AI referral intake automation reads each inbound referral, identifies the patient and referring provider, extracts the clinical and demographic fields your intake process requires, checks the referral for completeness, and routes it to the right queue. Incomplete referrals are flagged with exactly what is missing rather than filed and discovered later.
Four steps between arrival and a schedulable file.
Patient identified against your records, referring provider identified, referral type determined.
Diagnosis, requested service, insurance details, authorisation status and clinical notes pulled into structured fields.
Compared against what your intake process actually requires. Anything missing is named explicitly.
Complete referrals go to scheduling. Incomplete ones go back out with a specific request, not a generic one.
None of these are exotic. They are what a scheduler deals with every day.
The referral arrives, scheduling books it, and the authorisation was never obtained. Caught at intake instead.
Two patients with similar names. A confident mismatch is worse than no match, so ambiguous cases go to human review.
Handwritten referrals are read where possible and escalated where not, rather than sitting in a pile.
A referral with no diagnosis or requested service cannot be triaged clinically. Flagged immediately, not at booking.
Coverage that does not match the requested service, surfaced before the patient is called.
An urgent referral formatted identically to a routine one, separated by content rather than by form.
It reads each inbound referral, identifies the patient and referring provider, extracts the fields your intake process requires, checks completeness, and routes the referral to the right queue with anything missing named explicitly.
It is flagged with exactly which fields are missing, so the follow-up request is specific rather than a generic "please resend". That usually collapses two exchanges into one.
Multi-signal matching across the identifiers present in the referral. Where signals conflict or confidence is low, the item routes to human review rather than being matched incorrectly.
It reads them where legibility allows and escalates where it does not. A referral that cannot be read reliably is surfaced for a person rather than guessed at.
No. It produces a referral that is ready to book, with everything scheduling needs already extracted and checked. The clinical and scheduling judgment stays with your team.
The pipeline referrals arrive through.
The other half of the intake problem.
How urgent referrals get separated from routine ones.
Send a week of real ones. We will show you which would have been flagged at intake.
Card required to start · BAA before PHI · Answer in one business day