By Kacie Geretz, Director of Growth Enablement
April 2, 2026
Medical coders are the hardest role for healthcare group leaders to fill, with 34% citing it as their top recruiting challenge.
Autonomous medical coding separates volume growth from headcount by coding encounters and sending them straight to billing with no human intervention; encounters flagged for missing documentation, ambiguity, etc., are routed for manual coding.
Health systems using autonomous medical coding have eliminated mandatory overtime, enabled coders to transition into higher-complexity roles, and reduced DNFB, turning a workforce constraint into a revenue cycle advantage.
Revenue cycle teams are stretched thin, and the medical coder shortage isn't improving. Hiring takes months, even if you manage to find qualified candidates. Meanwhile, patient volume and payer complexity keep climbing. Scaling coding operations doesn't have to wait on a perfect hire. Autonomous medical coding handles routine encounters so your skilled coders can focus on complex work requiring human judgment.
The numbers tell a difficult story. When the Medical Group Management Association (MGMA) polled group leaders about their hardest roles to fill, medical coders topped the list at 34% (1). That's not surprising given the specialized training required, but it creates real operational problems when you're trying to maintain existing capacity, let alone grow.
The U.S. Bureau of Labor Statistics projects roughly 12,300 new medical records specialist positions opening between 2021 and 2031 — just 7% growth for a field where demand is outpacing supply by a widening margin (2).
There's another factor at play: coders burn out. They're processing more than 50 charts a day while navigating increasingly complex payer requirements, chasing documentation gaps, and working denial appeals. There's no time for the careful analysis that drew many people to the field in the first place.
Those pressures show up directly in financial performance:
In most industries, the answer is to hire. In medical coding right now, that's becoming less viable. Even when you find candidates, onboarding takes time, and a new coder typically needs weeks or months to reach full productivity. Autonomous medical coding can get you there faster.
Computer-assisted coding (CAC) tools still need human review for every case. Autonomous medical coding works differently: the solution assigns codes to patient encounters and sends them straight to billing without human intervention, while your coding team focuses on encounters that the solution was unable to code due to factors like missing or ambiguous clinical documentation.
KLAS Research reports that organizations using autonomous medical coding see clear return on investment through higher efficiency and reduced staff strain (3). Coding automation has become the top AI use case in healthcare, according to their research. Organizations aren't just maintaining operations — they're improving performance metrics while reducing the burden on existing staff.
Read the KLAS Segment Insight Report on Autonomous Coding
The technology performs especially well in high-volume, relatively standardized specialties. Radiology and emergency departments have seen particularly strong results because coding patterns are consistent enough for the solution to handle reliably. Some organizations have also deployed autonomous medical coding successfully in pathology, primary care, and surgery (3).
The practical difference is that volume growth no longer requires proportional headcount growth. You can handle 20% more encounters without hiring 20% more coders. The solution processes routine cases, and your team focuses on the complex scenarios where their expertise matters most.
Recruiting costs, training time, benefits, and turnover risk make every new hire a significant investment with uncertain returns. Autonomous medical coding changes that calculation across three dimensions:
Direct cost avoidance. You're not paying recruiting fees, signing bonuses, or benefits for additional headcount. The technology requires an investment, but it scales more efficiently than adding headcount.
Faster cash flow. When coding doesn't require waiting for an available human coder, claims move faster, A/R days shrink, and reimbursement reaches your organization sooner.
Fewer coding-related denials. Inconsistent coding and documentation gaps that slip through when teams are overwhelmed drive denial volume. Transparent autonomous medical coding solutions show the reasoning behind every code assignment, making it easier to catch issues before claims go out.
One concern among leaders is whether automation will make coding staff feel threatened or devalued. Organizations find the opposite. When autonomous medical coding handles the more routine, high-volume encounters, coders can shift toward the clinical analysis that draws on their expertise.
Career development follows naturally. Coders can specialize in complex service lines where automation is less effective. They can move into quality assurance roles overseeing the solution's performance and catching edge cases.
