AI Can Be the Magical Arrow in Your Denial Team’s Quiver

As healthcare organizations grapple with the complexities of revenue cycle management (RCM), artificial intelligence (AI) is emerging as a transformative force. Hospitals and health systems spend an estimated $19.7 billion annually to appeal denied claims, with 90% of CFOs identifying denials as a major challenge for RCM teams. About 15% of all claims submitted to private payers are initially denied. While more than half of these denied claims are eventually approved and paid, they incur costly and time-consuming appeals.

In ambulatory surgery, AI is playing a role in enhancing efficiency and addressing mismatch denials. However, current AI technologies still fall short when it comes to handling complex scenarios like surgical coding, where the nature of procedures can deviate significantly from pre-authorization guidelines.

Traditional AI solutions, while effective at identifying patterns, often lack the necessary depth of contextual understanding to accurately interpret complex medical and surgical scenarios. Surgical coding involves highly specific and dynamic contexts where pre-authorized procedures frequently change based on real-time decisions made during surgery. Existing AI models struggle to adapt to these variations, leading to incorrect coding, claim denials, and substantial financial losses.

Mismatches occur when surgical procedures deviate from what was pre-authorized by the payer. These mismatches are often unavoidable, as surgical realities can differ from pre-surgery plans. However, they commonly lead to claim denials with far-reaching consequences:

• Resource drain: Denials teams invest significant time and effort appealing these claims, leading to high human resource costs.
• Cash flow disruption: Lengthy denial and appeal cycles delay reimbursements, impacting an organization’s financial stability.
• Revenue loss: Lower-value denials are often written off due to the cost of appeals, resulting in significant lost revenue over time.

AI excels at identifying patterns in data, offering capabilities like:

• Interpreting clinical notes
• Providing accurate code pairings
• Identifying potential mismatches before claims submission

While these systems can process vast amounts of data, their understanding remains limited to predefined scenarios. Complex surgical procedures and the nuances within clinical notes often exceed the capabilities of current AI models, leading to errors and coding inconsistencies. Even though these models are “trained” on large datasets, they struggle to capture the fluidity and context-dependent nature of surgical procedures.

The solution lies in a new AI paradigm: Data Obstruction-Based AI, which goes beyond traditional pattern recognition by incorporating a deeper contextual awareness. This approach uses advanced algorithms designed to understand the relationships and nuances within surgical and clinical data, adapting dynamically as new information becomes available during the procedure.

With a stronger contextual understanding, data obstruction-based AI can:

• Proactively adjust codes based on real-time surgical updates, reducing the likelihood of mismatches and denials.
• Initiate reauthorization processes automatically, minimizing delays and ensuring smoother claim approvals.
• Integrate seamlessly with EHR systems to capture procedural details and update coding in real time.

A shift to data obstruction-based AI would provide several benefits:

• Proactive prevention: Address mismatches before claims are submitted, minimizing the likelihood of denials.
• Real-time adaptability: AI systems can evolve with each new data input, maintaining accuracy despite procedural changes.
• Enhanced consistency: Standardizing coding practices with contextual intelligence reduces variability and improves accuracy.

Empowering coders: By handling routine coding tasks, AI allows human coders to focus on complex cases that genuinely require expertise.

Data obstruction-based AI has the potential to dramatically reduce coding costs by up to 90% and increase revenues by up to 20%. Unlike existing solutions, it can identify and adapt to missed opportunities with precision, ensuring correct CPT and ICD code assignments, even for complex surgical cases.

For surgical denials, data obstruction-based AI can:

• Recognize mismatches in real-time and automatically initiate reauthorization processes.
• Alert staff to obtain necessary updates before submitting claims, minimizing the risk of denials.

For instance, a hospital using such an advanced AI platform reported salvaging over $5 million in surgery mismatch denials.

When considering AI for denial management, organizations should evaluate:

• Current denial patterns and their impact
• The potential of advanced AI to achieve 90% accuracy in preventing mismatch denials
• The primary and secondary costs associated with their current denial processes
• The adaptability of their AI strategy
• How they would reinvest resources if technical denials were significantly reduced

The future of healthcare finance lies not just in combating denials but in preventing them altogether through a deeper, context-aware AI approach. With nearly 60% of healthcare organizations considering AI for RCM operations, the real question is not if but when and how they will integrate these technologies. Data obstruction-based AI offers a critical solution for organizations seeking to optimize their revenue cycle operations, achieve greater accuracy, and enhance their financial performance.

Milagro is at the forefront of innovation in autonomous surgical coding, leveraging advanced data obstruction-based AI to tackle the industry’s toughest coding challenges. By enhancing accuracy, preventing costly denials, and optimizing revenue cycles, Milagro’s solutions empower healthcare organizations to drive efficiency and financial stability.

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