A revenue cycle report published August 3, 2026 makes an uncomfortable claim about the AI tools coding and billing teams have spent the last two years adopting: automating a broken process just produces broken results faster. The report, from RCM analytics vendor MedEvolve, finds that nearly two-thirds of healthcare providers now use AI somewhere in revenue cycle management, but that 65% to 85% of the human “touches” claims still require produce no financial outcome at all.
The efficiency trap in AI-driven medical coding
MedEvolve’s core argument is simple, and it applies directly to how coding departments evaluate AI coding tools: efficiency is not the same as effectiveness. A platform that codes and submits claims faster is not necessarily producing more clean, paid claims — it may simply be moving flawed work through the pipeline at higher speed. CEO David Henriksen put it bluntly: “efficiency isn’t the same as effectiveness.”
For an AI medical coding platform specifically, that distinction matters more than for almost any other RCM function. A coding engine can hit an impressive autonomous coding rate — the share of charts it codes without human review — while still generating codes that trigger denials, additional documentation requests, or manual rework downstream. The automation looks successful on a dashboard. The claim still doesn’t get paid on the first pass.
What the “touch tax” looks like in a coding department
MedEvolve frames the hidden cost as a “touch tax”: administrative work that consumes staff time without moving a claim closer to payment. Its case study is concrete — a $635 claim required six or seven touches before it was resolved, at an estimated $5 to $10 in labor per touch, compared with a clean claim that was paid after a single touch. Multiplied across a mid-size health system’s claim volume, that gap is where most of the real cost of “AI adoption” actually lives, and coding accuracy is frequently the reason a claim needs a second or third touch in the first place.
How coding errors drive avoidable touches
A code that doesn’t match the documentation, a missed modifier, or an incomplete HCC capture doesn’t usually stop a claim from going out the door. It stops the claim from getting paid the first time it arrives at the payer. Every one of those denials or requests for additional information becomes another touch — coder rework, biller follow-up, appeal preparation — that an automation-rate metric never sees, because the original claim was already counted as “coded.”
Why automation rate is the wrong metric for AI medical coding
Most vendor scorecards for AI coding tools lead with throughput: charts coded per hour, percentage of claims coded without human touch, turnaround time from documentation to code assignment. Those numbers describe how much work a system did. They say nothing about how much of that work actually resulted in a paid claim without additional intervention. A coding team can hit record automation rates in a quarter where denial rates and rework volume both climb, and the dashboard will still read as a win.
The MedEvolve report’s recommendation is to stop measuring AI success by task completion and start measuring it by financial outcome — and that reframing applies just as directly to coding accuracy as it does to the rest of the revenue cycle.
Metrics that actually measure AI coding value
Coding leaders evaluating or auditing an AI coding platform get a more honest picture from outcome-based metrics than from automation-volume metrics. A few worth tracking alongside — not instead of — autonomous coding rate:
- First-pass clean claim rate for AI-coded claims specifically, separated from human-coded claims, so accuracy gaps don’t hide inside a blended average.
- Denial rate attributable to coding errors, tracked back to the specific codes or modifiers involved, not just total denial volume.
- Touches-to-resolution per claim, which surfaces rework that a “percent automated” figure never captures.
- Coding accuracy against payer determination — how often the code the AI assigned survives payer adjudication unchanged.
- Total cost-to-collect for AI-coded claims versus the baseline, which is the number that actually reflects whether automation helped.
Building these metrics into vendor evaluation
None of these require new instrumentation most coding departments don’t already have access to in their practice management or clearinghouse reporting. The change is in what gets asked at vendor evaluation and quarterly review time: not “what’s the automation rate,” but “what’s the first-pass clean claim rate on the claims this system coded, and how does that compare with before.” A vendor confident in the accuracy of its coding, not just its speed, should have that answer ready.
What this means for coding teams evaluating AI platforms
The broader point of the MedEvolve report — that AI could make the revenue cycle crisis worse rather than better if organizations keep measuring the wrong thing — is a useful gut check for medical coding specifically. Faster coding is only valuable if it’s also accurate coding. A platform that trades a coder’s time for a biller’s rework hasn’t reduced the touch tax; it’s relocated it.
That’s the standard Medikode’s automated medical coding platform is built around: coding accuracy and first-pass clean claim performance as the metrics that matter, not autonomous coding volume as an end in itself.