To those who don’t work in healthcare (and even some who do), the revenue cycle can be a mysterious back-office function. In reality, the revenue cycle is the lifeline of any hospital.
When the revenue cycle functions smoothly, healthcare services are coded, documented, billed and processed appropriately, and the hospital receives proper payment. When the function is inefficiently managed, revenue is often delayed, deficient or outright denied, which can significantly impact the hospital’s financial stability.
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New approaches and tools to improve revenue cycle management (RCM) are always needed to safeguard a hospital’s financial foundation. In the past few years alone, more and more hospitals and health systems have turned to artificial intelligence (AI) to improve their revenue cycles.
What Is Artificial Intelligence?
Artificial intelligence is technology that uses large amounts of available data to enable computer systems to perform tasks with informed processes and automation. Unlike traditional software that requires explicit programming for each function, AI systems can make decisions, solve problems and learn from experience.
Applications of AI have grown considerably in the past few years. AI is what powers everything from computer-assisted word processing to voice-activated computer assistants and self-driving vehicles.
In healthcare, almost 40 percent of health systems surveyed in 2025 by the Healthcare Financial Management Association (HFMA), are either piloting or implementing generative AI tools, which create output or content, in their RCM. (“The Revenue Cycle Intelligence Report,” December 2025).
How is AI Used in the Revenue Cycle?
AI can perform a number of functions that were once only the domain of human expertise. The HFMA survey reports that finance leaders in general view the greatest potential for AI in resolving gaps in documentation.
For smaller hospitals, such as rural and critical access hospitals, one of AI’s key applications is automating some tasks within claims processing, says Samantha Geroli, CRCR, CPCT/A, OTC, Manager, Extended Business Office (EBO) | RCM Client Services at JTS Health Partners (JTS).
“Artificial intelligence is going to be super helpful in taking over and potentially improving manual, repetitive tasks and take a lot of lower-level work off the human revenue cycle team,” she says.
The manual process for managing claims can be time-consuming with a lot of back-and-forth interaction between hospital revenue cycle staff and payors to monitor the status of claims. Geroli notes that a claim can take as long as 60 to 90 days to be processed—meaning the payment to the hospital is delayed as long as a month and a half after it has provided services. When denials are issued from the payor or a mistake is made in the processing, the payment is delayed much longer.
AI can automate the processing of claims by communicating directly with payors—either through the payor’s own computer system or interacting with human staff—to check whether the claim is processing correctly, Geroli explains.
“I’ve listened to a several recorded calls between an AI team member and a payor team member, and the conversation is almost like another human on the line rather than a machine. Sometimes, the conversation is AI team member to AI payor team member, which is incredibly effective as well,” Geroli says.
If there is a hold up with a claim, the issue can be quickly flagged for human attention, instead of waiting for the issue to be identified through manual processes.
Geroli adds that AI can also identify accounts with higher balances and prioritize them for more timely collection, resulting in a better financial outcome.
Other functions of AI include identifying reoccurring errors within claims processes and supporting appeal of denials, says Randal Wynn, PMP, who is the Director of Client Advisory and Consulting services for JTS.
“Currently, artificial intelligence is production oriented. It’s helping to improve productivity by reducing rework inside Revenue Cycle,” he says. In the near future, AI will be used to optimize process paths through higher-level strategic workflows, resulting in the identification of time delays and process failures that are not easily recognized today due to siloed systems and distributed teams involved across the life cycle of the claim.
The Benefits of AI in Revenue Cycle Management
According to the HFMA survey, leaders view generative AI as a way to improve quality, accuracy and collaboration across the revenue cycle. Mid-cycle tasks that topped a list of those that leaders believe AI tools could most effectively improve include:
- Identifying missed reimbursement opportunities—60% of respondents selected this task
- Uncovering gaps in clinical documentation—57% of respondents selected this task
- Reducing coding-based denials—43% of respondents selected this task
For smaller hospitals that might be just beginning to use AI, the technology offers the appeal of greater efficiency.
“Rural and critical access hospitals often operate on very tight budgets, limiting their ability to staff sufficiently, meaning revenue cycle employees often perform multiple functions,” Wynn says.
This is where AI can step in to enhance efficiency. By handling routine, repetitive tasks, AI enables revenue cycle staff to redirect their time and expertise toward higher-level work—such as prioritizing high-dollar accounts—that can generate the greatest financial benefit for the hospital, he says.
If they’re not handling routine processing tasks, revenue cycle staff will also have more time to work directly with insurance companies to resolve issues with claims and secure payment faster.
