The denials that trace back to registration rather than coding.
A meaningful share of denials originate at the front desk: wrong payer, mistyped member ID, missing referral, coverage that was inactive on the date of service. They are diagnosed in billing weeks later, and the feedback rarely reaches the person who made the entry.
What goes wrong today
A meaningful share of denials originate at the front desk: wrong payer, mistyped member ID, missing referral, coverage that was inactive on the date of service. They are diagnosed in billing weeks later, and the feedback rarely reaches the person who made the entry.
What good looks like
Front-desk-caused denials counted separately and fed back to the front desk, so the error type is visible to the people who can prevent it.
Denials coded to a front-desk cause, not lumped together
Feedback loop to the desk, not just to billing
Validation at capture for the top recurring causes
Eligibility resolved before the date of service
What to measure
Agree these before changing anything, so the effect is visible rather than argued about.
Clean-claim rate
Denials attributable to registration
Rework hours per 100 claims
Where we stand: In development
Eligibility automation is in development; capture accuracy is live.
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?
Eligibility automation is in development; capture accuracy is live.
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.