Read the Genesis Healthcare System Case Study for a real-world example of how autonomous coding alleviated hiring pressure and improved coding team job satisfaction.
Not all autonomous medical coding solutions deliver the same results. Based on KLAS research and industry feedback, four factors separate effective platforms from underperforming ones (3):
Prioritize transparency. You need to see how the solution reached its code assignments. Systems that deliver codes without showing their work create audit problems and make it hard to spot errors before claims go out. Choose platforms that link every code to the specific clinical documentation supporting it.
Require strong implementation support. KLAS found that vendor support and training directly affect outcomes. Vendors who stay engaged through implementation get better results.
Start with high-volume specialties. Begin in radiology or emergency medicine, where autonomous medical coding performs most reliably. Prove ROI there before expanding to more complex service lines.
Evaluate your vendor's trajectory. Ask about development roadmaps and how they incorporate customer feedback. The technology is advancing quickly, and you want a partner building forward.
Many AI-based healthcare solutions operate as black boxes, delivering outputs without explaining the reasoning. Nym's autonomous medical coding engine is built on the opposite principle. Powered by Nym's proprietary Clinical Language Understanding (CLU) technology, it links every code assignment to the specific clinical evidence in the patient record and the coding guidelines that support it.
That transparency accelerates payer appeal cycles, supports coder education, and builds the audit trail that gives revenue integrity teams confidence. At Inova, Nym's engine reduced annual ED coding costs by more than $1.3M+, cut the weekly revenue sitting in DNFB by 50%, and eliminated mandatory overtime, while enabling four coders to advance into higher-complexity roles.
See how Nym's autonomous medical coding engine can help your health system scale without adding headcount. Request a demo to learn more.
How does autonomous medical coding help health systems scale without adding staff?
Autonomous medical coding handles high-volume, routine encounters end-to-end, so human coders can focus on complex work requiring clinical judgment. The solution runs continuously with no breaks, sick days, or turnover risk, so health systems process more volume with the same team.
How long does it take for autonomous medical coding to scale compared to a new hire?
A new coder typically needs around six months to reach full productivity after recruiting. Autonomous medical coding solutions can begin processing cases within weeks of deployment once configured to your documentation patterns. Capacity is immediate rather than gradual, and there's no turnover risk once the system is operational.
Can autonomous medical coding maintain accuracy as patient volume increases?
Yes. Autonomous medical coding solutions maintain consistent accuracy regardless of volume because they don't experience fatigue or cognitive overload. The systems improve as they process more cases and incorporate corrections. Organizations report that accuracy remains stable or improves as volume scales, particularly in high-volume specialties.
1. Medical Group Management Association (23 March 2023). Bottom line impacts from revenue cycle staffing challenges. MGMA Stat. Retrieved February 12, 2026, from https://www.mgma.com/mgma-stats/bottom-line-impacts-from-revenue-cycle-staffing-challenges
2. Bureau of Labor Statistics, U.S. Department of Labor (28 August 2025). Medical Record Specialists. Occupational Outlook Handbook. Retrieved February 12, 2026, from https://www.bls.gov/ooh/healthcare/medical-records-and-health-information-technicians.htm
3. Blauer, T. (26 August 2025). From Hype to Reality: What Healthcare Leaders Should Know About Autonomous Coding Solutions. KLAS Research. Retrieved February 12, 2026, from https://engage.klasresearch.com/blog/from-hype-to-reality-what-healthcare-leaders-should-know-about-autonomous-coding-solutions/8341/
Kacie Geretz, RHIA, CPMA, CPC, CCA is the Director of Growth Enablement at Nym, where she aligns Nym’s product roadmap with the evolving needs of health system partners and serves as the externally-facing expert on Nym’s autonomous medical coding engine. A graduate of The Ohio State University’s Health Information Management program, Kacie brings deep expertise across the revenue cycle—having led revenue integrity programs, built managed care contracting and credentialing infrastructure, and driven denials and A/R process improvement initiatives. She is passionate about advancing healthcare automation and regularly shares insights on coding innovation and RCM transformation.
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