The ins and outs of predictive modeling
Unlike traditional IT projects, predictive modeling is never truly finished, but instead aspires to turn healthcare into a continuously learning system. "This stuff is all iterative," Peele says. "You get started. You discover something. You try something. It doesn't work. You figure something else out. It's sort of like groping around in the dark in a room until you find the light switch."
On October 28, my other presenter with Peele will be Christine Vanzandbergen, the clinical decision support officer at Penn Medicine, at the oppose end of Pennsylvania from UPMC.
While not as far along with its predictive modeling efforts as UPMC, Penn Medicine is also leveraging a variety of different tools to make progress. "Our approach has been to take our data internally, understand it, validate it in a predictive model, evaluate our results, and look for partners that we think can improve those results," Vanzandbergen says.
Both Peele and Vanzandbergen have been unimpressed with the offerings of the many technology vendors hawking products specifically for predictive modeling for healthcare. Both provider organizations continue to focus on internal efforts while they try to identify partners that can truly add value. "Our most recent partnership turned in some really disappointing results when compared to the performance of our internal readmission risk model," Vanzandbergen says.