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By Venkata Nuli
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Healthcare reimbursement has become so complicated that efficiency can no longer be measured only by how quickly a claim moves through a system. In my view, the more important question is whether the process is understandable, accountable, and capable of handling the many exceptions that arise in real-world care.
That matters because administrative friction can reach both providers and patients. A 2026 KFF Health Tracking Poll found that 47 percent of insured adults said they had experienced a delay or denial involving a prescribed service, treatment, or medication during the previous two years. For people with chronic conditions, the share was even higher.
Those numbers reinforce something I have seen repeatedly in revenue-cycle work: reimbursement is not simply a back-office issue. When the process becomes difficult to navigate, the effects can extend into the delivery of care itself.
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The burden on medical practices is also significant. The American Medical Association reports that physicians and their staff complete about 40 prior authorizations each week and spend an average of 13 hours on them. To me, that illustrates why the conversation about efficiency cannot stop at automation. Technology can reduce repetitive work, but it cannot automatically make a complicated system more transparent or accountable.
Beyond Automation
I believe artificial intelligence has an important role in medical billing, particularly where tasks are predictable and rules are clearly defined. Coding support, routine claim processing, eligibility checks, and other structured activities may all benefit from automation.
But that’s the easy 80 percent. The remaining 20 percent is where billing stops being a data problem and becomes a human one (aka human-in-the-loop): appealing a denial that hinges on clinical nuance, negotiating with a payer with logic, or deciding how to handle a patient’s bill.
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That 20 percent doesn't shrink as AI improves. It was never really about processing claims, but about exercising judgment on people’s behalf. The best billing operations don’t ask AI to replace that judgment; they use it to free up the time to exercise it well.
My concern is not that AI will replace people. It is that organizations may assume an automated process is reliable simply because it is fast. A system can produce an answer without necessarily making clear how that answer was reached, what information may have been missed, or who is responsible when something goes wrong.
In healthcare reimbursement, those questions matter.
Building Oversight Into the Process
I came to this view partly through examining billing operations where a physician had placed understandable trust in the people managing the process. The issue was not that trust was misplaced. The issue was that a busy doctor could not realistically review every denial, rejection, unpaid account, and administrative detail while also focusing on patient care.
That experience reinforced for me that oversight has to be built into the process rather than left to someone to perform only when a problem becomes visible.
In my experience, one of the biggest risks is focusing too heavily on the final collection figure. A report may show how much money came in, but that number alone does not tell a provider whether claims were missed, denials were left unresolved, accounts receivable are aging, or the same workflow problem is recurring.
I believe providers should understand the process behind the result rather than treating the result as the whole story.
Keeping Humans Involved
That same principle should guide the use of AI. I would rather see technology handle the predictable work while experienced people focus on exceptions, denials, unresolved receivables, and situations that require interpretation. Humans in the loop should not be treated as a backup plan. They should be part of the end-to-end cycle.
For providers evaluating a billing system or partner, I believe the questions should go beyond headline metrics. How are results achieved? Which performance indicators are being monitored? What happens when automation cannot resolve a case? Who reviews the system’s work when an output looks incomplete or incorrect?
Healthcare providers do not need to choose between people and technology. They need a model in which each is used for what it does best. AI can help make routine work more efficient, but judgment, context, nondeterministic variables, and accountability still need human oversight.
If providers want a more sustainable reimbursement process, they should look beyond the numbers and insist on visibility into how those numbers are produced. In my view, efficiency is valuable only when it comes with responsibility.
Venkata Nuli is co-owner of AAA Medical Billing, which provides medical-billing and revenue-cycle-management services to healthcare practices. His work focuses on claims operations, denials and the use of automation in reimbursement workflows.
The views expressed in this article are the author's own.
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