AI Voice Agents Can Handle Patient Billing Questions—But Only If They’re Purpose-Built

Patient billing questions are some of the hardest calls a health system handles. They’re high-stakes, emotionally charged, and often deceptively complex. And with 60% of hospital expenses tied up in labor, these calls aren’t just stressful—they’re costly.

That’s why revenue cycle leaders are turning to AI voice agents. The pressure to cut costs is growing—thin margins, economic uncertainty, and looming tariffs aren’t helping—and the billing call center represents a high-ROI opportunity. They’re essential for collections, expensive to operate, and increasingly overloaded as more patients shoulder the cost of care.

But here’s the catch: not just any AI will do. Choosing the wrong solution doesn’t just waste your investment—it creates more confusion, more cost, and more cleanup for your team. While big-name tech players are transforming customer service in retail, banking, and hospitality, there’s a reason these companies aren’t focusing their AI on patient billing support.

What makes patient billing different

In most consumer industries, support calls are simple: the customer knows exactly what they need—track an order, cancel a subscription, process a refund. The agent follows a script and maybe tries to upsell the customer something else.

Calling the hospital billing office? Totally different story.

Most callers don’t know how to explain what’s wrong—just that something feels wrong. “Why is my bill so high?” is often the starting point, not the actual problem. That means the agent has to do the heavy lifting—dig through account details, connect the dots, field follow-up questions, and somehow keep the conversation on track.

And let’s not forget: the patient on the line might be stressed, confused, or still recovering from care. They don’t have patience for jargon, long holds, or getting bounced from one agent to another. One bad answer can mean callbacks, complaints—or worse, broken trust at a time when it’s already dangerously low.

One call that can break your AI

Take something that seems straightforward: a patient doesn’t see their bill in the payment portal after receiving a statement in the mail.

When we dug into 13,000 real billing calls, we found this single inquiry rarely has a single answer. The invoice might have been withdrawn. It could’ve been adjusted after being resubmitted to insurance. Maybe it’s been paid. Or perhaps—and this is where things get tricky—it went to collections.

You’d think the next move would be simple: if the bill is in collections, tell the patient and give them the agency’s contact info, right?

Not necessarily. In our analysis, 11% of callers had at least one bill in collections, but agents only provided collection agency information in 37% of these cases. The reason? Experienced agents know when this information helps and when it doesn’t.

Missing this kind of nuance leads to poor patient experiences. Case in point: when we tested a basic “if collections, then provide collections info” approach when building our own AI voice agent, 7 in 10 of these patients would have been sent down entirely wrong resolution paths. That’s a lot of noise—and a lot of wasted time for everyone involved.

The true cost of a wrong answer

The problems begin long before a patient gets sent down the wrong path. Getting even a generic AI tool off the ground in healthcare billing is rarely turnkey. Revenue cycle call centers require deep configuration—resolution logic, controls and guardrails, conversation paths tuned to billing nuance.

And who owns that lift when the vendor lacks domain expertise? The very people you’re trying to relieve—call center teams, revenue cycle operations. So instead of reducing cost, you’ve just shifted it. And probably added new ones.

When AI gets it wrong, the burden doesn’t just fall back on humans—it multiplies. More follow-up calls. More confusion. More dollars spent on training, staffing, and drawn-out resolutions. And over time, it adds to burnout in roles already prone to high turnover.

There’s also a cost to patient loyalty. One frustrating experience can damage trust—and send patients elsewhere the next time they need care.

So what does good look like?

Truly effective AI in healthcare billing doesn’t just sound human. It thinks like a human call center agent—combining communication skill, billing expertise, and operational awareness to resolve complex issues in real time.

That means:

• Recognizing the complexity behind each question: Purpose-built AI understands this—and is designed to investigate, not just respond. It needs the logic and data structure to uncover root causes, and surface the right next step and resolution.
  
• Understanding how to communicate with patients: Patients often call in a heightened state. AI should know how to meet them there. For example, our research found that while patients expect empathy from AI, too much can feel fake—even coercive.
  
• Structuring data in a way AI can actually use: Billing data is messy. Domain expertise means knowing what matters and how to organize it so AI can reason through it—and what not to include to avoid overwhelming it and minimize hallucinations.
  
• Using evaluation as an engine—not a checkbox: The best AI systems leverage billing experts to tag and refine datasets, simulate complex scenarios before launching new use cases, and use those insights to continuously tune and improve performance.
  
• Accounting for the full billing ecosystem: One bill might involve a hospital, an ancillary provider, an insurance company, and a financial assistance program. AI needs to be built with awareness of those handoffs, policies, and resolution pathways.

Bottom line

AI can absolutely move the needle in patient billing support—but only if it’s purpose-built for the job. That means domain expertise baked in from day one, not bolted on after the fact. In a space where every interaction matters—for cost, for experience, for trust—there’s no room for shortcuts.

The next generation of billing support demands solutions that understand not just what patients are asking, but what they actually need. It requires AI that can navigate billing complexity while preserving the human element patients expect.

If a generic AI voice agent can’t handle even the “easy” calls—like helping a patient with a bill in collections—it’s not ready for healthcare. And in this moment, ready matters.

Ready to see what good looks like? Meet Kora—the AI voice agent built specifically for patient billing support. Kora powers your frontline operations, guiding patients through the nuances of their bills to deliver resolutions. She saves valuable time (and headspace) for your team, too.

Ben Kraus is Director, Content Marketing at Cedar