Return on investment doesn’t just have to impact revenue.
The capabilities and evolving potential of AI may seem like a remedy for revenue cycle optimization, but it isn’t that simple.
When judging a vendor’s proposal, Shannan Bolton considers the following: What is the current state of revenue cycle operations, what are the challenges she’s wanting to address, and how would the solution being presented support the system’s efforts.
Bolton, vice president of revenue cycle optimization for Stanford Health and a participant in the HealthLeaders Mastermind program on AI in revenue cycle and finance operations, also notes that the return on investment doesn’t have to impact revenue, but simply be beneficial to the system.
“[Let’s say] there’s a tool that we have in place, but its [performance] is stagnant. I’m looking for continuous optimization,” Bolton said. “So, I’d look at it holistically: What are they offering or doing for other organizations that our current vendor hasn’t thought of or isn’t moving towards even if the changes are small or in a focused area.”
After market research, deciding on a vendor, and signing on the dotted line, you’re ready to begin the implementation process, which is where the real work begins.
At this stage, a service line agreement with the vendor will detail expected deliverables and timelines, from the official launch of the solution.
Bolton emphasizes the importance of SLAs as an accountability measure for both health systems and vendors.
“If we’re not able to implement [it] on time, and it’s because [the vendor’s] team wasn’t ready to go, then maybe our first payment doesn’t start till six weeks later than we planned,” she explained. “These are the commitments that I’m going to build into those service line agreements.”
A common misconception around AI is that it is self-sufficient once implemented, but there are limitations to the technology which require oversight. For example, Bolton notes that most AI solutions manage simpler tasks, but not middle revenue cycle tasks that require more detail and clinical knowledge.
“That space becomes more complex and the rules can change often by payer, location, or specialty,” she said.
Technology managing the simpler, repetitive tasks leave staff available to handle more complex tasks, like denials management. However, you can’t successfully implement a new solution without staff support, and leaders must be open and transparent in their conversations and messaging.
“We want our staff to continuously perform at the top of their scale.” Bolton said. “This means proactively developing the staff to upskill them once we bring in AI to perform that more simplistic work.”
Whenever the system starts a project related to AI or automation, they begin by assessing the current staff’s experience and expertise. This allows leaders to repurpose staff by placing them in areas where their skills are needed, provide additional development opportunities to those wanting to move to the next level or empower staff to make other career decisions that better align with their current skills and goals.
“From a humanity standpoint, it’s so important,” Bolton said. “Making sure that the staff know we have their best interests at heart, that we’re going to develop you, support your career development, even if that means it’s not in this organization.”
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