RCM Reboot: How to Fix Hidden Inefficiencies in Your Claims Process

Hi and happy Tuesday,

A revenue cycle leader at a 12-location medical group once told us something that stuck:

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

On paper, their RCM was solid. The team was experienced, denial rates were within national benchmarks, and their billing partner hadn’t raised any red flags. But a closer look told a different story. Hidden in routine workflows were tiny, systematic errors—modifiers missing, outdated codes slipping through, documentation falling just short of payer rules.

And together, those “small misses” were costing them hundreds of thousands each year.

Here, we break down what even high-performing RCM teams often miss—and what the best are doing to fix it.

Don’t confuse “stable” with “healthy”

A recent MGMA poll found that claims payment is the #1 revenue cycle challenge facing practices today.

But here’s the nuance: the pain isn’t always obvious. Even “stable” RCM processes can quietly leak revenue due to:

• Silent payer changes – Rules update frequently, but alerts often get lost in the shuffle.
  

• Manual fatigue – Teams expected to review 40+ claims/hour can’t catch everything.
  

• Lagging feedback loops – Denials show up weeks later, making it hard to trace and teach.

The issue is not a lack of effort, but rather structural blind spots and friction.

Shift from reactive to preemptive

Instead of learning from denials, top practices are learning before submission.

They’re using tools that:

• Scan for high-risk issues across multiple payers
  

• Flag claims in seconds, with clear explanations
  

• Require zero integration or system change

Think of it as a silent partner—doing the boring checks staff don’t have time for.

Turn denial prevention into a habit

One group that took this approach saw:

• $668K in projected annual savings

• 50% reduction in denials

• 78% fewer chart corrections post-submission

Their Chief Compliance Officer called it the most effective tool she’d seen in 10 years.

Try this on your own (right now)

If you’re not ready for a dedicated tool like Auxee yet, here are 2 low-lift ways to start applying AI to your process today:

Method #1: Use tools like ChatGPT or Claude to create pre-submission checklists from denials or payer bulletins

Does it work? Yes. These models are excellent at:

• Spotting repeated denial reasons (e.g. modifier missing, documentation incomplete)

• Summarizing dense language (payer PDFs, policy updates)

• Translating technical policy language into clear checklist items

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

Caveats:

• It’s only as good as the input. If you feed it vague denials like “services not covered,” it may generalize too much.
  

• It won’t know the latest payer-specific changes unless you provide them in the prompt (since it doesn't have live access to payer portals).

Still, for internal training and building awareness? It’s a strong tool.

Method #2: Natural language review of anonymized claims

Does it work? Yes, with realistic expectations. If you paste in:

• The CPT/procedure

• Diagnosis codes

• Key charting notes … and ask whether this meets payer criteria, the model can:

• Call out missing elements (e.g. no chief complaint listed, modifier mismatch)

• Highlight vague documentation

• Suggest what to double-check based on CPT rules it was trained on

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

Caveats:

• It can’t guarantee payer acceptance (no live access, no claims database)

• It may hallucinate if the prompt lacks context or specificity

• It’s best used with known payer policies provided in the same prompt

These examples are great for training and quick experiments, but they’re not reliable or scalable for production. That’s where a dedicated tool like Auxee comes in.

If you want to go beyond experiments and see what AI-powered scrubbing looks like in action, we’ll run a 100-claim audit using your real data - no integration, no commitment. You’ll get flagged issues, fix suggestions, and a modeled ROI based on your current workflows.

👉 See how it works in under 2 minutes.

Next week, I’ll show you how one healthcare group automated their claims process without a vendor and what it reveals about the future of denial management.

See you next Tuesday,
Dino Gane-Palmer