According to new research, artificial intelligence (AI) might assist clinicians identify patients who are at risk of suicide, potentially enhancing preventive efforts in everyday medical settings.
The study, published in the journal JAMA Network Open, contrasted two approaches: automatic pop-up alerts that interrupted the doctor’s workflow and a passive system that simply displayed risk information in the patient’s electronic chart.
The study discovered that interruptive alerts were considerably more effective, prompting doctors to do suicide risk assessments in response to 42% of screening signals, compared to only 4% with the passive method.
Colin Walsh, an associate professor of biomedical informatics, medicine, and psychiatry at Vanderbilt University Medical Center, observed that the majority of people who commit suicide had seen a health care provider in the year preceding their death, typically for reasons unrelated to mental health.
The team tested their AI system, known as the Vanderbilt Suicide Attempt and Ideation Likelihood model (VSAIL), to see if it could effectively urge doctors in three neurology clinics to screen patients for suicide risk during regular visits.
“Universal screening is not practical in all situations. “We created VSAIL to help identify high-risk patients and initiate focused screening conversations,” Walsh explained.
The VSAIL model uses normal information from electronic health records to estimate a patient’s 30-day probability of suicide attempt.
The researchers proposed that comparable technologies be tested in other medical contexts.
According to Walsh, health care institutions must weigh the benefits and drawbacks of interruptive notifications.
“The findings indicate that automated risk detection, when paired with thoughtfully designed alerts, has the potential to significantly improve suicide prevention efforts,” the study’s authors said.
According to studies, 77% of people who commit suicide make contact with primary care doctors in the year leading up to their death.
