The original version of this blog appears on Libman Education’s website here and was published on June 30, 2026.
A recent article in The Atlantic about Tesla’s self driving technology included a quote that perfectly captures one of the emerging challenges of autonomous medical coding:
“Full Self-Driving works almost all of the time… And that’s the problem: We are asking humans to supervise systems designed to make supervision feel pointless. A machine that constantly fails keeps you sharp. A machine that works perfectly needs no oversight. But a machine that works almost perfectly? That’s where the danger lies.”
Researchers have found this isn’t simply human complacency. Highly reliable automation naturally changes human behavior. As systems perform correctly over and over again, people become less vigilant because their experience tells them the technology has earned their trust. While the article was about autonomous vehicles, the same question may be one of the most important conversations healthcare needs to have about automated medical coding.
Much of the discussion around AI has focused on familiar questions. Can we trust it? Can it improve productivity? Reduce backlogs? Increase coding consistency? Help organizations do more with fewer resources? Those are important questions. But I think we’re overlooking another one. What happens when AI consistently meets expectations, and we begin trusting it so much that we stop exercising the very expertise required to oversee it?
Medical coders and revenue integrity professionals have spent years developing the ability to interpret documentation, apply complex coding guidelines, recognize clinical nuance, and stay current with an ever changing regulatory landscape. Those skills were built through repetition. Every chart, every coding update, every payer denial, and every difficult case strengthened their expertise.
As AI takes on more of that work, we have to ask ourselves a different question. How do we use AI to elevate human expertise without losing the habits that created that expertise?
Healthcare leaders are already recognizing this shift. Mayo Clinic recently redesigned its leadership competency framework for the AI era and found that the competencies becoming most important weren’t technical. They were critical thinking, forward thinking, and leading effectively alongside AI.
Medical coding is facing a similar inflection point. For decades, the value of coding professionals has largely been measured by what they personally produced. AI doesn’t diminish that expertise. It changes how organizations can leverage it. Instead of spending the majority of their time assigning codes, coding experts have an opportunity to spend more of their time improving the systems that assign them.
The question is no longer simply whether coding professionals know how to code. It is whether they can apply that expertise in new ways by questioning AI recommendations, recognizing patterns, connecting coding decisions to downstream revenue cycle performance, and continuously improving the technology supporting their work.
Real oversight means asking more strategic questions.
Those questions cannot be answered by auditing a handful of encounters. They require looking across thousands of encounters, identifying patterns, understanding trends, and connecting coding decisions to denials, reimbursement, compliance, documentation quality, and operational performance. They require professionals who know not only when AI is wrong, but why.
Historically, coding expertise was measured by productivity and accuracy.
Tomorrow’s coding experts may be valued not only for the volume of charts they personally code, but for the intelligence they bring to the system.
To see how HIM leaders at Intermountain Health and Ohio State University Physicians reimagined coding roles after implementing Nym's autonomous medical coding engine, check out the Peer Insights Playbook: The People Side of Autonomous Coding.
Organizations also need to stop thinking about AI as software they purchased and start thinking about it as a product they continuously refine alongside their technology partner. Every successful product evolves. Teams monitor performance, learn from user behavior, identify weaknesses, and release improvements. Automated medical coding should be no different.
That requires a different mindset and, perhaps more importantly, a different investment in people. Organizations will need coding professionals who continue sharpening their own expertise while helping AI sharpen its own. They will need education that extends beyond coding updates to include data analysis, pattern recognition, and AI oversight. The organizations that succeed won’t simply trust AI. They’ll never stop questioning it.