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Special Report: Faster Analytics at University of Michigan Health System

July 20, 2016
by Rajiv Leventhal
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Editor’s Note: Healthcare Informatics has compiled together a five-story Special Report section on data analytics for its July/August print issue. This story, and one posted online yesterday about provider organizations making strides in their data analytics work, are part of that special section.

As Healthcare Informatics reported as part of its Up and Coming health IT vendors’ section last issue, many data analytics companies have recently gained momentum because they address a pain point in the provider space. But other vendors in this segment, such as Seattle-based Tableau, featured in last month’s Healthcare Informatics 100 issue, offer data visualization products with a focus on business intelligence (BI). One of Tableau’s health system clients is the University of Michigan (U-M) Health System, which has a Fast Analytics team whose job is to crunch data and assist more than 30 groups across the health system—which has three hospitals, 40 outpatient hospitals, and more than 140 clinics—with their dashboards.

Jonathan Greenberg, director of the five-member Fast Analytics team at U-M Health System, says the team’s core question that it asks itself over and over is, “How do we do reporting and analytics here better?” To that end, the Fast Analytics team has a key philosophy around three major groups: skills, process, and tools, Greenberg says. “By fine tuning that triangle, based on the staff you have, you can come up with a successful environment to improve reporting and information decision making, which was our main goal.”

Jonathan Greenberg

About five years ago, when the 990-bed U-M Health System was looking for analytics tools, Greenberg’s analytics team was being carved out from the organization’s central IT team; it was mostly focused on professional billing, he notes. “We were the red-headed stepchild in a medical school IT shop. We were managing the billing system and things like RVU [relative value unit] recording, and all recordings surrounding billing.”

Indeed, at that time, the analytics team at the U-M Health System managed a large and outdated billing system. To produce reports, they either had to do the coding themselves or pay large fees to an outside vendor, and the final output was often late or incorrect. “We had to fight our way through all kinds of vendor interactions in order to have the right to pay huge sums of money for report alterations, and it wasn’t a cost effective way of reporting. Also, it wouldn’t let us see the next layer of reporting in our organization,” Greenberg notes. At the same time, the health system was looking at Epic, he adds. “I was very involved in those initial stages, and I realized there was a huge missing reporting and analytics component there too. And so everything kept coming back to Tableau,” he says, specifically noting his organization’s capability to leverage the vendor’s automation and data visualization techniques. Greenberg said that throughout the whole vendor selection process, U-M Health System looked at 14 different packages spanned across an 18-month period.

Now, Greenberg points to having the ability to find outliers and being able to do multi-dimensional outlier searchers so the analytics team can zoom in on characteristics. He gives an example of such characteristics that are causing doctors to be margin negative rather than margin positive on the same procedure. “It’s about helping to answer questions like that by finding trends and outliers, but more important than that, it’s giving people a simple and intuitive way to drill down into the data,” he says. “If we build the dashboards right, the menu structures and everything else are intuitive, and people know how to use the tool without much training. That is so incredible,” he says. “Doctors out there don’t have time; if they wait more than seven seconds for a page to open, they’re gone.”