Study finds AI can spot patients at risk for suicide
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New Delhi: New research shows that artificial intelligence (AI) can assist doctors in identifying patients who are at risk for suicide, potentially enhancing preventive efforts in normal medical settings.
The study, published in the journal JAMA Network Open, compared two approaches: automatic pop-up alerts that interrupted the doctor's workflow and a more passive system that simply displayed risk information in the patient's electronic record.
The team found that the interruptive alerts were far more effective, leading doctors to conduct suicide risk assessments in connection with 42 per cent of screening alerts, compared to just 4 per cent with the passive system.
Colin Walsh, associate professor of biomedical informatics, medicine and psychiatry at Vanderbilt University Medical Center said that most people who die by suicide have seen a health care provider in the year before their death, often for reasons unrelated to mental health.
The team tested whether their AI system called the Vanderbilt Suicide Attempt and Ideation Likelihood model (VSAIL), could effectively prompt doctors in three neurology clinics to screen patients for suicide risk during regular clinic visits.
“Universal screening isn't practical in every setting. We developed VSAIL to help identify high-risk patients and prompt focused screening conversations,” said Walsh.
The VSAIL model analyses routine information from electronic health records to calculate a patient's 30-day risk of suicide attempt.
The researchers suggested that similar systems could be tested in other medical settings.
Healthcare systems need to balance the effectiveness of interruptive alerts against their potential downsides, Walsh said.
These results suggest that automated risk detection combined with well-designed alerts could help us identify more patients who need suicide prevention services, said the authors.
Studies have shown that 77 per cent of people who die by suicide have contact with primary care providers in the year before their death.
With inputs from IANS