Suicide Risk Prediction for Suicide Prevention

This trial aims to determine if a suicide risk prediction algorithm can better identify adults at risk of suicide in primary care settings and help them receive more mental health support. The focus is on the tool's ability to identify individuals who might need extra help and involve them in safety planning. Participants include adults who have visited Kaiser Permanente Washington for primary care during specific periods. As an unphased trial, this study allows participants to contribute to important research that could enhance mental health support systems.

The trial information does not specify whether you need to stop taking your current medications. It's best to discuss this with the trial coordinators or your doctor.

Research has shown that special computer programs, known as suicide risk prediction algorithms, help identify individuals who might be at risk of suicide. These tools aim to enhance mental health care by identifying those who need extra support.

For example, one study successfully identified adults at risk of suicide using similar programs. This capability assists doctors and mental health professionals in determining who requires more attention. Another study demonstrated that these programs could accurately predict risks, which is crucial for providing timely help.

There is no direct evidence of safety issues for individuals using these programs. Since this is not a drug treatment, typical safety concerns like side effects do not apply. Instead, the focus remains on how effectively the programs can predict risk and support mental health care.

Researchers are excited about this trial because it introduces a cutting-edge suicide risk prediction algorithm aimed at enhancing mental health care. Unlike traditional approaches that rely on self-reported symptoms and clinical judgment, this algorithm uses data-driven insights to identify individuals at risk more accurately. By implementing this tool in primary care settings, the goal is to prompt timely mental health monitoring, potentially preventing suicide attempts. This proactive approach could revolutionize how we address suicide risk, offering hope for more effective prevention strategies.

Research has shown that tools called suicide risk prediction algorithms can help identify individuals at higher risk of suicide. In this trial, participants in the experimental arm will receive suicide risk monitoring, which involves using the suicide risk prediction algorithm in primary care to prompt additional mental health monitoring. These tools analyze data to predict suicidal behavior and accurately identify suicide-related risks 80-89% of the time. Studies indicate that these algorithms assist healthcare providers in making better mental health care decisions by identifying those who may need extra support. By finding individuals at risk, these prediction models aim to provide timely help and improve patient outcomes.¹²⁶⁷⁸