How to Expose AI Blindspots With These 4 Qs

Hi and happy Tuesday,

At 2:13 a.m., the COO of a 30-location specialty group replied to a short demo of an AI claim scrubbing tool.

“I don’t agree with the model... It feels too doctor-centric. But if your system is adaptable and scalable, I’ll give you 15 minutes.”

No pleasantries. No fluff. Just a direct callout of something that’s become a pattern: most “AI” tools still ask too much of already-stretched teams—and often solve the wrong problem.

That late-night message sparked a candid exchange that conveyed more than most webinars and whitepapers combined. And it left us with four questions every healthcare exec should be asking long before the pilot phase.

What every RCM leader should ask before saying yes to AI

Don’t ask product questions. First, ask operational ones about fit, flow, and what actually moves the needle.

1. Is it software, or is it support?

Most tools want you to bend your workflow around their features. But most teams don’t want another platform. They want silent support - something that works inside what they already use. 

As that COO put it:

“Are you replacing our process or reinforcing it?”

In today’s environment, adaptability is key.

2. Does it address issues or just flag them?

Surface-level AI is everywhere. But pointing out a missed modifier isn’t the same as helping the team fix it, explain it, and learn from it.

The real ask: Can your staff act on what the system tells them, without bouncing between platforms, ticket queues, or third-party support?

3. Will it preserve knowledge, or create new training gaps?

With RCM turnover averaging 30%+ annually (Becker’s, 2024), teams are constantly rebuilding internal know-how. Good AI doesn’t just assist—it quietly teaches as it works.

If your team gets smarter just by using the tool, you’re investing in institutional memory, not just tech.

4. Has it actually delivered results?

This isn’t theoretical. One multi-site group that asked these same questions found the right fit and saw:

• $668K in annual savings

• Denials down from 16% to 7%

• Chart corrections cut by 40%+

That’s not from a revamp. It was from AI running in the background, reviewing every chart before it became a problem.

Why these questions matter more now than ever

It’s not just about smarter software. It’s about systems thinking in a new era of reimbursement.

Over the past year, payers have quietly introduced tighter logic checks, expanded documentation requirements, and increased scrutiny on even routine procedures. But most teams haven’t updated their workflows to match.

What’s changing:

• Payer rules are evolving faster than staff training cycles.
  Many denials now stem from subtle updates buried in provider bulletins - things your team didn’t miss because they were careless, but because the information never reached them in time.
  

• Generic AI tools don’t interpret narrative context.
  Tools not trained on medical nuance miss key decision points hidden in free-text notes. That’s where true claim risk lives and why most off-the-shelf automation fails to reduce denials in any meaningful way.
  

• Internal QA teams are reaching their bandwidth limits.
  Even with talented people, most review processes can’t scale fast enough to keep up with increasing volume, let alone the complexity. It's not about working harder, but more about creating leverage.

📊 Case in point:
A 2024 MGMA report found that at mid-sized group practices, up to 32% of all RCM labor hours are spent on reworking claims.

And the top-cited reason? 

Upstream documentation errors were missed during the initial review.

The best AI isn’t just smart, it’s invisible.

The most effective solutions don’t announce themselves. They don’t ask your team to do more. They quietly lift the burden of repetitive checks, freeing up time and attention for edge cases and exceptions.

So, if your team is still relying on people to manually interpret notes, track down codes, and piece together payer rules, the real risk isn’t just inefficiency, it’s fragility.

One staff departure, one policy change, or one missed update, and the whole system wobbles.

Next week, I’ll reveal the operational leak no one’s budgeting for, and how smarter scrubbing is changing that.

See you next Tuesday,
Dino Gane-Palmer