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. It also affords opportunities for population-wide analysis to identify groups of individuals who would benefit from proactive intervention.
Join the HIMSS New England Chapter to hear lead investigator Dr. Robert Aseltine discuss the genesis of this work and how it will be applied throughout Connecticut’s health systems.
Presented by Dimensional Insight
- Robert H. Aseltine, Jr., PhD, UCONN Health, Professor and Chair, Division of Behavioral Sciences and Community Health Director, Center for Population Health, UCONN Health
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