Suicide Risk Prediction for Suicide Prevention
What You Need to Know Before You Apply
What is the purpose of this trial?
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.
Will I have to stop taking my current medications?
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.
What prior data suggests that this suicide risk prediction algorithm is safe for use in primary care?
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.
Overall, these tools are being tested to assess their potential in real-world settings without causing harm, making them a promising option for improving mental health monitoring.12345Why are researchers excited about this trial?
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.
What evidence suggests that the suicide risk prediction algorithm is effective for identifying at-risk patients?
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
Are You a Good Fit for This Trial?
This trial is for adult primary care patients. It's designed to see if using a suicide risk prediction algorithm can help doctors spot who might need more mental health support.Inclusion Criteria
Exclusion Criteria
Timeline for a Trial Participant
Screening
Participants are screened for eligibility to participate in the trial
Implementation
Implementation of the suicide risk prediction algorithm in primary care to prompt extra mental health monitoring
Follow-up
Participants are monitored for safety and effectiveness after implementation
What Are the Treatments Tested in This Trial?
Interventions
- Suicide risk prediction algorithm
Trial Overview
The study tests whether a suicide risk prediction algorithm in primary care settings can effectively identify patients at higher risk of suicide, prompting additional safety planning and monitoring.
How Is the Trial Designed?
2
Treatment groups
Experimental Treatment
Active Control
Quality improvement intervention: 6 months following implementation of the suicide risk prediction algorithm in primary care to prompt extra mental health monitoring.
Find a Clinic Near You
Who Is Running the Clinical Trial?
Kaiser Permanente
Lead Sponsor
National Institute of Mental Health (NIMH)
Collaborator
University of Washington
Collaborator
Citations
Suicide risk assessment tools and prediction models
These models aim to assist in stratifying risk, improve clinical decision-making, and facilitate a personalised medicine approach to the prevention of suicidal ...
Machine learning and the prediction of suicide in ...
Regarding the predicted outcome, 41 (51%) studies used ML to predict lifetime suicide attempts (e.g., retrospective assessed past attempts), ...
3.
bmcpsychiatry.biomedcentral.com
bmcpsychiatry.biomedcentral.com/articles/10.1186/s12888-024-06273-2Predicting suicidal behavior outcomes: an analysis of key ...
Predicting the outcomes of suicidal behaviors can help identify individuals at higher risk of death, enabling timely and targeted interventions.
Leveraging AI for Suicide Risk Prediction
Key Findings: The models achieved an accuracy in detecting suicide-related transcripts of 80-89%, demonstrating the promise of AI in detecting ...
Risk Model–Guided Clinical Decision Support for Suicide ...
This randomized clinical trial tests whether interruptive clinical decision support (CDS) prompted more frequent in-person suicide risk ...
Evaluation of a Veterans Health Administration Suicide ...
This cohort study examines treatment engagement, health care utilization, and suicide-related outcomes associated with the Veterans Health.
7.
research.kpchr.org
research.kpchr.org/News/CHR-Stories/CHR-Researchers-Engage-with-Health-Systems-to-Prevent-SuicideCHR Researchers Engage with Health Systems to Prevent ...
Four algorithms were developed through the MHRN and found to be successful in identifying at-risk adults. One of these algorithms —referred to ...
One-Week Suicide Risk Prediction Using Real-Time ...
The behavioral changes identified by the algorithm predicted suicide risk in a time frame of 1 week with an area under the curve of 0.78, ...
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