Most coding-related denials are decided before a claim ever reaches the payer. A code that doesn't fully reflect the documented care, a missing modifier, or a diagnosis-procedure mismatch gives payer systems exactly what they're built to flag. The claim comes back, and your team absorbs the rework.
The volume of that rework is growing. Coding-related denials increased 126% in 2024 (1), and in 2025, 41% of providers reported overall denial rates above 10%, up from 30% three years prior (2).
Autonomous medical coding addresses the problem at the coding step itself. It won't eliminate every denial. I'd be skeptical of any solution that promises it will. What it changes are two of the most common root causes: insufficient code specificity and inconsistent rule application.
Manual coding under volume pressure produces variability. Coding decisions shift across coders, across shifts, and with documentation quality. The result: modifier errors, codes that undersell the documented level of care, and diagnosis-procedure mismatches that raise medical necessity questions. Payers now use automated review technology to detect exactly these patterns, contributing to a 122% increase in commercial payer requests for information (1).
None of this reflects your coders' skills. It reflects the limits of manual coding scalability: more encounters, fewer coders, and guidelines that change throughout the year.
The first way autonomous coding helps prevent denials is precision. Nym's autonomous medical coding engine, powered by Clinical Language Understanding (CLU) technology, translates provider notes within patient charts into medical codes in seconds with over 95% accuracy and zero human intervention. Because the engine reads the full clinical documentation on every encounter, it's built to select the most specific codes the documentation supports.
Why does specificity matter for denial prevention? Specific, well-supported codes substantiate the medical necessity of the interventions and procedures performed during the encounter. When the codes on a claim fully reflect what the documentation says happened, payers have less room to question it.
Customer data shows what this looks like in practice. At Inova, average charges per emergency department encounter increased by over 10% after implementing Nym's engine, attributed to more complete capture of bedside procedures, appropriate E/M level 4s and 5s, and more consistent injection and infusion coding. Codes that accurately reflect documented care support both revenue integrity and claim defensibility. Read the full Inova case study.
The second mechanism is consistency. Medical coding operates under layered rule sets: organization-specific guidelines and standard operating procedures (SOPs), CMS and AMA guidelines, NCCI edits, and payer-specific requirements that vary by contract. Medical coders may not always apply these rules consistently or may not account for recent guidelines/policy updates, creating potential variability within final assigned codes.
Depending on the underlying technology, autonomous medical coding solutions will improve coding consistency. Nym's engine, for example, uses proprietary machine-learning models and rules-based clinical ontologies to apply the same rule set the same way to every patient encounter it codes. Nym's engine also maintains alignment with external regulatory and payer guidelines as well as each health system's internal coding guidelines, so every code assignment reflects both industry requirements and your organization's coding philosophy. That consistency removes the variability that payer algorithms are designed to flag.
To provide an example of what improved coding consistency looks like in practice, one large health system that leverages Nym for emergency medicine coding experienced increased capture of social determinants of health (SDOH) codes, specifically those related to homelessness, after deploying Nym’s engine. This was attributed to Nym’s engine reading and picking up the diagnosis documentation more consistently than its team of medical coders had before autonomous coding was deployed.
At a different large health system using Nym's engine for radiology professional fee coding, coding-related denial rates dropped 97% after going live with Nym. Fewer denied claims means fewer appeals and rework for your team. And when a payer does challenge a code, Nym's engine provides a complete audit trail with documentation references and guideline citations for every code assigned.
*Results vary by organization. Denial performance depends on documentation quality, payer mix, and front-end processes alongside coding. What autonomous coding changes is the coding step itself: specific, documentation-supported codes assigned under consistently applied rules.
Through two mechanisms: selecting the most specific codes the clinical documentation supports, which substantiates medical necessity, and applying CMS, AMA, and payer-specific coding rules consistently across every patient encounter, which eliminates the variability that payer systems flag.
No. Denials are also driven by factors outside coding, including documentation quality, eligibility, and prior authorization. Autonomous coding reduces the denials that originate at the coding step, and customers are experiencing results like a 97% decrease in radiology professional fee coding-related denials at one large health system.
CAC suggests codes for human validation, so consistency still depends on the reviewer. Autonomous medical coding assigns codes with zero human intervention, and routes encounters directly to billing, applying the same coding logic to every chart.