AI’s Role in the Push for Suicide Prevention
The reality of mental health support in the U.S. is one that lacks affordable care and a general lack of resources.
In the second month of school, a Utah student talked about accidentally falling off a building. The child didn’t look anyone in the eye that day. Peers joked about killing themselves for weeks after that incident, and the student went on medical leave later that month.
In Herriman, Utah, seven students at one high school have already died by suicide during the 2018 and 2019 school year. In health-tech circles, however, a hopeful question has appeared: Can artificial intelligence (AI) combat the suicide crisis? It’s timely as ever today, Sept. 10, which is World Suicide Prevention Day.
News about AI being able to recognize signs of depression has created a narrative of big brother watching over us and “curing” depression and suicide. But the reality of mental health support in the U.S. is one that lacks affordable care and a general lack of resources. AI can help, in some small way, overcome these issues and more.
HBI Solutions is a company that uses AI with data already available to providers to create clinical decision support tools, providing insights into suicide risk that is not otherwise obvious. Many studies indicate that people who intend to attempt suicide deny their intention or suicidal thoughts. But HBI’s predictive Spotlight Suicide Attempt Model has achieved results that show the importance of better risk visibility at the point of care and why data science still has much to learn about risk context and mental health.
The Forces Working Against Suicide Prevention
The gap in mental healthcare manifests in both access problems because of financial constraints and underserved populations with no providers in their area. In Utah, for instance, suicide rates have been increasing steadily at least since 1999, with many counties showing a lack of available resources across the board. Simply put, there is an insufficient number of providers to serve every patient who tries to get mental health support, and specific groups — such as those with language disparities — have an even wider gap in care accessibility.