Denials

Reducing Front-Desk-Caused Claim Denials

The denials that trace back to registration rather than coding.

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.

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