State of the Union: Why Medical Coding Automation Is Broken—And What Comes Next
- Aubrey Ibele
- 1 day ago
- 3 min read
Legacy Revenue Cycle Workflows Require AI Infusion
In today’s ambulatory environments, most practices still rely on EMR-integrated tools like level-of-service calculators and native documentation modules. While these may have once helped ease provider burden, they’ve become part of the problem—adding complexity, increasing click fatigue, and ultimately slowing down revenue cycle operations.
Meanwhile, Computer-Assisted Coding (CAC)—a once promising solution—has failed to evolve. Originally designed for inpatient environments, CAC’s rule-based model simply can't keep up with the pace, volume, and variability of outpatient and specialty workflows. The need for manual review and limited flexibility means CAC is now more of a bottleneck than a benefit.
What’s Being Sold as “AI” Is Mostly Smoke and Mirrors
Many of the biggest names in medical coding automation are selling smoke, not solutions. Beneath the flashy interfaces and buzzwords, you’ll often find:
Rules engines disguised as AI
Labor-intensive coding teams are doing the real work
“Autonomous” platforms that can’t deploy across specialties and purposes built for Emergency Department and Radiology
If your platform relies on static rules, lacks the ability to train on audited coding data, and can’t deliver specialty-specific direct-to-bill outputs —it’s not artificial intelligence. It’s automation theater.
The Fallout: Practices Are Losing Trust in Automation
After being burned by immature tech, many RCM leaders are hesitant to invest in a new solution—especially one with consistently broken promises and failed implementation timelines. Their concerns are valid:
Can it scale across my specialties?
Does it automate E/M and Surgical Procedures?
Will it increase accuracy or increase denials?
Is this vendor really using AI—or just another rules engine?
This widespread fatigue and skepticism have caused stutter-stepping in decision making, even as the complexity of coding and shortage of coders continue to worsen.
What Real AI Looks Like: The Twin Cities Orthopedics Case Study
When Twin Cities Orthopedics (TCO)—the largest orthopedic practice in the U.S.—found themselves buried in a 21,000-case coding backlog, they knew incremental improvements weren’t enough. They needed a transformational change.
By implementing aiH.Automate™, TCO achieved:
A reduction in surgical coding turnaround from 5 days to just 1
A 50-60% increase in coder productivity
Up to 95% accuracy on direct-to-bill encounters
A 3-day reduction in charge lag
Backlog cut from 10.5 days to 1.5 days
This isn’t future-state. This is real-world AI, solving real-world problems—today.
You Can’t Afford to Wait
Here’s the hard truth: if you're still relying on legacy systems or fake AI, you're already falling behind. While many practices hesitate, payers have no such reservations. They’re using AI to auto-deny claims at scale.
To compete—and survive—you need:
Technology that can scale across specialties and encounter types
Proven accuracy and speed
True automation that eliminates, not masks, human labor
Implementation support that meets timelines and deliverables
The future of coding is AI-driven, autonomous, and direct-to-bill. Anything less is just a delay tactic.
What to Do Now
If you're evaluating medical coding automation, ask the hard questions:
Is this real AI or just rules dressed up as intelligence?
Can it scale across specialties—now?
Does it support E/M and Surgical encounters?
Do I get direct-to-bill capability with audit trails and accuracy thresholds?
What results have other organizations seen?
The business case is simple: automation isn’t a nice-to-have anymore—it’s critical infrastructure.
Medical coding is the heartbeat of your revenue cycle. If it’s inefficient, your entire organization suffers. The good news? You don’t have to settle for half-baked solutions.
aiHealth is leading the charge into a new era of coding—where AI actually works, coders thrive, and revenue flows faster.
Let’s talk about what real AI can do for you.
