American hospitals are racing to transform how they handle money—and they're turning to artificial intelligence to do it. A quarter of health system financial leaders have already scaled AI beyond experimental pilots, according to a new survey by the Healthcare Financial Management Association, with nearly half of all respondents still testing AI in select corners of their revenue cycle operations.
The stakes are enormous. The U.S. revenue cycle management market—the complex machinery hospitals use to bill patients, collect payments, and manage claims—currently totals about $90.6 billion. By 2030, that figure is projected to balloon to nearly $308 billion. But AI offers a chance to bend that curve. According to McKinsey research cited in the HFMA survey, AI could cut the cost to collect by 30 to 60 percent, while improving payment accuracy and freeing staff to focus on higher-value work and patient care.
The survey, which gathered input from 95 finance professionals at hospitals of all sizes, reveals where the biggest transformations are taking place. More than a quarter of respondents expect AI to reshape mid-cycle operations—the grinding daily work of claims processing and denial management. Smaller shares anticipate major changes on the front end (7.4%) or back end (16.8%) of the revenue cycle. The strategy most executives are pursuing is a blend of buying AI solutions, building in-house, or partnering with outside vendors.
Yet the transition isn't seamless. Sixty-three percent of surveyed leaders say their workforce is only somewhat prepared for the skills the revenue cycle will demand in the next five years. "It's kind of like someone wanting to become a blacksmith for horseshoes when you see the Model-T coming out of the factories," said Dr. Gerard Brogan, senior vice president and chief revenue officer at Northwell Health, one of the nation's largest hospital systems. "Will you be able to find the workforce to do this work?" The workforce anxiety is real: hospitals are already struggling to attract people into medical coding roles as autonomous coding tools gain traction.
Financial leaders also harbor specific fears about AI deployment. A quarter worry that AI errors could be scaled faster than humans can catch them. An equal share fret about growing dependency on external technology partners. Close behind, at 21 percent, is anxiety over workforce disruption and morale, while 18.9 percent cite regulatory or compliance risks.
Perhaps most pressing is the unresolved tension between hospitals and insurance companies. "We just want to be paid for the services we're doing," said Candice Powers, chief revenue officer at USA Health, who participated in the survey. "There needs to be better collaboration across all stakeholders. The patient is getting lost in this." Powers sees AI as part of the answer—a way to "eliminate the costs baked into the model"—but technology alone cannot heal the deeper friction between payers and providers.
The HFMA will discuss these challenges at its annual conference, being held June 7–10 in National Harbor, Maryland. As hospitals deploy AI at scale, they face not just technical obstacles but human ones: retraining workers, managing fears, and maintaining focus on the patient amid a sea of data and algorithms.