The traditional entry point into revenue cycle is getting harder to find.
As AI and automation take over more repetitive work, health systems are hiring fewer people to perform manual data entry, basic coding, claim status checks and other transactional tasks that once served as the training ground for future revenue cycle leaders.
Several executives at Becker’s 11th Annual Health IT + Revenue Cycle Conference in Chicago said those jobs are not disappearing overnight, but the direction of travel is becoming clearer. The entry-level workforce is getting smaller, the work is becoming more complex and health systems are increasingly looking for analytical, technical and critical-thinking skills earlier in employees’ careers.
That shift creates a paradox for revenue cycle leaders. Automation may solve workforce shortages and reduce administrative burden, but it is also eliminating some of the work through which employees historically learned the business.
The transactional work is going away
Rebecca Ashe, director of finance education and quality assurance at Tampa, Fla.-based Moffitt Cancer Center, said her organization has already seen the entry-level pool begin to contract as automation changes the work.
“The pool of entry-level positions is getting smaller,” Ms. Ashe said. “But there’s also more complexity and people like having the change and being challenged, whether it’s within their current scope or for them to have a career ladder.”
Ankit Rohatgi, MD, vice president of utilization management and physician advisory at Cincinnati-based UC Health, was more direct about where the work is headed.
“The low-volume work is getting eliminated. Inevitably, that’s going to happen,” Dr. Rohatgi said. As a result, he said data literacy is becoming increasingly important for new hires, who will need to understand dashboards, identify anomalies and recognize when technology produces a bad result.
That mirrors what other health systems are seeing. Rochester, Minn.-based Mayo Clinic has largely stopped recruiting for some entry-level manual data-entry roles, with early-career hires instead being directed toward work such as prompt engineering, analytics and data storytelling.
“It’s going to be harder and harder to find the talent if we don’t evolve through automation and AI and using our EHR the way it’s designed,” Nikki Harper, chair of revenue cycle-analytics, automation and diversified revenue at Mayo, said. “So for us to be able to recruit, we have to make it something that they’re interested in.”
In practice, she said, that has meant building fellowship and internship programs and a “citizen developer” track that puts early career hires to work on prompt engineering and data storytelling rather than claim status lookups.
The shift is also visible higher up the revenue cycle. Leaders at UC Davis Health have described moving coders away from repetitive areas where AI is mature and toward more complex coding and audit work, while other systems are using AI for coding assistance, denial management and appeals.
Brandon Burnett, chief RCM officer at FinThrive, said the technology is increasingly filling gaps health systems may struggle to staff anyway.
“We’re being forced to use technology because we can’t find talent, and the technology is in some ways replacing many of those functions,” he said.
Miguel Vigo IV, chief revenue officer at UC San Diego Health, said the issue is not simply about reducing headcount. Health systems increasingly have to plan around the possibility that the same candidate pool will not exist five years from now.
“The technology investment is not to diminish the workforce,” he said. “It’s really to protect the fact that when I ring this bell in five years, I don’t know what I’m going to get.”
If the first rung disappears, what comes next?
For decades, repetitive work gave revenue cycle employees thousands of opportunities to learn payer rules, workflows, exceptions and failure points before advancing into more complex positions.
AI is disrupting that progression.
“There is no longer this journey where you start as a junior employee and you have all these repetitive tasks that actually teach you how to do your job,” Beth Hand, interim vice president of care delivery applications at Oklahoma City-based OU Health, said.
Ms. Hand said health systems will still need mentorship, coaching and preceptorship even when AI is handling the underlying repetitive tasks.
“There are certain things in career ladders that can’t be replaced,” she said.
Jennifer Armendariz, vice president of managed care and revenue cycle at Madera, Calif.-based Valley Children’s Healthcare, raised the same concern as automation takes over more entry-level work.
“How do we maintain expertise in the workflows that we build out for AI and automation into the future?” she said.
Her answer includes more deliberate training, job shadowing and documentation of the work being automated so employees understand the underlying process rather than simply supervising a black box.
“I think it’s critical that we are documenting our workflows,” Ms. Armendariz said. “Everything that goes into AI, everything that is automated, should be very clearly documented and stored.”
Gary Zimmer, MD, chief medical officer of Utica, N.Y.-based Mohawk Valley Health System, said he worries that the risk of losing that foundational knowledge may actually be understated.
“If we don’t have the next generation coming up learning how to think and how to understand and they don’t understand the underlying methodology, you’ll reach a point where people aren’t going to question what they’re seeing,” Dr. Zimmer said. “When that happens, then the trust but verify goes away.”
The issue is increasingly becoming a succession-planning problem. In a recent interview with Becker’s, Solventum health information systems CTO Hari Balasubramanian said future leaders may replace years of manual transactions with earlier exposure to exception management, analytics, process optimization and AI oversight.
“The traditional training ground isn’t disappearing, but it is evolving,” Mr. Balasubramanian said. “Instead of spending years manually processing transactions, future leaders will learn the revenue cycle through exception management, analytics, process optimization and AI oversight — with AI transparency allowing them to clearly examine and understand how AI was applied. The opportunity is to expose emerging talent to a broader view of revenue integrity much earlier in their careers and accelerate their learning.”
