Sanford Health's Dane Hudelson, a participant in the HealthLeaders Mastermind program on AI in revenue cycle and finance, says his team of data analysts and tech experts does the behind-the-scenes work that improves all aspects of the health system.
AI programs for revenue cycle and financial operations may not directly affect a healthcare organization's clinical outcomes, but they do play a crucial role in creating a healthy health system. And that'll make everyone smile.
"When you have nurses that are spending the first week of every month, putting schedules together, creating Excel documents, that's not what they signed up for," says Dane Hudelson, enterprise director of data & analytics at Sanford Health. "So when we're able to kind of step in and say, ‘You know, let us help you take care of that, we'll handle it digitally and give it back to you in a couple hours and you go back to doing what you enjoy doing.' … My employees definitely enjoy … knowing that they're impacting patient care."
Hudelson, a participant in the HealthLeaders Mastermind program on the use of AI in revenue cycles and financial operations, spoke during a HealthLeaders podcast this week on Sanford Health's AI strategy. He noted that the South Dakota-based health system, the largest rural heath network in the country, developed its AI strategy in-house, drawing from an enterprise data and analytics team created in 2015.
"We are well versed," he noted. "We create tools, but also have the foundational knowledge and unique understanding of the patients we serve, so our projects are tailored to Sanford patients and Sanford as an organization."
Their first AI project, which went live some 18 months ago, focused on intelligent automation scripting.
"With a little over 160 facilities, our first project was aligning with their operational groups to hopefully make life a little easier on some of the processes that they were having to do for each of their facilities for the month-end closing process," Hudelson said. This "historically required a team of five individuals that would invest, give or take, 80 to 100 hours in some instances, even a little bit longer."
The solution, a "digital employee" leveraged on the Python platform, saves revenue cycle staff at least 100 hours per month—the key metric used in determining the value of an AI-based rev cycle/finance tool. Saved time, Hudelson said, translates to reduced expenses and more efficient processes as rev cycle staff can focus on "recurring processes that are inefficient, time-consuming [and] labor-intensive."
Like, say, creating a more efficient way for nurses to schedule their shifts.
Sanford Health has launched roughly 20 rev cycle AI programs over the past 18 months, Hudelson said, saving the health system roughly 7,500 hours of manual labor.
Hudelson takes great pride in his enterprise data & analytics team, which comprises some 70 people—including mathematics experts, Epic-certified report developers, statisticians, even a research nuclear physicist and some psychology folks.
"A lot of these kids--I call them my kids-- enjoy doing this because they can understand we might not be a patient-facing department, but they understand," he said. ‘And it's very easy for them to see how their work impacts patients on a day-to-day basis, whether we're dealing with nurse schedules or one of the other projects that's not so much rev cycle oriented as it is [making people more efficient at what they do.]"
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Eric Wicklund is the associate content manager and senior editor for Innovation at HealthLeaders.
KEY TAKEAWAYS
Sanford Health launched its enterprise data & analytics department in 2015, and was able to stay in-house to develop an AI governance strategy.
The health system has launched roughly 20 AI programs in rev cycle and finance operations over the past 18 months, saving Sanford at least 7,500 hours of manual work.
Hudelson says his team knows they’re doing non-clinical work, but those programs are helping to improve workflows for clinical staff and giving doctors and nurses more time to do what they love: Treating patients.