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Moving Out of Its Emergent Phase, Healthcare Data Analytics Becomes More Real

March 29, 2016
by Heather Landi
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As healthcare organizations move further into data analytics work, what are they learning in the process?
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The healthcare analytics phenomenon has been a leading edge topic for several years, with exciting discussions among health IT leaders around the capabilities of Big Data and predictive analytics tools at virtually every industry conference and event. What’s more, the practice of data analytics has now grown out of its infancy stages as more providers move forward, and move more deeply into, the challenging work of setting up the data warehouses and business intelligence capabilities needed to support analytics.

As further proof of the growth of the healthcare analytics market, last month Watson Health, IBM’s artificial intelligence computer system, acquired the Ann Arbor, Mich.-based Truven Health Analytics, a provider of cloud-based healthcare data, analytics and insights, with its more than 8,500 clients, for $2.6 billion. Upon completion of the acquisition, IBM’s health cloud will house one of the world’s largest repositories of health-related data.

It’s clear that data analytics is here to stay; yet the discussions around it are changing, health IT leaders say. Keith Figlioli, senior vice president of healthcare informatics at the Charlotte-based Premier, Inc., recently spoke with Healthcare Informatics on the state of healthcare analytics and he referenced Gartner’s Hype Cycle schematic around the maturity and adoption of new technologies as a good parallel. Gartner is an information technology research and advisory company, and according to Gartner’s Hype Cycle schematic, there are five key phases of a technology’s life cycle beginning with technology trigger and then moving into the peak of inflated expectations. That’s followed by a downswing, called the “trough of disillusionment,” and then eventually a gradual upswing, referred to as the slope of enlightenment and finally the plateau of productivity.

“We were in this hype cycle for so long, and I think we’re starting to get into the trough of disillusionment,” Figlioli says. “So I think many people are testing a lot of concepts, seeing if this stuff would work, and now they’re starting to see, one, how hard this work is, and two, how immature the space is.”

He continues, “We were in a four to five-year hype cycle in this area, and now we’re in this trough, and right after that, it picks back up and you get productivity gains. I think in the next two years or so, we’ll come into that upswing again. But we’re just in the early stages of getting into this trough, where people are really digesting what they spent and the tools that they have and asking the question, Is it really going to get them there?”

Keith Figlioli

While the description may be stark, Figlioli uses it to illustrate how the discussions around data analytics are changing as the industry moves into second and third itineration buying cycles.

“I think now the discussions are, Wow, this is hard; we’re in it for the long haul here,” Figlioli continues. Even with our business, people who didn’t talk to us on the first round are coming back and want to know more about deep enterprise data management capabilities. And what I see is that we’re in second and third round buying cycles, which tells me that people are getting smarter, and they are realizing the level of complexity here. We see more attention to data management, data acquisition, data transformation and enterprise data management,” he says.

Four years ago, Premier launched its PremierConnect Enterprise business intelligence and enterprise-wide analytics platform, which includes a cloud-based data warehouse, which is vendor- and payer-agnostic.

“The storyline here, to me, is more about data management. When it comes to data analytics tools, it might be a great dashboard or a great predictive algorithm or actuarial analysis on risk stratification, but if the data coming into those systems is not good, those tools are almost useless, and most health systems don’t have good data,” he says.

Figlioli also is involved with his organization’s Data Alliance Collaborative, an organization that includes representatives of 12 of the largest health systems focused on co-developing data analytics methods and sharing best practices. So what have these pioneering health systems been learning in the process?

“It’s a marathon, not a sprint,” Figlioli says, “I think the two biggest lessons are, one, at what stage are health systems from a cultural readiness standpoint to do this type of work? So, are they socially and culturally ready to use data to run their business?”

And many health IT leaders say that cultural readiness must come from the top down. “Clinical leadership has to be 100 percent aligned that this is the way we’re going to run the enterprise and deliver care,” Figlioli says.

The second learning centers on effectively prioritizing the analytics work across the enterprise.

“When you do these types of projects what becomes very clear is that you have multiple stakeholders with multiple interests across these larger enterprises so, how do you think about the priority order to do the work that gets you the biggest bang for the buck?” Figlioli says.

Leveraging Analytics Locally, and Enterprise-Wide

Al Villarin, M.D., CMIO at Staten Island University Hospital, agrees that a cultural shift is vital for analytics work. “We have an entire country of clinical people who learned to practice medicine not using analytics, so it’s going to take a generation of training with the use of analytics to change clinical practice,” he says.