Autonomous RCM Is Here, But Most Health Systems Aren’t Ready for It
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Revenue cycle has been automating for years, but the tone changed in 2025. It is no longer just “help me click faster.” It is “take the work, run the workflow, come back with a finished outcome.” That is what people mean when they say autonomous RCM: agentic systems that can touch eligibility, authorizations, coding support, claim edits, denial follow-ups, and patient billing in one continuous loop.
You can see the market signal in where platforms are placing bets. Waystar’s agreement to acquire Iodine Software is a pretty loud one, because it ties documentation integrity and reimbursement enablement closer to the same operational spine as claims and denial management.
So yes, the tooling is here. The harder question is whether the org is built to supervise it.
The First Time RCM Actually Feels Like A System
When autonomous RCM works, it does something traditional RPA never quite nailed. It handles variability. Not perfectly, but meaningfully. Instead of breaking the moment a payer portal changes a button label, the system can re-orient, re-check policy, pull context from the chart, and choose the next best action, sometimes even drafting the appeal narrative with citations back to documentation.
It also pushes RCM toward standardization without forcing every team into the same template. Agentic workflows can encode “how we do things here” as a living playbook, and that matters because a lot of revenue leakage is not complex. It is inconsistent. Missing modifiers. Unclear documentation. Follow-ups that do not happen. Work queues that turn into graveyards.
And from an operational lens, these systems are showing up because the pressure is real. Denials and rework are not a rounding error; they are a staffing model. Many RCM leaders have been staring at rising denial volume and shrinking tolerance for delays, which is why “end-to-end automation” keeps resurfacing as a priority in revenue cycle conversations.
Faster Claims Can Still Be Wrong Claims
Autonomy changes the risk profile. With classic automation, failures were loud. A bot crashed. A queue stalled. People noticed. With agentic RCM, failures can be quiet, and worse, they can look confident.
Here are the three failure modes I keep seeing in how health systems talk about this internally.
First, documentation to billing becomes a shorter path, which is great until it is not. If your clinical documentation integrity and coding support are increasingly AI-mediated, you need auditability at the decision level: what evidence was used, what guideline was applied, what was ignored, and why. The Waystar–Iodine logic is basically a bet that CDI can be made more real-time and operational, but that also means mistakes can scale faster if governance is weak.
Second, policy drift becomes a real operational hazard. Payer rules change. Local coverage determinations shift. Contract language varies by plan. If an agent is operating off stale assumptions, it does not fail like software. It fails like a person who is sure they remember the policy.
Third, autonomy increases your “blast radius.” One misconfigured workflow does not just misfile a claim. It can systematically bias coding choices, appeal language, or authorization pathways across thousands of encounters before anyone realizes the pattern. And if you are relying on surface metrics like “clean claim rate,” you can accidentally hide compliance debt until an audit forces the reveal.
My Perspective: Readiness Looks Boring, And That’s The Point
I do not think the question is “should we use autonomous RCM.” The question is, “do we have the operating discipline to supervise it.”
If I were advising a health system, I would treat autonomous RCM like a clinical rollout, not an IT feature. Start narrow. Select workflows where the downside is reversible, such as status checks, document assembly, or drafting (not sending) appeals. Require human sign-off on the first thousands of actions, not the first week. Measure divergence, not just throughput: where did the agent disagree with human staff, and why?
Then build the boring foundations. Versioned policies. Role-based controls. Immutable logs. A failure playbook. A rollback button that is real, not theoretical. Gartner has been warning for a while that AI risk and governance cannot be bolted on after adoption, and healthcare is the industry where “after” is usually too late.
Autonomous RCM will absolutely become normal. But the winners will not be the systems that automate the most. They will be the systems that can prove, to themselves and to auditors, exactly how the automation behaved.
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Prompt of the Day: Stress-Test Your RCM Autopilot
Role: Act as an RCM safety and compliance reviewer.
Context: We are piloting an agent that handles eligibility checks, claim edits, and first-pass denial responses.
Task: Identify the top 10 failure modes that could create audit risk or compliance exposure.
For each failure mode, provide:
What it looks like in operations
How to detect it early (signals, dashboards, sampling)
The control to mitigate it (human review, policy check, logging, gating)
End with a 90-day rollout plan that starts safe and expands scope only when controls prove stable.