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AuthPilot AI, insurance approvals, sorted
AI Healthcare

AuthPilot AI, insurance approvals, sorted

Builds the insurance approval request from the notes, checks it against the rules before sending, and tracks it. Nine days down to two.

ClaudeLangChainFHIRX12 278OCRNode.jsPostgreSQLReact

Getting insurance approval before treatment is the job everyone in a practice hates most. This turns a pile of faxes into something you can actually track.

The problem

  • Doctors and practice managers name this as their single biggest admin headache
  • More than 17 in every 100 Medicare Advantage requests get refused, against 5 for regular Medicare
  • For Medicaid inpatient it can be as high as 44
  • Every rejected request costs around $57 in staff time to redo
  • Staff spend hours hunting through notes for the exact test result or treatment history the insurer wants
  • Nobody owns tracking, so requests get lost and patients wait

How it works

It reads the insurer rules for the treatment you are requesting and works out what they actually want to see.

Then it pulls that evidence out of the patient notes. Diagnoses, what has already been tried, scans, test results, dates.

If something is missing, it tells you before you send, in plain words. It drafts the letter explaining why the treatment is needed, in the format that insurer expects. It sends it, logs the confirmation, and chases it if it goes quiet.

If it gets refused, it prepares your case for the phone call with the insurer, and drafts the appeal.

The important bit

It never submits on its own judgment. It gathers, checks and drafts. A person approves every single submission. That is what made both the compliance officer and the doctors comfortable with it.

An example

An orthopedic group sending 340 requests a month was getting 22% rejected. That is about 75 redone every month, roughly $4,275 in wasted staff time, before you count the delayed operations.

Checking against the rules before sending took first-time approvals from 78% to 94%. The typical wait went from nine days to two. Two staff moved off data entry and onto the complicated appeals that genuinely need a person.

An infusion practice was losing patients to competitors purely because approval took too long. Time to first treatment dropped by six days. The clinical team put it plainly: patients now start treatment the week they are diagnosed instead of the month after.

Built with Claude, document reading for faxed replies, and connections into records and insurer systems.

Interested in building something like this?

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