In a recent educational webinar, “AI-Powered Revenue Recovery: A Smarter Approach to Clinical Denials and No Surprises Act Disputes,” Aspirion’s Chase McGrath, Vice President of Product, and Nikki Ritchson, Senior Director of Operations, revealed how hospitals can harness artificial intelligence (AI) to recover more revenue—faster—across two of the most administratively burdensome areas in revenue cycle management.
The Scale of the Problem
The financial stakes in clinical denials and out-of-network reimbursement under the No Surprises Act (NSA) are staggering. Clinical denials account for well over $200 billion in annual claim losses for hospital organizations, with denial rates continuing to climb year over year. Between 15% and 17% of all submitted claims currently carry a denial, and the time and resources required to resolve them grow more demanding every year.
The out-of-network landscape under the NSA presents its own challenges. McGrath pointed out that reimbursement as a percent of billed charges typically falls below 15%—far under Medicare and Medicaid levels, let alone contracted rates. Ritchson added that between 2022 and 2025, more than three million disputes were filed through the federal independent dispute resolution (IDR) process, a number that continues to rise as providers become more familiar with their options.
When providers push back, Ritchson noted, they win more than 88% of the time, with payouts running three to four times the initial qualifying payment amount (QPA). The challenge is the process itself, which she said averages close to 200 days from claim filing to payment.
Why These Two Problems Share One Solution
One of the webinar’s most compelling insights was the significant operational overlap between clinical denials management and NSA out-of-network dispute resolution. Both workflows require establishing the acuity of care provided, documenting good faith efforts, and making a clearly articulated ask of the payer. Both involve strict, unforgiving timelines—miss a single deadline in the IDR process, and 100 or more days of administrative work can be forfeited in an instant.
As McGrath explained, this convergence is exactly where AI delivers outsized value. The same workflow platform, the same letter-generation technology, and the same specialized resources—clinicians, attorneys, and appeals specialists—can serve both service lines. Building for one means you’ve largely built for the other.
What AI Actually Does
McGrath level-set on the practical definition of AI, describing it as a spectrum ranging from simple rules-based systems and robotic process automation at the foundational level, up through natural language understanding and computer vision, and finally to the large language models (LLMs) that power tools like ChatGPT. All of these systems share the same basic architecture: ingest data, apply reasoning, produce a desired output. What differentiates more advanced systems is their capacity to learn over time.
At Aspirion, AI is deployed across the full account lifecycle. On intake, AI reads placement files to identify claim type, routes work intelligently to the right queues, and triggers submissions automatically—a step that previously took up to five days now happens the same day a claim arrives. The system tracks every critical deadline in real time, from the 30-business-day open negotiation window to the four-day IDR filing period, flagging cases at risk of missing key junctures before it’s too late.
Most significantly, AI is now generating complete, submission-ready negotiation letters. Using the medical record and basic claim details as inputs, the system produces a letter that addresses all the key factors IDR entities evaluate—facility acuity, scope of services, care timeline, and good faith negotiation history—in a matter of minutes. What previously required an analyst to spend an hour or more now takes a fraction of the time, with human reviewers providing a final check before submission.
Results That Speak for Themselves
The operational improvements are measurable. Under the NSA workflow, AI has reduced the time from placement to negotiation submission from roughly five days to same-day processing, and overall placement-to-closure timelines have dropped from 37 days to 21, though individual results may vary. Most revenue outcomes have held strong throughout: AI-assisted cases continue to recover two times the initial QPA through open negotiation, and three to four times in IDR proceedings.
On the clinical denials side, the results echo findings Aspirion has shared previously. AI-generated first-level appeals were completed in an average of 32 days, compared to 70 days for manually processed appeals—a 2.2x acceleration, though individual results may vary. Most full resolution timelines have improved from 219 days to 158 days.
Building Your AI Strategy
McGrath walked through three strategic paths organizations can take: build, buy, or partner. Building offers maximum control but requires significant internal resources and time—it is a multi-year journey. Buying delivers speed but may sacrifice customization. Partnering, the hybrid approach, combines operational expertise on one side with technical capability on the other, and is often the most practical path for healthcare organizations navigating limited IT bandwidth and complex compliance requirements.
Whichever path an organization chooses, McGrath emphasized the importance of accounting for risk upfront across five dimensions: financial, legal, operational, reputational, and structural. He also stressed the value of early impact indicators. Before cash collections reflect the full benefit of AI investment, organizations can track leading metrics like days to first appeal submission and resolution rates—signals that build the internal case for continued investment and make the CFO conversation far more productive.
The Opportunity Is Here
Ritchson closed with a pointed call to action: payers are already deploying AI at scale. The acceleration of denial volume and the increasingly automated nature of payer response letters are direct evidence of that. For providers, the choice is not whether AI will play a role in revenue recovery—it is whether they will use it to keep pace.
With proven outcomes already in hand and AI investment driving results across multiple service lines, the opportunity is undeniable. What determines whether organizations seize it, as Ritchson explained, comes down to four Cs: the capital to invest, the capacity to scale, the capability to execute, and the commitment to follow through. In the race to adopt AI, miss one—and the rest won’t matter.
To learn more, watch the full on-demand webinar.
