June 13-14, 2018 | San Francisco, CA

Tips for Doing DIY Predictive Analytics Right

By Mike Miliard

Michael Johnson, a decision support data scientist at Bend, Oregon-based St. Charles Health System, has only worked in healthcare for a couple years. Before that, he'd spent most of his career doing data modeling and predictive analytics in higher education and in the military.

During his short time so far in this data-intensive industry, Johnson, who will speak at the HIMSS Big Data and Healthcare Analytics Forum on June 13, says he's been impressed by the scope and variety of challenges that can be solved by smart applications of predictive modeling.

But the best way to make that happen, he said, is to learn how to do that hard work in-house. While many health systems rely on outside companies to develop and manage their algorithms,

DIY predictive analytic projects are well worth the effort it takes to get them off the ground.

"I'm not going to kid you," said Johnson. "You have to roll up your sleeves and make some decisions." You'll probably need to make some investments, too – in technology and in staff training."

But the benefits of having complete control over your analytics are hard to overstate.

Read more on Healthcare IT News.

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