New Case Study: Using Natural Language Processing (NLP) to Prevent Suicides

Suicide is one of the leading causes of death nationwide, with an estimated 90% percent of occurrences related to potentially treatable mental health conditions. And yet, 40% of people who had recently attempted suicide said they were not receiving mental health services. NeuroFlow combines suicide prevention technology and compassionate human outreach to identify and support at-risk individuals before a crisis occurs.

Our newest case study highlights how our unique combination of suicide prevention technology and compassionate human outreach can identify and support at-risk individuals before a crisis occurs.  In fact, 53% of individuals who triggered an NLP urgent alert were not flagged by a PHQ assessment in the 30 days prior to the alert.  “NLP-triggered urgent alerts are a life-saving feature within the NeuroFlow platform,” says Faith Best, LCSW, Clinical Services Senior Manager at NeuroFlow, “Many of these alerts are triggered in between PHQ assessments, meaning the individual would not have received support for weeks without the intervention of NLP, putting them at greater risk for
self-harm or suicide.”

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