While many hospitals and health systems are zeroing in on expanding their use of AI, Jason Hinkle, CFO of Marion-based Deaconess Illinois, said some finance leaders are not focused enough on the work that determines whether these tools can succeed.
“Data governance is a strategy issue. It’s not an IT project,” Mr. Hinkle said during a recent episode of Becker’s “CFO and Revenue Cycle” podcast. “Fewer people talk about the less glamorous part about implementing AI, which is ensuring that the underlying data that feeds the AI models is accurate.”
Deaconess Illinois is a four-hospital system and an affiliate of Evansville, Ind.-based Deaconess Health System. Mr. Hinkle, who has been with the system for nearly a year, said the system puts a huge emphasis on data governance.
With ample amounts of data coming from an organization’s electronic medical record and its human resources systems, Mr. Hinkle said those systems often are not in sync. To combat this, Deaconess Illinois launched an enterprisewide data governance office, where every metric has a specific owner and clear process for how it is defined, calculated and maintained.
“We’re really working across the whole organization to align on what our definitions are, where the data is coming from and who’s actually responsible for maintaining that data,” he said. “It’s not just someone in IT or finance. This goes all the way down to the operational leader level, saying, ‘You’re the owner of this specific metric,’ and making sure we are validating that it’s accurate, it’s coming from the right systems and it’s all ultimately aggregating to the same data lake.”
Mr. Hinkle said the governance office gives the system consistency.
“Wherever you go in the organization, a given metric uses the same definition and is calculated the same way,” he said. “Leaders spend their time acting on the numbers instead of debating them.”
Mr. Hinkle added that the office also improves data accuracy and positions the system to expand its AI capabilities.
He said rolling forecasting is another underdiscussed topic in healthcare finance. He said a static budget is “becoming a bit archaic,” but systems have been slow to move away from it because repeating a three- to four-month budget cycle several times a year was too time-consuming. AI-enabled tools have since made that process less burdensome.
“So often we get into these month-end calls and [see] the same variances we had the previous month or the same ones we’re reporting against this month because ultimately something changed from our assumptions that were used in the original budget, and until we redo an annual budget next year, we’re going to have that variance,” he said. “[Rolling forecasting] just gives us a lot more visibility into where we actually think we’re going to land for the year.”
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