AI Can Speed Up Medical Billing. Humans Must Remain in Control | Opinion

Healthcare billing processes have become increasingly complex, making efficiency a multifaceted concern. Rather than merely measuring how swiftly claims move through the system, the crucial question is whether the process is comprehensible, accountable, and equipped to handle the various exceptions that arise in real-world care.

A 2026 KFF Health Tracking Poll revealed that 47 percent of insured adults experienced delays or denials involving prescribed services, treatments, or medications within the past two years, with this figure being even higher for individuals with chronic conditions. These statistics underscore that reimbursement is not solely a back-office concern; when the process becomes difficult to navigate, it can negatively impact the delivery of care itself.

The strain on medical practices is substantial as well. The American Medical Association notes that physicians and their staff complete approximately 40 prior authorizations weekly and dedicate an average of 13 hours to these tasks. This highlights the necessity of discussing efficiency beyond automation alone. While technology can streamline repetitive tasks, it cannot inherently make a convoluted system more transparent or accountable.

Artificial intelligence (AI) can play a significant role in medical billing, particularly in handling predictable and rule-based tasks such as coding support, routine claim processing, and eligibility checks. However, this comprises only about 80 percent of the overall process. The remaining 20 percent involves aspects that transcend data handling, including appeal denials based on clinical nuance, negotiations with payers, and determining how to manage a patient's bill.

These challenges are unlikely to diminish as AI technology advances. Instead, they highlight the enduring need for human judgment. The best billing operations do not aim to replace human judgment with AI but rather to free up time for individuals to exercise their judgment effectively. A critical concern is that organizations might mistakenly assume an automated process is reliable solely because of its speed.

While a system can generate answers rapidly, it may not transparently explain how those answers were derived, which information might have been overlooked, or who bears responsibility for errors. In healthcare reimbursement, such questions are paramount.

During my examination of billing operations, I encountered an instance where a physician placed trust in the individuals managing the process based on their understanding. The concern was not that this trust was misplaced; rather, the issue arose from a busy doctor's inability to realistically review every denial, rejection, unpaid account, and administrative detail while simultaneously focusing on patient care.

This situation reinforced my belief that oversight must be embedded within the process rather than being contingent upon resolving a problem after it has become apparent. One of the most significant risks stems from an excessive focus on the final collection figure. A report may display the amount received, but this single metric fails to provide insight into whether claims were missed, denials were unresolved, accounts receivable are aging, or if the same workflow issues persist.

Providers require a deeper understanding of the process behind the result rather than accepting the result in isolation. This principle should also guide the application of AI. I advocate for technology to manage predictable tasks while expert practitioners concentrate on exceptions, denials, unaddressed receivables, and scenarios that necessitate interpretation.

The role of humans "in the loop" should not be viewed as a fallback option but rather as an integral component of the entire cycle.

When evaluating a billing system or partner, providers should consider more than headline metrics. They should inquire about the methods used to achieve results, the performance indicators being tracked, and the procedures in place when automation cannot resolve a case. Who reviews the system's output when it appears incomplete or incorrect?

Healthcare providers do not need to choose between people and technology; they require a model that leverages each for its strengths. AI can enhance the efficiency of routine tasks, but essential judgment, context, non-deterministic variables, and accountability must still be overseen by humans. Efficiency, while valuable, should only be sought when it is accompanied by responsibility.

As Venkata Nuli, co-owner of AAA Medical Billing, emphasizes, providers should prioritize transparency into how numbers are derived, not merely the numbers themselves, to ensure a sustainable reimbursement process.