According to a new A.I.-assisted algorithm, he was one of several hundred V.A. patients nationwide, of six million total, deemed at imminent risk of suicide.
“I don’t like this idea of a list, to tell you the truth — a computer telling me something like this,” Barry, a retired postal worker.
The trends have defied easy explanation and driven investment in blind analysis: machine learning, or A.I.-assisted algorithms that search medical and other records for patterns historically associated with suicides or attempts in large clinical populations.
“The fact is, we can’t rely on trained medical experts to identify people who are truly at high risk,” said Dr. Marianne S.
The V.A.’s algorithm updates continually, generating a new list of high-risk veterans each month.
The veteran’s doctor explains what the high-risk designation means & makes sure the person has a suicide safety plan: that any guns and ammunition are stored separately; that photos of loved ones are visible; and that phone numbers of friends, social workers and suicide hotlines are on hand.
The computer mixes and shuffles scores of facts from the medical records — age, marital status, diagnoses, prescriptions — and settles on the factors that together are most strongly associated with suicide risk.
“The risk concentration for people in the top 0.1 percent on this score was about 40 times,” said John McCarthy.
Ronald Kessler, a professor of health care and policy at Harvard Medical School, said: “Right now, this and other models predict who’s at highest risk.