Palo Alto, CA (June 14, 2018) – Recent data from the CDC show that suicide is the second leading cause of death for people aged 15-34 in the US, and tenth for all ages. The CDC estimates the financial cost of suicide and suicide attempts at $59 billion annually, inclusive of lost productivity and medical costs. Adjustments for under-reporting of suicide deaths increases that total to $93.5 billion.

These sobering statistics led long-time analytics provider, HBI Solutions, to build what they believe to be the first predictive suicide risk model available on the commercial market. Powered by data from across the care continuum and HBI’s proven data science capabilities, the new model identifies individuals at risk for attempting suicide, assigns a risk score, and exposes the clinical, social and lifestyle factors driving that risk to help clinicians take action.

It couldn’t come at a better time, as the push for better population health management has led hospitals and health systems to increase their focus on social determinants of health and improve care for patients at risk for attempting suicide.

Traditionally providers have turned to known risk factors like a behavioral health diagnosis or a family history of suicide. However, non-medical conditions and situations like housing status, income and even zip code can influence suicide risk as much as some medical conditions.

HBI’s new model helps identify which patients in a certain population should be targeted for intervention and gives providers both clinical and non-clinical factors driving that risk. “We’ve known for some time that the collection and analysis of social determinant of health data is helpful in the identification of those at risk for any type of health event,” said HBI Solutions CEO, Eric Widen.

Always expanding its risk model inventory, HBI decided to add suicide risk to its list of peer-reviewed and validated models in 2017. “In building this new model, our machine learning algorithms have found social determinants to be particularly important to predict suicide attempts,” said Widen.

Officially offered to the market in late 2017, two HBI Solutions clients are putting the model to the test. Hospitals and general medical providers often don’t have a full medical and social profile for the patient and may not have time to dig through past visit notes and summaries where risk factors, particularly social determinants, could be hidden. Having this machine learning tool at the point of care, helps clients target the highest risk patients, better allocate resources, and hopefully save lives.

According to Widen, “Our data science approach is unique. Our risk models use any and all available data, employ natural language processing to incorporate data from notes and summaries, deliver predictions in real-time, and present risk in context with a patient’s full medical profile.”

About HBI Solutions

HBI Solutions was founded in 2011 by a physician, a data scientist, and a healthcare IT business executive who shared a vision of improving health and reducing costs. Their solutions are grounded in clinical care and data science, and their work is prospectively tested, peer-reviewed, and published in leading medical journals. HBI continually seeks to build or innovate on these solutions to provide more value to clients and support delivery of better care at a lower cost.