The Coders Are Leaving. The AI Isn’t Ready.

The Coder Retirement Wave 

Hospitals planned for AI to absorb a large share of coding work, so many never prepared for the people who do it today walking out the door. The AI hasn't arrived at the scale and accuracy that was promised, and the retirements have. 

For several years, the working assumption in revenue cycle has been that AI would take over much of the coding workload, so the thinning of the coding workforce was a problem technology could solve. That assumption is why so many hospitals haven't sounded an alarm. It is also why the retirements now underway are leaving a bigger impact than anyone planned for. 

Two of the country's largest systems are already living this 

At this year's AHIMA conference, leaders from Cleveland Clinic and Mayo Clinic spoke candidly about the middle revenue cycle, as relayed in conference recaps. Cleveland Clinic's Nick Judd said roughly half of its coding workforce is retirement eligible. Mayo Clinic's Stacey Bui described a single Friday when retirements took out close to 10% of one department. 

These are large, well-resourced organizations, and what they describe tends to reach the rest of the industry a couple of years later. The question that every hospital should be asking is how many of your own coders are within a few years of retirement, and what happens to throughput and DNFB, as well as backend implications of denials, if several leave in the same quarter. 

The timing matters because the market is already short of experienced coders. A June 2026 TechTarget report put the national coder shortage at up to 30%, citing figures from the AMA. Retirements are landing on a bench that was already thin. 

AI hasn't taken the work, and it is pushing some people out 

AI-assisted coding is real and useful, especially on high-volume, lower-complexity encounters. What remains absent is the sweeping replacement of staff resources that many forecasts expected. 

Complex inpatient records, specialty services and anything that needs clinical judgment still need and will continue to need, at least for a while, a trained person, while most organizations are still working through how to govern, audit and trust automated output. 

But there is a secondary effect that is receiving less attention, the loss of the seasoned coding professional. Many of the coders that have been doing the job for years learned by shoulder-to-shoulder mentorship and utilizing coding books while building their reputation on accuracy and guideline fundamentals, before moving to computer-assisted coding years later. With CAC, an algorithm suggests a code, but the coder still reviews the chart, understands the suggestion and validates that it is correct, so the critical thinking stays with the coder. AI can change that. It assigns the code, and the coder role shifts toward managing exceptions. That is a bigger shift than it sounds, and for some experienced coders it may be one factor among several in deciding when to retire. 

There is an irony in this. Hospitals expected AI to help with the retirement problem, but it may be making it worse, because some experienced coders would rather leave than work in a different way. 

Coding is a craft, and craft takes time 

The art of coding is reading a record the way a clinician wrote it, noticing what is missing, knowing when to query, and defending a code under audit. That judgment is built one decision at a time, over years. When the job shifts toward validating what a system proposes, there are fewer of those reps. A reviewer who has never had to make the call from scratch has a more difficult time spotting the wrong answer that looks right. 

This conundrum makes the pipeline thinner on both ends: experienced coders are leaving, and the people replacing them are likely to need more time to reach the same depth, because the work that used to teach the craft is increasingly done for them. 

A practical path forward 

None of this calls for panic, but it does call for a plan that doesn't solely depend on AI closing the gap on its own timeline. Start by mapping retirement exposure by team, by credential and specialty, so you know where even a handful of departures would hurt most. Then capture what your veteran coders know before they go: query patterns, payer quirks and the reasoning behind tough calls, ideally through pairing them with those remaining and providing documented examples rather than a binder nobody opens. 

Build training that keeps new coders making decisions, not only approving them, with coding first and review second, backed by regular audits against regulatory standards. Be reasonable about productivity and give new coders a longer runway than you would have a few years ago, because quality is built before speed. Then plan your capacity as a blend. Core in-house expertise, flexible and managed support from long-term partners, and AI tools each carry part of the load, and the mix should be chosen before a retirement wave forces it. 

Think of this dual approach as insurance. No one likes paying for it, but it reduces risk in a substantial way. Organizations that bring in the right partners early, while there is time to onboard them well, can absorb a wave of retirements and implement AI in a trusted way without losing ground. 

The organizations that come through this well will be the ones that treat coding expertise as something to protect and as an investment that pays dividends, not something technology can quietly replace.

Need help protecting your coding capacity or revisiting your model as retirements ramp up? Contact Us!

About the Author

Autumn Reiter, MBA, BSN, RN, CCDS, CCDS-O, CDIP, CCS

Chief Strategy and Solutions Officer at UASI

A registered nurse with 20 years of experience and a BSN from Virginia Commonwealth University, Autumn began her career as a CDI specialist and has spent more than a decade building and leading CDI programs at scale. At CorroHealth, she advanced from Director of Clinical Documentation Improvement to Vice President of CDI Services and Tech Enablement and, most recently, Senior Vice President of Payment Integrity and Provider Risk Strategies. Now Chief Strategy and Solutions Officer at UASI, she pairs frontline CDI insight with the business perspective of an MBA from Western Governors University, helping healthcare organizations strengthen documentation accuracy, protect revenue integrity, and connect clinical care to financial performance.