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Wednesday, July 17, 2013
An emerging function of clinical analytics is to match patients to the right services at the point of need. One method to perform this matching is to use predictive analytics. We will share how Allina Health has employed a strategy of quasi-real-time risk score calculations based on various data sources available in an enterprise data warehouse to pair patients with those services, including the development and implementation of a readmission predictive model. Then, we will lead a discussion on how risk score calculations were operationalized via automatic calculations and the deployment of in-memory dashboards to make interventions actionable prior to patient discharge. Through this session you will able to gain insight on the developmental, operational, and measurement stages of deploying real-time predictive modeling in today’s healthcare setting and how these tools can be implemented while focusing on all elements of the triple-aim; patient experience, care quality, and cost minimization.