How One Practice Found $450K in Revenue Hiding in Plain Sight

Hi and happy Tuesday,

So, a COO, an RCM manager, and an AI pilot walk into a dental group…

That’s not a joke - it’s how a regional healthcare group quietly tested AI-powered claim scrubbing, without changing any of their existing systems.

Here’s how they did it and how you can, too.

Step 1: Define a low-lift test

Start with a narrow scope. This group picked:

• One high-volume payer

• Five top CPT codes

• 100 recent claims

They pulled the data into a spreadsheet, removed PHI, and hit send to the AI solution provider. No software. No training. No integration.

Step 2: Let the AI do its job

The AI reviewed the claims and flagged issues like:

• Missing or incorrect modifiers

• Coding inconsistencies

• Obsolete documentation requirements

Each issue came with a fix. No “black box” magic, just line-item suggestions.

Step 3: Model the impact

By applying the pilot’s outcomes to real-world volumes (5,000 claims/month, $75 avg value), here’s what they projected:

• +10% clean claim rate = $450K/year in faster or recovered revenue

• 1 FTE reallocated = ~$90K/year saved

• DSO cut from 42 to 28 days = better cash flow

And all this before connecting the AI to their systems.

What you can learn from this

• Start small. Pilots don’t need buy-in from IT or ops. Prove value first.

• Spreadsheet-friendly AI > Full-blown installs. Look for tools that meet you where you are.

• Fixing claims before submission beats cleanup after denials. Every clean claim is a win.

If you're curious whether this kind of pilot could work at your practice, we're happy to help. Scrub 100 claims—no integration, no risk—and get a custom ROI report based on your real data.

👉 See how it works in under 2 minutes.

Next week, I’ll break down what even high-performing RCM teams often miss—and what the best are doing to fix it.

See you next Tuesday,
Dino Gane-Palmer