How this AI upgrade saved one dental group

Hi and happy Tuesday,

Last week, we spoke with an RCM director at a 10‑location dental group in Texas.
They’d just migrated to Tab32 and thought their claims were “mostly automated.”

Until they looked at their numbers:

• 67% clean‑claim rate

• Staff overage ≈20% just to re‑work denials and demographic errors

Their primary pain was delays, not audits. That conversation surfaced three lessons most multi‑site practices can use right now—whether you’re a dental clinic or not:

1. Automation is only as good as its blind spots

They already had a front‑end scrubber. But it stopped where real revenue risk begins - after the claim hit “Submit.”

Demographic typos, missing narratives, and mismatched x‑rays kept boomeranging back from payers.

Takeaway: If your scrubber doesn’t cross‑check narrative, images, and codes against payer policy, you’re not automated, you’re optimistic.

2. Low clean‑claim rate = payroll drain

At 67% clean claims, the team estimated they needed ~20% extra headcount just to fix preventable mistakes.

Quick self-diagnostic:

• Pull last month’s claims by location

• Count how many required post‑submission fixes (demographics, missing docs, code swaps)

• Multiply by your average claim value to see how much revenue you “hired” to re‑work

Example:

Step 1: Claims by location. Location A: 1,200 claims /  Location B: 900 / Location C: 1,050

Step 2: Claims needing fixing:  A: 180 / B: 140 / C: 120
(That’s 440 total—or ≈ 14 % of all claims.)

Step 3: Average paid amount per claim = $180
440 unclean claims × $180 = $79,200 in revenue you hired staff to chase for one month.

Cleaning those claims up‑front could free ~$80 k /month in avoidable re‑work (plus the staff needed to fix it).

3. API‑ready PMS? That’s an AI invitation

Tab32 offers open APIs like many other PMS providers. Translation: you don’t need to rip‑and‑replace to add an AI scrubber that reads notes, checks images, and pushes corrections straight back into the PMS queue.

If your platform exposes an API, the lift is lighter than you think.

Even automation needs a co-pilot

Most practices already have scrubbers, clearinghouse edits, and even built-in PMS checks.
But those systems often stop at the same blind spots: mismatched codes, missing narratives, and small demographic errors.

The cost?
A slower revenue cycle. Inflated staffing. And growth capital stuck in A/R instead of opening new sites or expanding services.

And that’s exactly where most systems fall short:
They can flag a blank box, but not a bad assumption. They’ll catch a missing field, not a narrative that contradicts the x-ray.

That’s why more practices are turning to smarter tools, ones that don’t just check fields, but read, think, and correct like a trained human would. At scale.

How Auxee is closing this gap

When practices plug Auxee into their PMS, they typically see:

_{Source: multi‑site dental & primary‑care clients, 2024-2025}

Curious what that looks like in real life?
A group handling 5,000+ visits a day used Auxee to cut scrubbing costs by 90% and drop denials from 16% to 4%—no new hires, no system swap.

If you are not ready to share your data, we can still walk you through what AI-powered claim scrubbing looks like live, in under 15 minutes.

Next Tuesday, I'll cover why top healthcare CFOs now list AI as their #1 RCM budget line—and the three metrics they use to prove ROI.

See you next Tuesday,
Dino Gane-Palmer