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What’s Working in RCM and Why AI Is Part of the Solution

What’s Working in RCM and Why AI Is Part of the Solution

Real-world AI is reducing denials and boosting revenue by stepping in at three critical moments: before, during, and after the visit.

Dino Gane-Palmer
July 22, 2025

Hi and happy Tuesday,

AI in healthcare gets headlines for robotics and radiology, but its most powerful and overlooked wins often happen far from the OR. They’re at the front desk, in the exam room, and inside the billing office. That’s where revenue either flows or leaks.

Here’s why those quieter moments matter:

U.S. providers forfeit $20 B+ a year to denials and avoidable write‑offs.
Front‑office turnover in ambulatory clinics tops 25 % in many states.
Payer rules tighten every quarter, while many RCM teams still rely on manual checks days or weeks after service.

AI won’t reinvent your revenue cycle overnight, but it excels at biting off the worst friction: catching coding errors, spotting missing documentation, and flagging denials early so you keep money that’s rightfully yours.

Before the visit: At the front desk

Here’s how AI tackles the biggest front‑desk risks before the visit even starts:

RCM pressure point	How AI helps	Why it matters
Entering correct patient info	Scans ID cards, auto-populates records	Fewer typos, ~1.6% fewer ID-related rejections
Checking coverage	Pings payers in real time	Catch inactive plans before the visit starts
Estimating patient cost	Interprets plan rules + visit notes	Reduces surprise bills and follow-up calls
Prior authorization needs	Flags with green/yellow/red indicator	Focuses staff on high-risk orders early

During the visit: Capturing clean data in real-time

Here’s how AI tackles common documentation and coding risks while the visit is happening:

RCM pressure point	How AI helps	Why it matters
Deciding if a test or procedure is justified (medical necessity)	Gives an on-screen prompt: required criteria present or missing	Avoids ordering something that will be denied later; protects reimbursement
Missed charges (services done but not entered)	Scans documentation and compares to typical charge list to flag omissions	Captures revenue that would be lost if an item is left off
Coding uncertainty (which CPT/ICD to pick)	Reads the note and suggests likely codes with rationale	Speeds coding, raises first-pass acceptance, cuts rework time
Drug choice vs patient’s coverage	Speeds coding, raises first-pass acceptance, cuts rework time	Reduces pharmacy call backs and delays; improves adherence

After the visit: Closing the financial loop

Here’s how AI helps address revenue and follow-up risks once the patient leaves:

RCM pressure point	How AI helps	Why it matters
Claim building	Prioritizes claims with risk scores	Staff focus on claims most likely to be denied
Statement strategy	Uses propensity-to-pay models	Reduces days in A/R and improves collections
Follow-up work	Segments accounts by recovery potential	Increases ROI on staff effort
Performance tracking	Live dashboard with key metrics	Detect trends early and adjust in real time

What’s working right now in the field

Up to 50 % fewer denials & 2× faster approvals (according to Experian + CAQH)
85 % denial drop at a 5 k‑visit/day dental group after deploying Auxee claim scrubbing.
Hundreds of staff hours shifted from manual checks to patient care.

High‑ROI use‑cases tried and tested with our clients:

Catch data and coding issues in under 60 seconds
Auto-verify insurance + flag missing authorizations
Trigger corrections within their current PM system
Turn mistakes into coaching opportunities—no extra training required

Plus, what I covered last week, AI even helps uncover hidden issues in your 835 files.

Where you can start if resources are right

Clean the front end: Automate eligibility and demographic capture first. Error prevention beats error correction.
Target top denial categories: Use recent remittance (835) data to find the three most costly denial codes and apply rules or AI prompts upstream.
Add denial-risk scoring before submission: Even a simple risk rank helps teams triage limited staff time.
Measure cash velocity monthly: Track days in AR by payer group and intervention date to confirm ROI.

Want a closer look at how AI fits into real workflows?

With Auxee, teams are reducing denials, speeding up payments, and finally getting ahead of payer changes, without changing their systems or retraining their staff.

See how Auxee works, read the case study or watch a 96-second demo to learn what’s working in the field.

You can also book a quick demo to get your questions answered, or take our RCM blindspot assessment to see where your practice is losing and how to plug the leaks.

Next week, we’ll expose why the slickest automations still miss the mark and how to spot when your AI is little more than fancy autofill.

See you next Tuesday,
Dino Gane-Palmer

That's it for today!

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Dino Gane-Palmer
[email protected]

About the Author

Dino Gane-Palmer is the founder of Auxee and CEO of PreScouter, an Inc. 5000–recognized innovation consultancy that helps Fortune 500 companies and global organizations capitalize on new markets and emerging technologies. He launched PreScouter while earning his MBA at Kellogg and later founded Auxee to help teams use AI to tackle complex, research-heavy workflows. His work has supported decisions at some of the world’s leading healthcare, manufacturing, and consumer brands. Dino is also the author of the best-selling book Do More With Less: The AI Playbook, a practical guide to applying AI where it matters most.