Readmissions 'Drop Like a Rock' with Predictive Modeling
"What the vendors are selling in predictive modeling land is so generic and so basic that it's not useful," Peele says. "That's not the vendors' fault. It's just [that] healthcare is local." But that hasn't stopped lots of health systems from spending millions on these vendors' products.
Healthcare analytics vendor MEDai produces an external financial model that UPMC's insurance division has found useful, Peele says. "The validity of having an outside predictive model smooths our bond rating, and we're very highly rated, so there's a business reason that we buy that particular predictive model," she says.
Otherwise, UPMC builds its own predictive models, using statistics packages such as Statistica, R, Stata, SAS, MATLAB, Vensim, and Tableau.
Don't let the potentially endless parade of tools fool you. Predictive modeling is within the grasp of any healthcare system. At Penn Medicine, a single variable—previous hospital utilization—is the key to predicting readmissions. While the results aren't as impressive as UPMC's, they're still significant.
Penn is trying to understand what services are affecting admissions and payment. "We've lowered our readmissions by2 or 3%, which has us on the right track, so we're encouraged by that," Vanzandbergen says.
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