Understanding the Shift in Hospital Revenue Integrity: Navigating AI-Driven Claim Denials
For decades, hospital revenue integrity teams have battled payment denials as if they were isolated transactions. A claim gets rejected, and the cycle of appeal, refile, and track results continues. However, this approach is becoming increasingly unsustainable.
According to the American Medical Association, 11% of all claims were denied by payers in 2023, a notable increase from 8% in 2021. This rise equates to approximately 110,000 unpaid claims for the average health system, highlighting the financial strain caused by denials.
The Evolution of Denials: From Rules to Behavioral Strategies
Today’s payers employ sophisticated analytics to strategically decide which claims to reject, often predicting the provider’s response or lack thereof. This approach isn’t solely about enforcing rules concerning medical necessity or coding clarity; it’s about identifying claims unlikely to be appealed or where underpayments might go unnoticed. This shift represents a significant change in the reimbursement landscape, leaving many hospitals unprepared.
Payers have historically had access to extensive claims data. The difference now lies in the tools available for analysis. AI-powered analytics enable payers to review years of claims and identify patterns in hospital responses. They can determine:
- Which denial codes are least likely to be contested by hospitals?
- What service lines see fewer complaints due to resource limitations?
- When underpayments fall below internal review thresholds.
With this data, AI-driven decision engines can assign “denial risk scores,” factoring in the likelihood of appeal and historical outcomes. Some denials succeed not because they’re accurate but because they are unlikely to be challenged.
Why Some Denials “Work” Despite Being Incorrect
Revenue cycle teams often prioritize rejections based on submission deadlines and anticipated revenue. If the cost of appealing a denial outweighs potential recovery, it may be economically sensible to forgo it. As reported by Becker’s Hospital Review, the cost per rejection rose from about $44 in 2022 to over $57 in 2023, imposing a significant industry-wide financial burden.
Payers recognize these calculations. As AI models incorporate provider capacity and historical appeal patterns, they tailor decisions towards cases with low appeal rates. Denials succeed not due to correctness but because they are profitable when unchallenged. This operational optimization poses sales losses for providers, often unnoticed because individual claims don’t appear significant.
What Hospitals Should Focus On
As payers leverage AI and automation to increase denial speed and complexity, hospitals struggle to keep up. The traditional methods of managing denials are insufficient amid the overwhelming volume and lack of necessary resources. Nearly 60% of providers have reported increased claim denials year-over-year, collectively spending over $20 billion annually to address them, according to MGMA.
To stay competitive, revenue cycle leaders must shift from reactive to strategic approaches. Critical questions include:
- Which denial categories have low appeal rates, and are they valid?
- Where are underpayments consistently just below verification thresholds?
- Which payers exhibit patterns of partial payments or “soft denials”?
- How often do hospitals forgo appeals based on expense rather than merit?
These inquiries are not about assigning blame but understanding the intersection of payer behavior and provider capacity where risks arise.
Leveraging Data to Level the Playing Field
Fortunately, providers can also utilize analytics to counteract these dynamics. Leading organizations are examining denials and underpayments from diverse perspectives, considering not just reason codes but also appeal costs, recovery likelihood, and financial impact.
Instead of asking, “Why was this rejected?” they explore:
- Why does this rejection persist?
- Which claims are systematically underpaid?
- Which rejections have the highest effort-to-upheaval ratio?
- Which payer policies correlate with high low-return denial rates?
This shift helps hospitals identify strategically designed denial patterns and address them through targeted appeals or payer negotiations. Data-driven insights transform anecdotal frustrations into measurable, defensible evidence.
The Unseen Losses: Recognizing Hidden Revenue Drains
Hospitals face unprecedented pressure on margins. In 2025, health system average operating margins reached 1.2%, their strongest performance of the year, yet leaving little room for error. The greatest risk isn’t the denial that reaches the work queue but the one that silently integrates into routine operations, unnoticed.
With AI deeply integrated into payer workflows, unnoticed revenue losses are more common than outright rejections. As hospitals invest in analytics and automation, it’s crucial to expand the conversation beyond speeding up appeals to understanding payer strategies and recognizing hidden patterns.
Visibility becomes invaluable when decisions are designed to remain under the radar.
Photo: digicomphoto, Getty Images
Paul Havey is Chief Commercial Officer of Revecore, a leading provider of complex revenue cycle management solutions to hospital systems nationwide. With a career in healthcare revenue cycle management, including over a decade at Change Healthcare (now Optum), Paul holds a bachelor’s degree in business administration from the College of New Jersey and an MBA in finance and consulting from Wake Forest University.
This article is part of the MedCity Influencer Program. Through MedCity Influencer, individuals can publish their views on healthcare business and innovation on MedCity News. Learn more Here.
“`
