Predictive Models Show Promise in Preventing Suicide

"Over 40% of people who die by suicide visit a health care provider in the month before their death, underscoring the critical role of health care settings in suicide prevention. Researchers have been trying to find better ways of quickly and accurately detecting suicide risk in these settings. One tactic that has shown promise is analyzing electronic health records (EHRs) to quickly identify people in need of help."

"One tactic that has shown promise is analyzing electronic health records (EHRs) to quickly identify people in need of help. In a study funded by the National Institutes of Health, Emily Haroz, Ph.D. , Roy Adams, Ph.D. , Novalene Alsenay Goklish, D.B.H. , and colleagues created new suicide risk prediction models using data in EHRs from the Indian Health Service (IHS). The models were better at identifying those at risk for suicide than currently used screening methods."

Electronic health record (EHR) data included over 331,000 visits by more than 16,000 adults to Indian Health Service (IHS) providers between 2017 and 2021. During that period, 324 people attempted suicide and 37 people died by suicide; 72% of attempts and 50% of deaths occurred within 90 days after contact with the health system. Predictive models that incorporated suicide risk factors documented in EHRs were developed and evaluated for predicting suicide attempt or death within 90 days after an IHS visit. Both models correctly identified 82% of individuals who attempted or died by suicide within 90 days, compared with 64% identified by current screening methods and history-based approaches.

Read at National Institute of Mental Health (NIMH)