Referrals

Referral Leakage: Finding Patients You Never Saw

Measuring how many referred patients never became an appointment.

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.

More in Process