a claim
VMA reviews + corrects
Correction logged by specialty + payer + code
AI retrains on real-world outcomes
Next claim: AI is more accurate
After 12 months: knows which codes get denied by which payer
| Workflow Step | Without PayerLoom | 🟣 VMA Does | 🔵 AI Does | Impact |
|---|---|---|---|---|
| Claim coding | Manual, 30+ min/claim | Reviews AI suggestions | CPT/ICD from notes | ~80% faster |
| Claim audit | Denial catches the error | Fixes flagged claims | Audit engine pre-screens | Prevents denial |
| Appeal letters | 1–2 hrs writing from scratch | Reviews + customizes | Draft in seconds, 50 templates | ~90% faster |
| Prior auth (future) | 1–3 hrs per case | Reviews AI draft | Full PA from EHR data | ~95% faster |
| Underpayments | Almost never caught | Disputes flagged amounts | Compares vs. contracted rate | 3–8% revenue recovered |
| Documentation gaps | Found at denial | Alerts doctor | Real-time sufficiency check | Prevents denial |
The No-Brainer Pitch
"We will recover at least 5× our fee in denied and underpaid claims in your first 90 days, or we refund you — and we'll handle every prior auth within 24 hours."
AI handles the cognitive work. VMAs handle the judgment calls. Together they recover revenue the practice didn't know it was losing.