By Kacie Geretz, Director of Growth Enablement
June 15, 2026
Autonomous medical coding reduces audit risk by applying consistent coding logic to every encounter, eliminating the variability that payers and auditors look for in denial patterns
Health systems using Nym have seen coding-related denial rates drop by 97% and achieved accuracy rates over 95%, with complete audit trails generated for every coded encounter
Autonomous coding engines can surface documentation quality gaps at scale, giving CDI teams data to address compliance exposure before it becomes a problem
Audit risk in the revenue cycle builds quietly. Inconsistent code assignment, unaddressed documentation gaps, and denial patterns that signal compliance exposure accumulate long before a formal audit surfaces them. For most health systems, the response comes after the fact: the claim is denied, the appeal filed, the audit letter is received.
Autonomous medical coding moves that intervention earlier. By applying consistent coding logic across every encounter and surfacing documentation issues before they reach a payer, it turns audit risk management from a reactive function into part of the standard coding workflow.
Manual coding introduces variability that is structural, not a reflection of individual coder skill. Under high volume and time pressure, coding decisions vary across coders, shifts, and documentation quality. That variability is what auditors look for: patterns of overcoding in a specific DRG, inconsistent E/M criteria application, code assignments that don't match the clinical documentation.
The scale of the problem is documented. The AMA estimated in 2024 that 12% of medical claims are submitted with inaccurate codes, with error rates reaching 40% in some specialties (1). Health systems under volume pressure face a compounding dynamic: more encounters processed under greater time pressure, which increases the likelihood of the inconsistency auditors find.
Manual coding and computer-assisted coding workflows that still depend on human review for every chart cannot eliminate this variability at scale. Autonomous medical coding can. The same coding logic, continuously updated for annual ICD/CPT changes and quarterly NCCI updates, is applied to every encounter without exception.
Health systems evaluating AI-powered coding solutions will encounter a range of claims about automation rates and accuracy. The distinctions matter specifically for audit risk.
Computer-assisted coding tools suggest codes for human review, meaning audit defensibility depends on the reviewer who validated each suggestion. Many solutions marketed as autonomous still require human intervention at some stage, or produce code outputs without explaining the clinical reasoning behind them. For HIM teams that face audits and payer appeals, a solution that cannot trace its reasoning back to the documentation is a liability.
Nym's autonomous medical coding engine operates with zero human intervention: encounters route directly to billing, and every coding decision includes a complete, human-readable audit trail with documentation references, guideline citations, and step-by-step rationale. When a payer challenges a code or an auditor requests documentation, the response is a clinical rationale generated automatically and consistently for every coded encounter, not a manually reconstructed justification after the fact.
Denials are the most visible downstream signal of coding accuracy problems and among the most expensive to address. The average cost to rework a single denied claim has risen to over $57, up from $43.84 in 2022 (2). 41% of providers reported denial rates above 10% in 2025, up from 30% three years prior (3). Each denied claim carries not only rework cost but potential audit exposure if denial patterns suggest systemic coding issues rather than isolated errors.
Consistent autonomous coding logic reduces initial coding errors, which reduces denials before they occur. At one large health system using Nym's autonomous medical coding engine for radiology professional fee coding, the coding-related denial rate dropped 97%, from 0.0998% to 0.0030%.
Sixty-five percent of medical coding errors trace back to documentation deficiencies (4). That's an upstream problem coding teams can't fix directly. Even where communication channels exist between coders and providers, it's difficult to identify which documentation practices are driving the most significant compliance exposure across a full encounter population.
Autonomous coding engines are positioned to surface that information. By processing every encounter through consistent clinical logic, they can identify documentation gaps not chart by chart but systematically, across hundreds of thousands of encounters.
At one large health system, Nym's engine benchmarked ED E/M leveling against comparable organizations, processing roughly 25,000 of the health system's monthly ED encounters alongside approximately 150,000 charts from four peer health systems. The analysis identified two documentation practices consistently suppressing E/M levels: providers were not consistently documenting consideration of escalation of care, and in lower-complexity encounters, X-rays were not being documented as independently interpreted. Neither was a coding error. Both were documentation practices that, left unaddressed, were creating both revenue leakage and compliance exposure.
Nym presented the findings alongside a coding guideline interpretation recommendation. The health system implemented all three recommendations, with a projected impact of a 5.84% increase in average reimbursement per ED encounter and $4.1M+ in predicted additional annual revenue (5). That outcome starts with the population-level documentation insight that autonomous coding engines generate.
Autonomous medical coding assigns codes to patient encounters without human intervention and routes them directly to billing. Every coded encounter includes a complete audit trail with documentation references and guideline citations. That means health systems have defensible clinical rationale for every code, automatically, rather than reconstructing a justification after a payer or auditor requests one.
Denials driven by coding inconsistency are reduced when the same coding logic is applied to every encounter. At one large health system using Nym's engine for radiology professional fee coding, the coding-related denial rate dropped 97%. Fewer initial errors means fewer claims requiring rework, appeal, or audit response.
Autonomous medical coding operates with zero human intervention: encounters go straight to billing with a complete audit trail. Other solutions, including computer-assisted coding and many marketed as AI-powered, often still require human review at some stage, or generate code outputs without explaining the clinical reasoning behind them. That distinction matters during audits, when defensibility depends on the engine's reasoning chain being traceable back to the documentation, not on a human reviewer's manual reconstruction.
By processing every encounter through consistent clinical logic, autonomous coding engines surface documentation patterns that affect E/M leveling or code accuracy across an entire provider population. At one large health system, Nym's analysis identified two documentation practices suppressing E/M levels across tens of thousands of monthly encounters. After the health system implemented the recommendations, the projected impact was a 5.84% increase in average reimbursement per ED encounter and $4.1M+ in additional annual revenue (5).
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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