What It Really Takes for AI Agents to Handle Patient Billing Calls

Calling the hospital billing office isn't like any other customer service experience. By the time patients reach out, they've waited weeks for a bill that's higher than expected, doesn't make sense, or isn’t something they can afford.

That difference matters more than many healthcare leaders realize, especially when evaluating AI agents for the call center. Hospitals are grappling with staffing shortages, supply cost inflation, and increasing payer denials, while Medicaid cuts and uncompensated care threaten to squeeze what's left of margins. There is zero room for error.

Why? Because the cost of an unsatisfactory answer is often an escalation or a callback, burdening the very teams providers are trying to relieve.

Bottom line: AI can’t meaningfully unlock cost savings or patient experience gains in billing support without deep domain expertise and the right underlying datasets. Our analysis of more than 20,000 real-world calls shows three reasons why.

1. Most patients aren't calling to pay—they're confused

A foundational step in any AI strategy is understanding why patients are calling in the first place. Without this insight, it's impossible to quantify automation opportunities and identify high-impact use cases.

Across three healthcare providers, we found that the vast majority of billing calls are confusion-driven. Just 5.7% were about making a payment. More common were calls about understanding balances (11.3%) or clarifying coverage (7.5%). These trends reflect today's reality of high-deductible health plans and complex plan designs.

Financial assistance calls averaged just 1.7%, but this is likely to grow as Medicaid reforms and expiring ACA subsidies drive more patients to seek help, often without knowing where to start.

But perhaps the biggest takeaway: these aren't transactional calls. They're navigation challenges. Agents act as human routers, connecting the dots across insurers, enrollment vendors, collection agencies, and more. As these calls grow in volume, the traditional approach—more people, more phone tree options—simply can't scale.

That's what separates purpose-built AI from the off-the-shelf chatbots: the ability to interpret payer benefits, eligibility rules, and financial assistance policies. Conversation skills matter—but knowing what to do with that information matters more.

2. Patients often present symptoms, not diagnoses

Understanding why patients call seems simple enough. But getting to those insights required extensive data processing. Because the truth is, patients don't call with those clear categories in mind.

In retail support, customers know exactly what they need. "Cancel my subscription." "Process this refund." "Track my order." They name the problem; the agent resolves it.

Healthcare billing couldn't be more different. Patients present symptoms, not diagnoses. "This doesn't look right." "I thought insurance was supposed to cover this." "Why do I owe so much?" They know something feels wrong, but can't quite put their finger on it.

That leaves agents responsible for translating vague concerns into actionable resolution paths. And the complexity behind those paths is staggering.

At one health system, we identified 71 different inquiry types, linked to 61 root cause groups, and resolved through 99 distinct intervention categories. This variation has real operational consequences: long onboarding cycles, knowledge gaps and retraining, and ultimately, burnout and high turnover.

For AI to handle billing inquiries, it has to diagnose problems from incomplete information. That means guiding patients through discovery while simultaneously analyzing account details to identify root causes, not just answering what patients think they're asking.

3. Being factually correct isn't the same as being helpful

That diagnostic complexity is only half the challenge. Once agents access a patient's account, they're looking at dozens of data points spanning multiple dates of service, billing cycles, and even years. The challenge isn't just finding the right information—it's knowing what to ignore while they're still figuring out what's wrong.

At one physician group, 11% of calls involved accounts with bills in collections. Yet, agents only mentioned collection agency contact details in 37% of those cases. The reason: they had to determine whether that information was actually relevant to the patient's concern.

Maybe they were calling about a recent invoice—or something entirely unrelated. When a patient says, "I have a question about my bill," the agent has to figure out which one, what the issue is, and whether collections information even applies.

Getting this filtering wrong is costly. Surface irrelevant information, and you've turned a quick billing question into an escalation. Miss relevant details, and you've failed to help the patient address their most pressing financial obligation.

What matters isn't how much data AI can access, but knowing which details to surface when.

The real test for AI in healthcare billing

Billing calls are tough, and they’re only getting tougher as more patients fall into coverage gaps or face underinsurance. That’s exactly why getting AI right matters more than ever.

Think about it: you wouldn’t put an untrained agent on the phone lines. You shouldn’t do that with AI either—especially not in a domain fraught with ambiguity, emotion, and judgment calls.

When AI meets a higher standard, providers actually get what they need: fewer calls reaching human agents, so they can focus on the cases requiring their expertise and humanity, while patients get clear answers fast.

That’s the kind of AI worth investing in.