AI Does Have Drawbacks
Although AI holds great promise for improving RCM, many hospitals face obstacles to implementing the technology.
“I think the biggest drawback is the high entry cost,” Wynn says.
AI technology can cost tens of thousands dollars for just single task tools, and the tools require technological expertise for planning, implementation and maintenance—challenges that can be formidable to small and rural hospitals, especially.
Additionally, the strength of AI tools are based on the integrity of the hospital’s data that AI uses to perform its tasks and make decisions. The technology must work within existing workflows in the hospital’s revenue cycle, which may need optimization before incorporating the new technology.
“Incomplete data and inconsistent workflows, which small hospitals often struggle with, will hamper the effectiveness of the technology,” Wynn explains.
Wynn adds that another challenge is mindset. Many of those in healthcare, even leadership, are cautious about AI adoption and its impact on their workforce. “In small communities, hospitals are often one of the largest, if not the largest, employer. AI can be viewed as a threat to the livelihoods of employees who are also community members,” he says.
The Value of AI in Revenue Cycle Management
Automating claims processing, for example, can offset some of the costs of the technology by ensuring that claims are processed faster, meaning hospitals are paid sooner, Geroli says. Identifying and correcting errors on claims may also reduce the amount of claims denials. The argument of “AI versus human” is not really useful. Typically, the payment for many accounts is lost because there is simply not enough human capacity to handle the range of claim variations. Adding AI may enhance the work of human staff rather than eliminate it, and would enable greater financial return for the hospital by supporting positive resolution of many more, or even all, claims denials.
Because AI can automatically target accounts with higher outstanding balances or large workload in low balance accounts, there is the potential to improve cash collections further, as well.
Geroli points out that what’s important to remember in the midst of AI’s popularity is that machines can’t perform all the work of the revenue cycle. A claim represents a patient encounter, which has varied circumstances, and so there will always be cases that require human intervention.
“Because there are so many working parts to these cases, their resolution requires experience and specialized skill that humans have.”
Managing some denials, for example, can be an intricate process of reviewing electronic medical records and other patient data to discern the cause of the denial and then work to resolve it. “Because there are so many working parts to these cases, their resolution requires experience and specialized skill that humans have,” she says.
Instead of a threat to jobs, Geroli says, AI should be considered an additional resource, an extra “automated” hand that is helping the hospital capture the revenue it deserves for the services it provides and secure greater financial stability.
Easing Into AI Adoption
Wynn suggests that one approach hospitals can take to adopting AI tools is limiting initial implementation to one or two applications. “Prioritizing one function will make it easier to evaluate the benefits of the technology in revenue cycle performance,” he says.
A first step for a hospital might be assessing gaps in its revenue cycle efficiency and evaluating which tools can address those limitations.
An AI technology provider can help a hospital, particularly those with limited technical expertise, with this evaluation and match the hospital’s needs with the best AI tool.
For small hospitals, Wynn says this might be an application that automates workflow in claims processing by flagging accounts that are past the average time it takes a payor to reimburse on an account. Revenue cycle staff can then focus on these accounts.
Wynn recommends that hospitals choose a vendor with deep experience in the healthcare space. Vendors who are currently providing expertise within hospitals and health systems will not only understand the complexities of healthcare, but also be familiar with the regulations governing AI technology, which can vary by type of hospital and from market to market, he says.
“Critical access hospitals, for example, have very specific rules for reimbursement, which can impact their return on investing in AI technology,” he explains.
An experienced healthcare services and technology provider will also be able to evaluate the hospital’s workflow process and how existing electronic health record systems will work with the new technology.
Once a hospital has measured a return on investment with a particular tool, it should then consider expanding the technology into other functions within its revenue cycle, Wynn says.
AI in Revenue Cycle Management: A Game Changer
Technology can be daunting. It can also be a game changer.
Threats to hospital revenue are only growing, and increasing charge rates will take organizations only so far, Wynn says. Identifying solutions that create long-term value—by enabling revenue cycle staff to work more efficiently and strategically—will be critical to securing a hospital’s financial future.
To do so, hospitals must thoroughly understand their revenue cycles and partner with healthcare services and technology providers who can help pinpoint the greatest return on investment. Equally important is approaching AI adoption thoughtfully, with clear goals, strong oversight and realistic expectations.
Maintaining the status quo risks falling behind.
“Hospitals and health systems of all sizes, not just the largest organizations,” Wynn comments, “must continue to innovate to remain financially resilient and be able to provide the quality care their communities deserve.”
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