AI Beyond the Note: How Podiatry Practices Are Using Artificial Intelligence to Improve Revenue Cycle Performance and Reduce Claim Denials

Artificial intelligence may offer podiatry practices value well beyond clinical documentation. By identifying reimbursement risks, revealing denial trends, and automating routine administrative tasks, this authors shares that AI-assisted tools could help strengthen revenue cycle performance while preserving human oversight. 

Key Takeaways

• AI can help practices identify documentation, coding, eligibility, and payer-related risks before claims are submitted. 

• Continuous analysis may uncover denial patterns, prioritize higher-value appeals, and reveal operational problems earlier than traditional monthly reporting. 

• AI should support—not replace—the judgment of clinicians, coders, and billing professionals, with results measured through outcomes such as clean claim rates, denial rates, and reimbursement timelines. 

When healthcare organizations discuss artificial intelligence (AI), the conversation almost always begins with documentation. Ambient AI has quickly become one of the most visible applications of the technology, helping clinicians generate medical notes more efficiently and reduce the administrative burden associated with documentation. In my observation, for podiatry practices, those benefits are meaningful. Less time spent documenting can translate into more time with patients, improved work satisfaction, and more consistent clinical records. 

However, documentation is only the first step in a much larger financial workflow. A complete clinical note does not guarantee reimbursement. Every patient encounter must still move through claim generation, payer submission, adjudication, payment, and, in some cases, appeals. Along that journey, relatively small errors—including missing modifiers, insufficient documentation of medical necessity, eligibility issues, or payer-specific requirements—can result in rejected or denied claims that delay reimbursement and increase administrative workload. 

As AI continues to mature, I find that many physician practices are beginning to recognize that its greatest value may lie beyond note creation. Rather than focusing solely on documenting care, AI is increasingly being used to help practices improve revenue cycle performance, identify reimbursement risks before claims are submitted, automate repetitive administrative work, and surface operational trends that might otherwise go unnoticed. For podiatry practices facing increasing reimbursement pressure and ongoing staffing challenges, these capabilities have the potential to improve financial performance while allowing clinicians and administrative staff to focus their attention where it matters most. 

Podiatric practice presents unique revenue cycle challenges. Many common procedures, including diabetic foot care, wound management, surgical interventions, injections, imaging, and durable medical equipment (DME), require careful documentation to establish medical necessity and comply with payer-specific coverage policies. Appropriate diagnosis-to-procedure relationships, laterality, modifier usage, frequency limitations, and prior authorization requirements all influence whether a claim is ultimately paid. Even practices with experienced billing teams regularly encounter denials resulting from documentation deficiencies, missing modifiers, duplicate claims, eligibility issues, or timely filing requirements. Guidance from the Centers for Medicare & Medicaid Services (CMS) continues to emphasize the importance of accurate documentation in supporting reimbursement. 

Historically, practices have addressed many of these issues only after receiving a denial from the payer. By that point, staff members must identify the cause of the denial, locate supporting documentation, make corrections, and resubmit the claim, a process that consumes valuable time while delaying payment. AI is beginning to change that model by helping practices identify potential issues before claims are ever submitted. 

This shift represents an important evolution in how AI is used within physician practices. Traditional claim scrubbing relies on predefined business rules that flag obvious issues before submission. While those rules remain essential, they cannot account for every documentation nuance or evolving payer behavior. AI complements those rules by analyzing patterns across large volumes of historical claims, documentation, and payer responses. Rather than evaluating a single field in isolation, AI can assess multiple variables simultaneously to determine whether a claim appears likely to encounter reimbursement challenges. 

For example, AI may recognize that a particular diagnosis-procedure combination has historically resulted in denials for one payer, identify documentation elements that are frequently associated with medical necessity denials, or detect missing modifiers based on prior successful claims. These insights allow billing teams to review higher-risk claims before submission instead of waiting several weeks for a denial that requires additional work to resolve. 

This proactive approach extends beyond individual claims. AI is particularly well suited to identifying patterns that are difficult for people to recognize manually. A billing manager reviewing hundreds or thousands of claims each month may not immediately notice that denials for one CPT code have steadily increased, that one payer has introduced a new documentation expectation, or that eligibility issues are becoming more common at a specific practice location. AI systems can continuously analyze these trends and surface meaningful changes early, allowing practices to address underlying workflow issues before they become widespread operational problems. 

Another area where AI is beginning to demonstrate value is denial management. Every practice faces denied claims, but not every denial deserves the same level of attention. Some involve relatively small reimbursement amounts, while others represent substantial revenue or face imminent filing deadlines. AI can help billing teams prioritize work by evaluating factors such as reimbursement value, appeal likelihood, payer behavior, historical success rates, and filing timelines. Instead of working denials chronologically, staff can focus first on claims that are most likely to produce meaningful financial recovery. Over time, AI can also identify recurring root causes, enabling practices to correct systemic issues rather than repeatedly addressing the same errors one claim at a time.