What is AI in healthcare revenue cycle?

Improving revenue cycle operations ranks as a top three strategic goal for healthcare leaders. It’s one reason why hospitals and health systems are increasingly investing in AI in the revenue cycle to strengthen performance.

“AI is quickly becoming foundational revenue cycle infrastructure,” one healthcare finance leader shared in response to an HFMA survey. “It’s changing how performance is measured.”

In fact, two out of three healthcare providers believe AI could improve the healthcare claims process, an Experian survey found. This process determines how healthcare providers are paid and how quickly payment is received.

And 69% of providers say that applying AI to revenue cycle has helped reduce claim denials and increase the likelihood that a denial will be overturned, the same survey indicates.

Understanding how AI is applied to revenue cycle

An HFMA article outlined a few of the reasons how AI is being deployed in the healthcare revenue cycle, the department that ensures hospitals and health systems are paid for the care and services they deliver. They include:

• “Situation response AI,” which helps guide staff actions to help avoid claim denials and increase the chances of appealing a denied claim
• Combatting medical necessity denials, which occur when a health plan believes the care or services delivered weren’t medically necessary
• Fast-tracking appeals by automating the writing of appeal letters
• Helping to prevent denials, such as by reviewing payer rules and using those rules to support a medical diagnosis
• Automating prior authorization, which health plans require for some types of care

AI can even help correct missing or incorrect patient information in real time, increasing the chances of a clean claim.

AI transformation in revenue cycle is happening quickly

HFMA’s Revenue Cycle of the Future report points to trends that are shaping how AI is applied to the healthcare revenue cycle.

For example, the HFMA survey referenced above indicates AI pilots are moving to production-scale deployments across the revenue cycle. Among 95 healthcare finance professionals surveyed by HFMA, 27% say their organizations are actively deploying AI at scale across multiple functions, and 53% are conducting pilots in select areas. 

And while the U.S. revenue cycle management (RCM) market totals about $90.6 billion today, according to industry estimates, by 2030, it is projected to reach nearly $308 billion.

Over time, experts predict providers  will use fewer and broader vendors for AI. This will allow providers to gain economies of scale. It will also help limit the amount of data shared with outside companies given concerns about cybersecurity.  

Where can I find out more about AI in the healthcare revenue cycle?

HFMA offers the following resources on AI in the revenue cycle:

• AI moves beyond revenue cycle
• Revenue cycle AI demands a tougher set of questions
• The Revenue Cycle of the Future: AI boom and workflow redesigns accelerate rev cycle transformation

FAQs

What are some of the biggest challenges related to deploying AI in the healthcare revenue cycle?

Some of the biggest challenges related to AI deployment in the revenue cycle include change management, or making sure that staff understand how AI will be used, why it’s necessary and the impact it could have on human team members. Other challenges include changes to workflows to incorporate AI effectively and operating model transformation.

How accurate is AI in the revenue cycle?

Experts at HFMA’s Annual Conference in June 2026 indicated AI models now are capable of delivering high levels of accuracy and reliability on the majority of revenue cycle tasks and decisions. This is considered a pivotal moment for the industry after 20 years of incremental progress.

What are overlooked factors for AI success in revenue cycle?

One of the biggest overlooked keys to AI success is the need to build trust between healthcare revenue cycle leaders and staff regarding how AI will be used. One of the lessons learned shared during HFMA’s 2026 Annual Conference was that teams need to see that AI performs at least as well as humans on tasks while delivering time savings before they will fully embrace it as part of their workflow.

How could AI improve payer-provider communication?

One of the ways that AI-to-AI payer-provider communication could transform the revenue cycle is by speeding up decision-making. For example, instead of two people talking on the phone for 30 minutes about a denial, two AI agents could exchange the same information programmatically in seconds. However, experts interviewed by HFMA caution, “We’re not there yet.”

Technology and revenue cycle management resources

Expand Your Knowledge with E-Learning

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Advance your revenue cycle career: Certified Revenue Cycle Representative (CRCR)

This program validates your knowledge and expertise in revenue cycle management, setting you apart in a competitive industry.

MAP Keys and MAP Award

MAP Keys are publicly-available industry-standard metrics or KPIs used to track your organization’s revenue cycle performance using objective, consistent calculations. 

HFMA’s MAP Award celebrates revenue cycle excellence in five categories: hospitals, critical access hospitals, hospital systems, integrated delivery systems, and physician practices. Winners excel in meeting industry standard revenue cycle benchmarks (MAP Keys), implementing the patient-centered recommendations and best practices embodied in HFMA’s Healthcare Dollars & Sense™ initiatives, and achieving outstanding patient satisfaction.

Revenue cycle management event

Hundreds of attendees come together every year at HFMA’s Revenue Cycle Conference to share strategies that improve revenue cycle performance. 

Revenue cycle forum

HFMA Community’s Revenue Cycle Forum is where revenue cycle VPs, directors, managers and supporting staff focus on exploring, sharing and solving revenue cycle issues. 

Healthcare finance jobs

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