AI-Powered Alerts Significantly Improve Suicide Risk Detection in Neurology Clinics

AI-Powered alerts improve suicide risk detection in neurology clinics, s

Neurology clinics were chosen for the study because certain neurological diseases and conditions are associated with a higher risk of suicide. A recent study conducted by researchers at Vanderbilt University Medical Center found that artificial intelligence (AI) can help doctors identify patients at risk for suicide, potentially improving prevention efforts in routine medical settings. The study tested the Vanderbilt Suicide Attempt and Ideation Likelihood model (VSAIL) in three neurology clinics. The VSAIL model, developed by Colin Walsh's team at Vanderbilt University Medical Center, analyzes routine information from electronic health records to calculate a patient's 30-day risk of suicide attempt. Neurology clinics were chosen for the study because certain neurological diseases and conditions are associated with a higher risk of suicide. According to La Razón, the study involved 7,732 patient visits over six months, resulting in a total of 596 automated screening alerts for suicide risk during regular clinic visits. As detailed in 20 Minutos, the researchers compared two approaches to reporting individuals at risk of suicide: automatic pop-up alerts that interrupted the doctor's workflow versus a passive system displaying risk information in the patient's electronic chart. Telex reported that with the interruptive alerts, doctors conducted suicide risk assessments in connection with 42 percent of screening alerts, while the passive system resulted in only a 4 percent assessment rate. "Most people who die by suicide have seen a healthcare provider in the year before their death, often for reasons unrelated to mental health," said Colin Walsh, associate professor at Vanderbilt University Medical Center. "The automated system flagged only about 8% of all patient visits for screening. This selective approach makes it more feasible for busy clinics to implement suicide prevention efforts," said Walsh, as reported by Telex. "Universal screening isn't practical everywhere, but VSAIL helps us focus on high-risk patients and spark meaningful screening conversations," he added, emphasizing the need for more focused screening discussions. "These results show that automated risk detection, combined with targeted alerts, can make a difference," noted the authors of the study. 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. "Healthcare systems must balance the effectiveness of interrupting alerts with their possible downsides," concluded Walsh, highlighting the need to find a balance between effectiveness and side effects. Medical Dialogues noted that while the interruptive alerts were more effective at prompting screenings, they could potentially contribute to alert fatigue, overwhelming doctors with frequent automated notifications. The researchers recommend that future studies should examine this concern and suggest testing similar systems in other medical fields to enhance risk detection and evaluation processes, as reported by Analytics India Magazine. The VSAIL model proved effective in identifying patients at high risk; one in every 23 individuals flagged by the system later reported suicidal thoughts. Medical Dialogues noted that in earlier prospective testing, where patient records were flagged but no alerts were fired, the model demonstrated its effectiveness in identifying high-risk patients. This article was written in collaboration with generative AI company Alchemiq

Study Shows AI Can Detect Suicide Risk Early

Published in the JAMA Network Open Journal, the study addressed the case with two methods of alerting doctors about suicide risk. 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).

AI alerts improve suicide prevention in clinics - Earth.com

Over the past decade, there's been growing concern about the staggering rates of suicide. Now, a remarkable study from Vanderbilt University Medical Center (VUMC) offers a ray of hope. The study illustrates how artificial intelligence (AI) alerts can aid doctors in identifying patients at a higher risk for suicide. Led by Dr. Colin Walsh, the research team tested the Vanderbilt Suicide Attempt and Ideation Likelihood (VSAIL) model. This AI-driven system aimed to prompt suicide risk screening in three VUMC neurology clinics. The findings, published in the journal JAMA Network Open, demonstrate that interruptive alerts - which actively notify doctors during their workflow - were far more effective than passive notifications embedded in electronic charts. The study compared two types of AI-driven alerts. Interruptive alerts actively interrupted the doctor's workflow by appearing as pop-up notifications during a patient consultation. This required the doctor to acknowledge and address the alert immediately, thus ensuring that they took action right away. Passive alerts provided the same risk information but did so in a less direct way. The information was displayed within the patient's electronic medical chart, where it could be seen but did not actively prompt the doctor to take immediate action. This approach avoided interrupting the workflow but relied on the doctor noticing and acting on the information independently. The study found interruptive alerts to be more effective at prompting doctors to conduct suicide risk assessments. The results were striking. The team found that interruptive alerts led to suicide risk assessments in 42% of cases, while passive alerts resulted in assessments in only 4% of cases. "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. But universal screening isn't practical in every setting. We developed VSAIL to help identify high-risk patients and prompt focused screening conversations," said Dr. Walsh. Suicide rates in the United States have been increasing steadily, with 14.2 deaths per 100,000 people annually. Suicide is now the 11th highest cause of death nationwide. The researchers noted that 77% of individuals who die by suicide have seen a primary care provider within the year before their death. These figures highlight the critical need for better ways to identify and support individuals at risk. The VSAIL model offers a significant advance in addressing this challenge. It uses data from routine electronic health records to assess a patient's 30-day risk of attempting suicide. In prior testing, VSAIL demonstrated its effectiveness by identifying high-risk individuals, with one in 23 flagged patients later reporting suicidal thoughts. This capability positions VSAIL as a powerful tool for targeted suicide prevention efforts. The new study involved 7,732 patient visits over six months, which triggered 596 suicide alerts. Researchers focused on neurology clinics, as certain neurological conditions carry heightened suicide risks. Of the flagged visits, only about 8% prompted alerts, highlighting the model's efficiency in busy clinical environments. During the 30-day follow-up, no flagged patients reported suicidal ideation or attempts. However, the team noted potential downsides, such as "alert fatigue," where frequent notifications could overwhelm clinicians. Future studies will explore this balance. "Health care systems need to balance the effectiveness of interruptive alerts against their potential downsides," Walsh noted. "But these results suggest that automated risk detection combined with well-designed alerts could help us identify more patients who need suicide prevention services." The study's success suggests that similar systems could benefit other medical settings. By selectively flagging high-risk patients, AI models like VSAIL offer a feasible and impactful approach to suicide prevention. "The automated system flagged only about 8% of all patient visits for screening. This selective approach makes it more feasible for busy clinics to implement suicide prevention efforts," Dr. Walsh concluded. The research was conducted by a multidisciplinary team from VUMC, including Dr. Michael Ripperger, Dr. Laurie Novak, and co-senior authors Dr. William Stead and Dr. Kevin Johnson. The study paves the way for innovative, AI-driven interventions that could save lives and redefine suicide prevention in healthcare. Like what you read? Subscribe to our newsletter for engaging articles, exclusive content, and the latest updates.