Study Shows AI Can Detect Suicide Risk Early
As artificial intelligence makes way to help doctors detect diseases like cancer at an early stage, it’s now proving its potential in addressing mental health crises. A study revealed that AI can identify patients at risk for suicide, offering a tool for prevention in everyday medical settings. Published in the JAMA Network Open Journal, the study addressed the case with two methods of alerting doctors about suicide risk: an active “pop-up” alert requiring immediate attention and a passive system (less urgent way) displaying risk information in a patient’s electronic chart. The study found that the active alerts outperformed the passive approach, prompting doctors to assess suicide risk in 42% of cases, compared to just 4% with the passive system. Besides, it highlighted the need for using precise tools to start a conversation about suicide risks. By combining automated risk detection with thoughtfully designed alerts, this innovation offers hope for identifying and supporting more individuals in need of suicide prevention services. Colin Walsh, Associate Professor of Biomedical Informatics, Medicine, and Psychiatry at Vanderbilt University Medical Center, highlighted the urgency of this innovation. “Most people who die by suicide have seen a healthcare provider in the year before their death, often for reasons unrelated to mental health,” Walsh mentioned. Previous studies show that 77% of individuals who die by suicide had contact with primary care providers in the year preceding their death. These findings underline the critical role AI could play in bridging the gap between routine medical care and mental health intervention. The study tested Vanderbilt’s AI-driven system, the Suicide Attempt and Ideation Likelihood model (VSAIL), in three neurology clinics. The system analyses routine data from electronic health records to estimate a patient’s 30-day risk of attempting suicide. When high-risk patients were flagged, doctors were prompted to initiate targeted conversations about mental health. Walsh explained, “Universal screening isn’t practical everywhere, but VSAIL helps us focus on high-risk patients and spark meaningful screening conversations.” While the results were promising, researchers emphasised the need for a balance between the benefits of active alerts and their potential downsides, such as workflow disruptions. The authors suggested that similar systems could be adapted for other medical specialities to extend their reach and impact. Earlier in 2022, Cambridge University released a paper to assess the patients at risk of attempting suicide using PRISMA criteria (Preferred Reporting Items for Systematic Reviews and Meta-Analyses).
Published in the JAMA Network Open Journal, the study addressed the case with two methods of alerting doctors about suicide risk.
A study revealed that AI can identify patients at risk for suicide, offering a tool for prevention in everyday medical settings.
["AI (Artificial Intelligence)", "Suicide"]
["artificial intelligence", "programming_languages:R", "AI", "data_tools:Spark", "innovation", "Suicide", "disruption", "R", "AI (Artificial Intelligence)"]
["AI", "artificial intelligence", "R", "innovation", "disruption", "programming_languages:R", "data_tools:Spark"]
Vidyashree is enthusiastic about investigative journalism. Now trying to explore how AI solves for all.