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Advocate Health Care

Advocate Health Care lowers readmissions with predictive analytics

When it comes to lowering a hospital’s readmission rate, fewer hospitals are doing it better than Advocate Health Care.

After only one year of using predictive analytics and robust patient education, the 3,500-bed hospital system has lowered its heart failure readmission rate by 21 percent.

“What Advocate and Cerner are doing together around readmissions is about 15-20 percent better than anything that exists in the industry today,” said Senior Vice President of Clinical Transformation Dr. Rishi Sikka.

Advocate Health Care partnered with Cerner and created the Advocate Cerner Collaborative (ACC) in 2012. Since then, the two organizations have worked together to develop advanced, evidence-based analytics to improve the quality and efficiency of patient care.

“One of the first things our collaboration did was create a readmission risk level model,” said Clinical Process Designer Fran Wilk. “It’s an algorithmic model that identifies our high risk patients. The technology puts, in the face of the clinician, a very quick clinical picture of that patient and what their co-morbidities.”

Clinicians monitor and update readmission risk levels every two hours. They focus their resources on the high risk patients and educate them on how to manage their symptoms while encouraging them to take ownership of their illness.

“It’s not just getting our patient through our threshold,” Wilk said. “It’s reaching into the environment that we’re sending them to and affecting their future. That is what helps them stay well at home.”

By comparing 2013 – 2014 year over year outcomes between January and June, Advocate Health discovered patients who receive high risk education have a 20 percent lower readmission rate.

“The solution actually suggests the education orders,” Wilk said. “Care managers can easily pull that forward and order the right education for bedside nurses to administer.”

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Client outcomes were achieved in respective settings and are not representative of benefits realized by all clients due to many variables, including solution scope, client capabilities and business and implementation models.