Top Trends in Automation, AI Adoption in Revenue Cycle

Where is the buzz?

Revenue cycle is still primarily comprised of manual processes, making the area ripe for technology adoption. But provider leaders seem to be focusing on some particular areas for automation adoption.

“KLAS has seen a lot of energy from providers in looking at automating spaces within claims management, specifically,” Day stated. “Within that area, you have claims status, denials, coding, and more, so providers are looking at autonomous coding, automating the patient estimate, and prior authorization technology.”

Prior authorizations, cost estimates, and other parts of claims management are major pain points within provider organizations. Automation can take some of the more repetitive and mundane tasks within claims management to free up qualified professionals to handle more complex responsibilities across the revenue cycle. For example, automated solutions can quickly check a claim’s status without much human intervention. In contrast, a manual process necessitates a staff member to call a payer or verify through a payer’s specific portal.

Providers are prioritizing operational optimization when it comes to technology investments within the revenue cycle, Chilton underscored. Where is the most administrative burden for revenue cycle leaders? That’s where automation will likely come to fruition within healthcare.

There is also an increased focus on automating these specific use cases over the last couple of years because of the loss of expertise due to turnover and other workforce challenges from the pandemic, Day added.

A survey from last year shows that most revenue cycle executives — 80 percent — said turnover in their department ranged from 11 to 40 percent while the national average at the time was 3.8 percent. An overwhelming majority of the executives also indicated a lack of qualified workers had a detrimental impact on organizational revenue channels.

“In the past, a revenue cycle leader may have solved a problem around claims or denials internally with the expertise they had in their own department, whereas now the expertise is getting diluted,” Day stated. “So, there is more of a reliance on, one, automation to help with the churn, and two, vendors to help with the expertise.”

“That expertise is still needed,” Day emphasized. “But turnover adds fuel to the fire.”

AI enters the revenue cycle chat

Any person interested in adopting technology in 2024 will encounter the term AI. AI is seemingly influencing everything from how we shop to how we brush our teeth. Healthcare is not different; AI has the potential to disrupt overly complex operations to streamline processes and return accurate results, something revenue cycle leaders are very interested in.

The explosion of ChatGPT has spurred energy around AI, according to Day and Chilton. The popularity of the generative AI chatbot and other AI technologies in healthcare has created a buzz, prompting providers to think of how AI can make day-to-day tasks easier. One major area of interest related to the revenue cycle is clinical documentation.

“Documentation won't go away, but AI will make it a lot easier and more efficient to hopefully reduce burnout for clinicians and then improve coding and other areas that rely on good documentation,” Chilton said. “So, there's a lot of energy there for sure.”

But AI adoption in the revenue cycle or healthcare is generally not full steam ahead.

Use cases for newer AI-enabled technology, like solutions leveraging generative AI, are simply lacking. There is a lot of optimism around what generative AI can do for the revenue cycle — writing letters for prior authorizations, medical appeals, and deals, as well as some post-procedural instructions for patients — but the real-time use cases are still being built.

“Overall, enthusiasm for AI is growing, but some people are still skeptical,” Day said. “Mainly, it depends on whether or not they have an established AI strategy or what type of organization you're talking to.”

A large health system, for example, may have a team of data scientists and the budget to adopt AI-enabled technologies and innovate within the revenue cycle. Meanwhile, a smaller, freestanding hospital in a rural area is likely trying to catch up with its digital transformation. After all, technology requires budget and people to monitor and train the AI, whether the provider organization or a vendor employs those people.

The barriers to automation, AI adoption

Provider organizations need the budget for technology investments, making cost the number one barrier to adopting automation and AI in the revenue cycle. But there are also internal cultural barriers, including concerns about overreliance on technology and gaps in in-house skills. So, providers not only need the money to implement and maintain technologies but also the human power to make them effective.

“There are similar challenges with your IT team's bandwidth,” Day explained. “Where does this technology investment fall on the priority list for the hospital based on what's happening with the IT team and what they're currently deploying? For example, if you're deploying an EMR, anything else will take a backseat because all of your technical expertise is being deployed in that EMR implementation. IT resources and staffing are important.”

Providers will have to consider the cost of deploying automation compared to building a solution themselves. Where is the balance, Day asked, and when does the technology become cheaper than the current solution that is in place?

Making the business case for technology investments can also be challenging when ROI is difficult to determine, an issue especially prevalent with revenue cycle technology.

“One of the reasons why it's difficult to determine the ROI is because some of these things aren't necessarily tangible,” Day stated. “You may not be replacing man-hours, but you're getting into different areas of revenue leakage because the man-hours that you replaced through automation are now focused on challenges that you couldn't get to because you didn't have the resources to get to them. It’s like operating at 50 percent before implementing automation, then going to 75 percent because more resources focus on more complex areas.”

A lot of the responsibility will fall on vendors to demonstrate the benefits of their technologies to providers, whether in traditional dollar amounts or increased capacity.

“There's a lot of energy, obviously, so it's going to be interesting to see in the next few years which vendors rise to the top and can prove that ROI or where consolidation happens because that always happens when there are new solutions,” Chilton stated.

“It's an exciting part of healthcare right now just to see how the technology can make a difference because I think this is one area that’s a little bit easier to justify using AI and other technologies, whereas there’s extra caution on the clinical side.”