The Costly Impact of Downcoding and How AI-Powered RCM Can Recover Revenue

In the complex world of healthcare finance, Revenue Cycle Management (RCM) teams face a barrage of challenges that directly impact a provider’s bottom line. Among the most persistent and financially damaging of these issues is downcoding, a systemic problem that leads to underpayments, administrative headaches, and significant revenue loss. As healthcare organizations increasingly deploy advanced technologies like AI in RCM, understanding downcoding and how to address it has become mission-critical for revenue integrity and financial performance.

What Is Downcoding?

At its core, downcoding occurs when a payer reimburses a claim at a lower service level than what was actually provided and billed. For example, a detailed, high-complexity visit coded as Level 4 or 5 may be paid as a lower-level service because the payer’s adjudication system or reviewer determined, rightly or wrongly, that the documentation didn’t support the higher code. This discrepancy immediately results in underpayment for services rendered. 

Downcoding may happen for a number of reasons, including documentation gaps, misinterpretation of coding guidelines, automated adjudication logic used by payers, or even system errors that strip crucial code details during integration between EHRs and payer systems.  

Why Downcoding Is Such a Big Deal for RCM Teams

1. Revenue Leakage and Underpayments

Downcoding directly translates into money lost. When services are systematically paid at lower levels than documented, organizations leave tens of thousands, or even millions, of dollars on the table. These cumulative underpayments can significantly disrupt cash flow, distort financial forecasts, and undermine revenue targets. 

2. Operational Strain on RCM Teams

Manual identification and remediation of downcoded claims pose an enormous administrative burden. Staff must review massive claim volumes, compare coded services against payment outcomes, appeal underpayments, and chase additional documentation, all using fragmented systems and manual processes. This inefficiency drains resources, slows revenue capture, and contributes to staff burnout. 

3. Hidden in Plain Sight

Unlike flat-out denials, downcoding often doesn’t trigger a full rejection that makes it obvious something is wrong. Instead, claims are paid, just not at the correct level. This subtle form of underpayment makes it difficult for teams to know when a problem exists without deep, systematic analysis. Traditional tools often fail to flag these patterns quickly or at scale. 

4. Compliance and Documentation Challenges

Downcoding can sometimes stem from inadequate clinical documentation or coder knowledge gaps. Ensuring that documentation fully supports the level of service billed is both a clinical and administrative challenge. Gaps between clinical documentation practices and coding requirements further complicate the process, especially as coding standards and payer requirements evolve. 

Underpayments: The Hidden Twin of Downcoding

Downcoding is one of several causes of underpayments, which occur when a payer reimburses less than the contractually agreed or legally owed amount. Underpayments may also result from technical errors in claim processing, retroactive policy changes, or disconnected systems that distort data as it flows through the RCM ecosystem. Without automated detection and follow-up, these underpayments often go unnoticed for months, compounding financial loss.

In the 2025 State of AI in RCM report, industry leaders highlighted underpayment detection as a top challenge for RCM teams, underscoring how pervasive this issue has become. 

How AI Is Changing the Game

With the rise of AI in RCM, organizations now have new tools not only to manage volume but to detect subtle anomalies like downcoding and underpayments at scale. AI systems can analyze complex patterns across millions of claims, flag discrepancies, and prioritize the highest-impact issues for action.

AI doesn’t just automate tedious work, it spotlights hidden revenue opportunities and reduces the time between service delivery and rightful payment, dramatically improving financial outcomes and operational efficiency.

Real-World Success: How Adonis Helps RCM Teams Uncover Downcoding and Recover Revenue

A compelling example of AI’s impact comes from ApolloMD’s work with Adonis Intelligence and AI Agents. Faced with pervasive revenue cycle inefficiencies, including downcoded and underpaid claims, ApolloMD implemented Adonis’ platform to gain full, real-time visibility into revenue performance. 

With Adonis Intelligence, ApolloMD uncovered $46.6 million in downcoded payments across payers, enabling proactive recovery and workflow improvements that would have been nearly impossible to find through manual review. This visibility empowered the organization to redesign workflows, close credentialing gaps, and strengthen payer connections, transforming its RCM operations. 

Beyond uncovering downcoding, adopting Adonis AI Agents helped automate labor-intensive tasks like payer outreach while reducing denials and administrative workload—leading to improved cash flow and operational scalability.

Final Thoughts

Downcoding and underpayments represent one of the most significant threats to financial health in healthcare today. Without advanced analytics and AI-driven insights, these issues are costly, time-consuming, and difficult to resolve. But with AI in RCM, revenue cycle teams can move from reactive denial management to proactive revenue cycle management, identifying hidden revenue, improving cash flow, and empowering teams to focus on strategic work rather than firefighting.