Measuring how many referred patients never became an appointment.
Referral leakage is invisible by construction. A referral that stalls produces no error and no complaint — the patient simply goes elsewhere, and the referring office quietly sends fewer next quarter. Most practices cannot produce the number because nothing tracks a referral end to end.
What goes wrong today
Referral leakage is invisible by construction. A referral that stalls produces no error and no complaint — the patient simply goes elsewhere, and the referring office quietly sends fewer next quarter. Most practices cannot produce the number because nothing tracks a referral end to end.
What good looks like
A count at every stage — received, complete, contacted, scheduled, seen — so leakage becomes a number with a stage attached rather than a suspicion.
A definition of 'complete' your schedulers agree with
Stage counts, not just a total
Referring-practice breakdown
Stall alerts before the patient gives up
What to measure
Agree these before changing anything, so the effect is visible rather than argued about.
Referred-to-seen conversion
Largest single stage of loss
Volume trend by referring practice
Where we stand: In development
Referral tracking is in development.
Rollout checklist
Document how this is handled today, and by whom.
Put a number on the hours or dollars involved.
Decide which cases must always reach a person.
Agree the metrics you will judge success on.
Frequently asked questions
Do you solve this today?
Referral tracking is in development.
How do we know if this is our biggest gap?
The Front Desk Opportunity Scan ranks this against every other area using your own volumes.
Keep going on MedReception.ai
MedFrontDesk.ai is the playbook library. MedReception.ai is the platform that runs it.