Readmissions 'Drop Like a Rock' with Predictive Modeling

Scott Mace, for HealthLeaders Media , October 8, 2013

When a Medicare patient new to UPMC walks through their doors, "We use a questionnaire that CMS requires us to get anyway on our patients," Peele says. Out of 24 questions on that survey, eight combinations of answers on five of those questions are "absolutely the signal that these people are going to run about 300% more expensive than people who don't hit those rules," she says. "Once you've discovered the questions and rules, you don't need to run a model anymore. The call center can deploy that on the phone."

Later, when a UPMC Health Plan member presents for hospitalization, the upper right-hand corner of UPMC's authorization screen prominently displays that member's readmission risk. But Peele shares an unexpected insight: Sometimes, the risk level is just so high that no added intervention is going to reduce it.

In this struggle to reduce readmissions, the main tool in UPMC's toolbox is the home visit. The trick of UPMC's success is in identifying the "sweet spot" of patients—those who aren't so sick that the home visit won't matter but aren't sick enough that a home visit will make a difference.

"There's a predictive range where you should put your resources, and the resource is a home visit," Peele says. "That's what actually matters. So we changed the discharge plan for people in that sweet spot, so they get a follow-up home visit."

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