In a recent Becker’s Healthcare webinar, “AI in RCM: How to Turn Claims Challenges into Revenue Opportunities,” Aspirion’s Jim Bohnsack, Chief Strategy & Client Officer, and Spencer Allee, Chief AI Officer, discussed how artificial intelligence (AI) is transforming healthcare revenue cycle management, particularly in the area of denials management. The presentation highlighted how hospitals and health systems can leverage AI technologies to address growing challenges with claim denials.
The Current Denials Landscape
The healthcare industry is experiencing a significant increase in claim denials. High-dollar claim denials have increased by 22%, outpatient denial volume is up 18%, and inpatient denials have risen by 13%.
“What we’re seeing today in the market, you all are seeing it. If you’re on the provider side, it’s a massive increase in the number of denials. The volume has increased tremendously over the last several years,” explained Bohnsack.
Of particular concern is the rise in partial denials such as diagnosis-related group (DRG) downgrades, patient-type downgrades, and necessity-related, length-of-stay challenges. These partial denials are especially problematic because they require the same resource-intensive appeals process as full denials but offer a smaller potential recovery (only the difference between the original billing amount and the reduced reimbursement).
Bohnsack noted that hospitals face a competitive disadvantage as payers have advanced more rapidly in deploying technology for denials management, implementing faster denials turnaround times and broader denials strategies.
Staffing Challenges in Denials Management
A major obstacle in addressing denials is the shortage of skilled professionals required for appeals, including nurses, coders, and attorneys. These well-trained, credentialed team members are essential for reviewing medical records and creating detailed appeal letters that provide evidence supporting the original billing.
In Aspirion’s experience, attorneys and clinicians traditionally focused on high-dollar, complex clinical denials have been limited in their throughput. On average, these professionals could only complete about 2.5 appeal letters per day due to the intensive work required to review medical records, find evidence, and compile it against care guidelines or coding rules.
The AI Approach to Denials Management
The presenters underscored the importance of starting with well-defined business problems rather than adopting technology for its own sake. Aspirion’s approach involves:
- Understanding manual processes first: Mapping existing workflows before attempting automation
- Incremental automation: Introducing technology components one by one rather than attempting full end-to-end automation immediately
- Machine learning (ML) for prioritization: Using models to identify which claims and appeals will have the highest impact
- Large language models (LLMs) for unstructured data: Leveraging AI to process medical records and surface relevant evidence
“We look for solutions to these problems across technology and in particular with AI and machine learning,” explained Allee. “Things like scoring models to help predict which accounts are likely to yield a reimbursement versus not so we can maximize dollars for our clients.”
By automating portions of the appeals process, Aspirion aims to increase efficiency, improve quality, and accelerate processing times. This allows the company to address the growing volume of partial denials that were previously impractical to pursue due to economic constraints.
Implementation Challenges
Aspirion highlighted several challenges in implementing AI for denials management:
- Data quality and completeness: Automation requires comprehensive data, which often means integrating multiple sources and filling gaps
- Access to guidelines and policies: Obtaining and integrating care guidelines, coding rules, and payer policies
- Change management: Training staff and integrating new tools into existing workflows
Both Bohnsack and Allee stressed that automation is meant to enhance efficiency and reduce manual burdens, not replace jobs. They noted that staff satisfaction has improved as AI tools help professionals work more effectively.
Results and Future Directions
During its AI journey, Aspirion has seen improvements in efficiency, quality, and throughput. Both executives emphasized that the technology has allowed staff to work at the top of their license, focusing their expertise on decision-making rather than manual processing.
Looking ahead, the rapid evolution of AI technologies, especially LLMs and agentic AI, presents significant opportunities for healthcare revenue cycle management.
“I’ve been doing this for 25-plus years and I’ve seen a speed of change and excitement in terms of what the power of this technology has to offer, especially large language models,” shared Bohnsack. “Quite frankly it’s amazing. Some of the new agentic AI capabilities and their ability to converse and answer questions—it’s just mind-boggling to me.”
The revenue-cycle experts emphasized their mission-driven goal of ensuring providers are paid appropriately and reducing their administrative burdens, ultimately benefiting patients and communities.
As payers continue to advance their technology capabilities, healthcare providers must likewise invest in AI and automation to effectively manage denials and secure appropriate reimbursement for care delivered.
To learn more, watch the entire on-demand webinar here.
