The Silent Cracks Draining Your Revenue Cycle

Hi and happy Tuesday,

A revenue cycle leader at a 12-location group once said something that never left me:

“Our denial rates weren’t alarming… until we realized what should have been getting paid.”

On paper, their revenue cycle looked fine. Benchmarks were met, denial rates were average, and the billing partner wasn’t worried. But buried inside “stable” processes were tiny cracks: modifiers left off, outdated codes slipping through, documentation not quite matching payer rules.

Those small cracks were costing hundreds of thousands every year.

Stable ≠ Healthy

Claims payment is the top challenge practices face today. But the warning signs don’t always show up in obvious ways.

Here’s where even strong RCM teams lose ground:

• Silent payer changes – Policies update constantly, alerts get buried.

• Manual fatigue – Staff moving through 40+ claims an hour miss small details.

• Lagging feedback loops – By the time denials arrive, weeks have passed.

The issue isn’t effort. It’s friction and blind spots baked into the system.

From reactive to preemptive

Leading practices don’t wait for denials to tell them what went wrong. They check claims before submission.

They rely on tools that:

• Scan for high-risk issues across multiple payers

• Flag problems in seconds with plain-language explanations; and,

• Require no system integration or workflow change

Think of it as a quiet safety net, handling the repetitive checks staff can’t realistically keep up with.

What prevention looks like in numbers

One group that embraced this approach saw:

• $668K in projected annual savings

• 50% fewer denials

• 78% fewer post-submission chart corrections

Their Chief Compliance Officer called it “the most effective tool I’ve seen in a decade.”

Your playbook

If you’re not ready for a dedicated tool like Auxee, here are two simple ways to start applying AI today:

1. Create pre-submission checklists from denials or payer bulletins

• Spot repeated denial reasons

• Translate payer PDFs into usable checklists

Example prompt: Summarize the main documentation requirements from this payer bulletin about CPT 99214.

Caveat: Output depends on input. Models won’t know live payer changes unless you supply them.

2. Natural language review of anonymized claims

• Paste procedure, diagnosis codes, and charting notes

• Ask if documentation meets criteria

Example prompt: Review this claim to see if it meets requirements for reimbursement of CPT 99406 by a commercial payer.

Caveat: Best for training and awareness, not production. Models can hallucinate if prompts lack context.

From experiments to scale

AI prompts are great for experiments. But for reliable results, you need more than experiments.

That’s where Auxee comes in. We’ll run a 100-claim audit with your real data—no integration, no commitment. You’ll see flagged issues, fix suggestions, and a modeled ROI.

or calculate how much you can save:

Next week, I’ll show you how to slash verification time and errors using your current tools (no rip-and-replace).

See you next Tuesday,
Dino Gane-Palmer