Plug-In AI That Can Scale in Your Revenue Cycle

Hi and Happy Tuesday.

Almost half of hospitals now use AI somewhere in the revenue cycle, and about three-quarters have some kind of automation in place. 

Yet denial rates still remain high. The tools are there, but they are not yet standard work.

That makes Q1 the right time to scale a small set of “mature” AI use cases that already work in other organizations.

Where things stand in January

So the question for Q1 is simple: 

If the tools exist, where can you “plug in” AI to clearly reduce rework and risk of denial?

1. Eligibility and benefits that actually run straight through

The gap: 94% electronic eligibility checks, yet dental providers alone spend $2.1B annually on verification.

Those numbers tell you many checks still bounce through portals, phone calls, and partial workflows instead of clean, straight-through transactions.

Q1 Target 1:

Move your top payers to straight-through processing with minimal manual touches

• Automate eligibility for the highest-volume plans

• Track and eliminate manual intervention points

2. Claim scrubbing and denial prediction for your top 5 payers

The opportunity: AI rules layers reduce denials by up to 30% for top payers.

Q1 Target 2:

Route all high-volume claims through AI validation before submission to the top 5 payers

In practical terms, that does not require reinventing your revenue cycle. It means:

• Focus on missing data and prior authorization mismatches

• Target coding combinations that repeatedly fail

3. AI-assisted coding in a few high-volume specialties

Proven results: Deep-learning models reduce coding errors and increase throughput in high-volume specialties.

Q1 Target 3:

Expand AI coding in 1-2 specialties (e.g., emergency medicine, radiology)

• Track coding-related denials before and after

• Measure coder productivity and turnaround time

The goal is a clear “before and after,” not a full coding overhaul.

4. Document AI for denials, EOBs, and medical records

Real impact: 250M transactions processed with 40% time reduction, 15K+ hours saved monthly, 99.5% accuracy.

Q1 Target 4:

Consolidate pilots into one document AI layer for denials, EOBs, and appeals

• Standardize data extraction from PDFs and scanned forms

• Integrate with worklists and analytics

In our next newsletter, we’ll give you a Q2 roadmap.

Dino Gane-Palmer