Authored by: Eric Widen, CEO and Co-founder, HBI Solutions
This week is National Suicide Prevention Week, and it has given me pause to remember the primary reason I co-founded HBI Solutions 7 years ago: to create and deliver actionable information to care teams to help keep patients healthy and safe. As part of this focus, we released our first Suicide Risk Model last year to aid care givers in suicide prevention.
The statistics are alarming and trending the wrong way. Death by suicide rose 28% nationwide from 1999 to 2016. More recent data from the CDC show that suicide is currently the second leading cause of death for people aged 15-34 in the U.S., and tenth for all ages. These sobering statistics are further exacerbated by the suggestion from the American Foundation for Suicide Prevention, that for every reported suicide death, approximately 11.4 people visit a hospital for self-harm related injuries.
Throughout my life, I have been close to people who have either committed or attempted suicide, and I know I am not alone. These events reverberate throughout friends and family and are never forgotten. The ability to better and more quickly identify people who need help creates clarity and purpose to our mission at HBI.
We have a crisis on our hands that needs to be addressed.
There are major shortages in front line primary care providers in the US today, and this will worsen as the aging population grows and demands more services. Further, primary care teams often lack mental health professionals and adequate assessment tools to identify mental health issues.
Machine learned, automated computer algorithms can assist primary care teams to quickly and accurately risk stratify their patient panels into the right mental health programs, for example suicide prevention programs. We have found that in addition to established clinical risk factors like diagnoses and lab results; non-clinical conditions and situations like housing status, income and education can strongly influence suicide risk.
Our product Spotlight surfaces insights from thousands of risk features that might not be recognized from a practitioner’s individual experience with the data already available in EHR and claims systems along with publicly available data to incorporate community social determinants of health.
HBI Solutions’ Suicide Attempt Risk model provides insight into the risk of suicide to assist providers in planning short-term interventions and long-term population health improvement strategies. Having this machine learning tool at the point of care, helps providers target the highest risk patients, better allocate resources, and hopefully save lives.
Drawing from data already available within health systems today, this solution holds promise towards suicide prevention.
Contact us at email@example.com to learn more.
If you’re feeling suicidal, talk to somebody. Call the National Suicide Prevention Lifeline: 800-273-8255 or contact the Crisis Text Line by texting HOME to 741 741