Using Advanced Analytics for Life-Saving Work
Webinar — January 24, 2023
Clinical data from electronic health record systems when combined with advanced analytics can equip healthcare providers with insights that can help alter the course of their patients’ lives. One example is assessing the risk of suicide.
The University of Connecticut and UConn Health Center has developed a clinically validated, machine learning-based approach for identifying individuals with a significant likelihood to attempt to take their own lives. The prediction tool is incorporated into an analytics dashboard that providers that can consult when interacting with patients directly. It also affords opportunities for population-wide analysis to identify groups of individuals who would benefit from proactive intervention.
In this webinar, lead investigator Dr. Robert Aseltine (Professor and Chair, Department of Behavioral Sciences and Community Health) will discuss the genesis of this work and how it will be applied throughout Connecticut’s health systems.
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