4 ways autonomous coding prevents claim denials at the source

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Healthcare providers face an unprecedented challenge with claim denials. Nearly $20B is lost fighting denied claims annually, while two-thirds of organizations report increasing reimbursement times. These trends are concerning: 55% of revenue cycle leaders experience rising claim errors, and 77% face more frequent payer policy changes than in the previous year. Across the board, each metric has worsened since 2022, pointing to systemic issues in how claims are processed and submitted.

While most organizations look for temporary fixes, forward-thinking industry leaders are finding promising results with autonomous coding as an initiative for denial prevention. By addressing potential denials before claims are submitted, AI coding offers a path to sustainable improvement in denial outcomes. To understand how autonomous coding can help prevent denials, let's first examine the headwinds facing coding today and the coding-related factors that contribute to claim denials.

Examining today's coding challenges

Behind today's high denial rates lies a fundamental tension – coding requirements grow increasingly complex as coding resources lessen. Consider the typical workflow in a practice's revenue cycle. Understaffed coding teams juggle mounting backlogs of encounters while trying to maintain accuracy. Billing staff spend hours on administrative tasks that could be automated. Physicians get pulled away from patient care to address documentation gaps discovered days or weeks after the encounter. These inefficient processes lead to denials that cost an average of $43.84 to rework, while the accompanying reimbursement delays strain operational budgets.

The staffing crisis in medical coding makes these challenges even more acute. With 30% of organizations reporting coding staff shortages, and experienced coders retiring faster than new ones joining the field, the pressure continues to build. Higher workloads lead to more errors, creating more denials that require staff time – a cycle that traditional solutions can't break.

Overall, as coding-related drivers of claims denials, missing or inaccurate data accounts for 46% of denials, followed by authorization issues at 36%, and incomplete patient information at 30%. Manual approaches to these processes struggle to maintain accuracy and velocity at sufficient scale with today's resourcing.

Breaking the denial cycle with AI

This is where autonomous coding provides relief. The way claims are coded plays a critical role in whether they're paid or denied. While basic automation tools can help human coders, they don't address the fundamental challenges in the coding process that lead to denials.

Let's examine four key ways coding impacts claim denials and how autonomous coding helps prevent these patterns.

1. Checking documentation and finding errors: Many denied claims are due to missing or incomplete documentation. In manual coding workflows, documentation gaps often aren't discovered until weeks after the encounter. By then, getting accurate information from providers becomes difficult or impossible. These delayed discoveries are one of the leading causes of preventable coding-related denials. In contrast, because AI coding analyzes documentation immediately after each visit, it prompts providers to correct deficiencies when the information is still fresh and readily available.
2. Coding accuracy and consistency: Inaccurate coding leads to denied claims when the codes assigned don't properly reflect the care provided or don't comply with payer-specific rules. In manual workflows, accuracy inevitably fluctuates with coder fatigue, varying expertise levels, and high workloads. As coding volumes increase and guidelines grow more complex, these inconsistencies multiply. Autonomous coding eliminates these variables by applying coding rules uniformly regardless of volume or complexity, maintaining consistent accuracy that helps to prevent denials.
3. Guideline changes: Keeping up with changing payer requirements is crucial for clean claims. When policies change, manual coding teams typically need months to learn and implement new guidelines proficiently, leading to increased denials during the transition period. The 2023 E/M guidelines change proved this point as practices saw spikes in denials while coders got up to speed. AI coding systems, however, update instantly to reflect new guidelines, maintaining compliance from day one and preventing these transitional denial surges.
4. Resource allocation: The availability of coding resources directly impacts denial rates. In manual coding environments, organizations face a difficult choice: either invest heavily in expanding coding teams or distribute coding duties across clinical staff to help bear the load. Both approaches drain resources while failing to address the root cause of denials. Autonomous coding offers a different path by handling the vast majority of encounter volumes automatically, allowing both coding teams and clinicians to focus on work that makes better use of their expertise.

Building a sustainable future
With denial rates climbing and organizations losing billions in denied claims each year, healthcare leaders need to consider new approaches to prevention. Autonomous coding offers powerful capabilities that can help reduce denials – bringing precision to documentation, reliability to coding, and speed to the entire process. The benefits extend beyond denial reduction: practices gain more efficient revenue cycles while their teams gain the bandwidth to focus on higher-value activities.

For organizations focused on reducing denials and building sustainable financial operations, autonomous coding represents a significant opportunity to make progress toward these goals.

Austin Ward is Head of Growth at Fathom, the leader in autonomous medical coding. He oversees the company's go-to-market efforts and client analytics.