AI in Suicide Risk Prediction

Most health system models draw on data already in the electronic health record: prior suicide attempts and self-harm diagnoses, mental health and substance use diagnoses, prescriptions, emergency visits and inpatient stays, and screening answers such as item 9 of the PHQ-9, which asks about thoughts of death or self-harm. Methods range from penalized logistic regression to random forests and gradient-boosted trees. Well-known efforts include models from the Mental Health Research Network, which includes Kaiser Permanente sites, work at Vanderbilt University Medical Center, and the VA's REACH VET, launched in 2017, which flags roughly the top 0.1 percent of risk scores for review. Several large studies report good discrimination, meaning the models rank higher-risk patients above lower-risk ones reasonably well. The main misconception is that these models predict who will die. They do not. Suicide is rare in any given month, so even an accurate model produces mostly false positives; most flagged patients will never attempt. The models are best understood as tools for prioritizing limited clinical attention. That makes the response to a flag the real ethical question. Good practice treats a flag as a reason for caring, voluntary outreach: a check-in, a review of the care plan, safety planning, and discussing limits on access to lethal means such as safe firearm storage. Poor practice uses flags punitively or coercively, shares them outside clinical care, or leaves them unexplained to patients. Health systems also need to test performance across racial, ethnic, age and sex groups, because a model trained on past records inherits gaps in who was diagnosed and treated. Outside health care, platforms such as Facebook have used automated detection of suicidal posts since 2017, with much less public validation.