Readmissions 'Drop Like a Rock' with Predictive Modeling

Scott Mace, October 8, 2013

Predictive modeling offers the key to understanding which healthcare services most affect utilization, readmissions, and payment, and how to tackle the outliers. These analytics are within the grasp of any healthcare system.

Somewhere out there, a hospital near you may be figuring out the technological secret to significantly lowering readmissions.

It isn't a secret easily uncovered, it takes hard work, and it takes working smart. But it can be done.

"Our admits and readmits have dropped like a rock," says Pamela Peele, PhD, chief analytics officer of the UPMC Insurance Services Division. UPMC is the short name for the University of Pittsburgh Medical Center, and Peele is one of two presenters in my October 28 HealthLeaders webcast.

UPMC uses a variety of modeling tools to identify patients who are high utilizers of its services and—significantly—are likely to continue to be high utilizers in the next year. "Most people just regress to the mean," Peele says. "There's a whole industry of disease management that has been built on regression to the mean. They say they're managing the patient, but the patient would have gotten better, utilization would have gone down if nothing had happened, because most people actually get better."

The secret, Peele says, is finding the 20% of patients who won't get better on their own but who could respond to intensive, coordinated care.

Scott Mace

Scott Mace is the former senior technology editor for HealthLeaders Media. He is now the senior editor, custom content at H3.Group.


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