Kaiser Permanente reduces patient mortality with predictive analytics

The managed care consortium's Advance Alert Monitor early warning system uses predictive analytics to identify patients at risk of deterioration within the next 12 hours.

Kaiser Permanente reduces patient mortality with predictive analytics
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In healthcare, early intervention can be the difference between life and death. Healthcare giant Kaiser Permanente has put that principle into action with a hospital workflow tool, supported by predictive analytics, that it uses to identify non-intensive care unit (ICU) patients that are likely to rapidly deteriorate.

The integrated managed care consortium, based in Oakland, Calif., has more than 12 million health plan members and employs more than 217,000 people, including nearly 60,000 nurses and 23,000 doctors. It operates 39 medical centers and 690 medical facilities in eight states and the District of Columbia.

"Patients who are in the hospital but outside the intensive care unit sometimes deteriorate in the hospital and have to be transferred to the ICU unexpectedly. These patients have far worse outcomes than patients who are admitted to the ICU directly," says Dr. Gabriel Escobar, research scientist, Division of Research, and regional director, Hospital Operations Research, Kaiser Permanente Northern California.

The scientific literature and direct analyses by Escobar's team shows that non-ICU patients that require unexpected transfers to the ICU make up only 2 percent to 4 percent of the total hospital population, but account for 20 percent of all hospital deaths. Their hospital stays also average 10 to 12 days longer than other patients.

Predictive analytics for proactive care

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