The predictive model that Duke Health uses to identify patients at risk of kidney failure is like “a watch tower in the sky,” says Will ElLaissi, program manager for the Innovation Living Lab at the Duke Institute for Health Innovation (DIHI). “We use a statistical model that can predict with 80 percent accuracy how long it will take a patient to hit end-stage renal disease,” he says.
Just as watch towers have guards who rapidly react to threats, DIHI brings a small team of clinical experts—including a nephrologist, care manager, and pharmacist—around a table to review each at-risk patient. With the help of a web-based, data analytics tool, team members can quickly access the most relevant information on each patient (e.g., kidney function trends, current medications), allowing them to make appropriate care decisions within a few minutes. DIHI calls this team-based method "population rounding."
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