Far too often, analytics efforts have fallen short of making a tangible impact on outcomes because the problem being analyzed hasn’t been successfully implemented in real workflows.
UnityPoint Health in Iowa focused on integrating analytical models in its readmission reduction strategy and coached the care team in proper workflows that facilitate optimal analytical results. With this approach, the delivery system, anchored by 532-bed St. Luke’s Hospital in Cedar Rapids, succeeded in reducing risk-adjusted readmission rates by 40 percent over three years.
The organization created multiple analytics models that layered together support care for patients to determine which patients were ready to go home and also which of those patients were unlikely to show up for a scheduled doctor appointment in the next 30 days.
“Imaging a patient ready to leave—what does that look like?” says Benjamin Cleveland, a data scientist at Unity Point.” Does the patient have a level of social support at home and takes their medications as prescribed?” Predictive analytics can show how a patient’s social and economic circumstances can affect outcomes, he notes.
Fortunately, analytics have matured and it is getting easier to identify high-risk patients and link them with care managers to make sure care transitions to the home turn out to be successful, says Rhiannon Harms, executive director at UnityPointHealth, who will speak along with Cleveland during an educational session at HIMSS18. “This is the story of a partnership between analytics, the IT department and clinicians,” she adds. “But you need to address physician workflow before bringing in tools and analytics.”
The session: “Stacking Predictive Models to Reduce Readmissions,” is scheduled at 8:30 a.m. on March 6 in Room Palazzo D.
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