A Reality Check on AI in Healthcare Finance

Your health system is likely facing a familiar squeeze. Margins are razor-thin, staff burnout is high, and the administrative cost of simply getting paid by payers and patients is climbing. As organizations look for ways to improve efficiency and reduce operational strain, it’s no surprise that AI has quickly become one of the biggest conversations in healthcare technology. In fact, you’ve likely been pitched “AI solutions” daily. Most of these promise a lot…and most of these are traditional automation or machine learning repackaged as something much more than what they are.

From where I sit as CarePayment’s Chief Information and Technology Officer, the gap between how AI is marketed and how it performs in operational reality is significant. Don’t get me wrong, there is real opportunity here – but also real hype. In our industry, confusing the two can introduce more risk than value.

My goal is to help cut through some of the noise around AI in healthcare finance and talk honestly about where I think the technology is helping, where it’s overhyped, and how healthcare leaders can better evaluate the claims being made in the market right now.

Defining the AI Conversation and Where It Gets Practical

Before getting into where AI is showing up across areas like revenue cycle, patient financial engagement, and operational workflows, it’s important to level-set on a few of the terms driving the current conversation.

A few terms are driving most of the current conversation:

Artificial Intelligence (AI): A broad category that includes systems designed to perform tasks that typically require human intelligence, often through pattern recognition and prediction.

Generative AI (GenAI): A subset of AI that creates content, such as summaries or responses based on learned patterns from large datasets. In healthcare finance, these tools are increasingly being used to help simplify communication, summarize complex documentation, and reduce repetitive administrative work that can slow teams down.

Large Language Models (LLMs): The engines behind GenAI tools. These models process and generate human-like language, enabling use cases like summarization, translation and question answering.

Across the market in healthcare, specifically within revenue cycle and patient financial engagement, I’m seeing a lot of vendors use these terms loosely to position their platforms as more advanced than they are. The result is sort of an unregulated Wild West where “AI-powered” can mean anything from basic automation to more sophisticated language-based tools. This lack of clarity is part of what makes this conversation difficult to navigate.

But if you can separate the technology itself from the way it’s being marketed, there are areas where AI is already proving genuinely useful. And interestingly enough, many of the strongest use cases aren’t the flashy ones being highlighted in pitch decks.

One great example is patient communication. A lot of hospital billing language isn’t written for the average person. GenAI is pretty good at helping translate dense banking jargon into something patients can understand. That alone can reduce confusion and cut down on a lot of frustrating back-and-forth for both patients and staff. Another area AI makes sense in is operational support. Things like helping teams navigate payer requirements, internal policies, or documentation-heavy processes without having to dig through hundreds of pages manually. It’s not flashy, but it saves time and removes a lot of unnecessary friction from day-to-day work.

In both use cases, the real value proposition for AI starts to show itself: AI is at its best when it removes the work that quietly wears teams down, not when it replaces people.

Separating the Claims from the Reality

With so much being positioned as “AI-powered” today, I’d encourage healthcare leaders to look past the claims and ask a few fundamental questions before bringing any solution into their workflows. In many cases, these answers tell you more than the actual pitch does.

Can the system explain how it arrived at its conclusions? If a patient is offered or denied, a financial option, can your team clearly trace the reasoning behind that decision?

What happens to our data and where does it go? Is patient and financial data fully contained within a secure environment, or is it being exposed to external models or training processes?

How are accuracy and bias monitored? What safeguards are in place to ensure outputs remain consistent, equitable, and aligned with your organization’s standards?

Where does human judgment remain in this process? Who is ultimately reviewing, validating, and approving outputs before they reach patients or payers?

Does this strengthen or weaken patient trust? At every point of financial interaction, is this technology making the experience clearer and more human, or more confusing and automated?

How We Think About AI at CarePayment

We’ve been very intentional about how we think about applying AI across the business. A human-first service model is something we’ve always valued at CarePayment, which is why I tend to view AI as a support layer, not a replacement for people or judgment.

In healthcare finance especially, I don’t think the goal should be to automate human interaction out of the experience. I think the goal should be to remove some of the operational friction around it. A lot of healthcare teams are buried in disconnected systems and manual processes that slow effective teams down. That’s where I think AI can actually be useful.

For us, this includes areas like simplifying communication, helping teams work through information more efficiently, reducing repetitive administrative tasks, or improving internal workflows behind the scenes. None of those use cases are particularly flashy, but they can make a meaningful difference in how teams operate day to day and how patients experience the financial side of care.

At the same time, you can’t have conversations around AI without acknowledging how quickly it’s evolving. The emerging challenge for leaders won’t just be deciding where AI can be applied, but knowing where it actually belongs, where it doesn’t, and how to approach both responsibly.

The Bottom Line

There’s no shortage of AI in healthcare finance right now. What’s in shorter supply is clarity.

The key, at least in my opinion, is cutting through that and staying focused on what actually works vs. what just sounds good in a pitch deck. In practice, the value tends to show up in pretty straightforward ways – making the day to day a little easier for your teams and making patient financing more accessible for patients. The harder part is knowing where to draw the line, especially when it comes to decisions that still need proper context and judgment.

More often than not, the value shows up in the basics, not the big promises. Focus on the practical, stay blunt about the limitations, and always prioritize the trust your patients place in your organization.