Suicide is one of the leading causes of death worldwide and the 10th leading cause of death in the US. A major barrier to suicide prevention has been that the cutting-edge scientific advances that have occurred in the past few decades have not yet been translated and implemented into clinical practice settings. We propose the development of a practice-based Center for Suicide Research and Prevention (CSRP) that will support the development, deployment, and evaluation of practice-ready and deployment-focused interventions aimed at improving the identification and effective treatment of those at risk of suicide. This Center will be a collaborative effort between researchers, clinicians, and stakeholders at Mass General Brigham (MGB) and Harvard University. Our focus is on improving the identification and prevention of suicide-related behaviors (SRBs) among patients presenting for treatment at emergency departments (EDs) and psychiatric inpatient units. Decades of research have shown that 50% of people who die by suicide are seen in a healthcare setting within one month before their death, 40% visit an ED the year before their death, and the suicide rate is highest in the weeks immediately following discharge from a psychiatric inpatient hospitalization. Our first aim is to build and maintain a cohesive and innovative transdisciplinary Center dedicated to advancing suicide prevention. This will be accomplished via the work of our proposed Administrative Core and Methods Core. Our second aim is to conduct four practice-focused research projects that target prediction and prevention of suicidal behaviors in ED and inpatient settings. Our Signature Project (SIG) will implement a previously-develop machine learning prediction algorithm based on electronic health record (EHR) and self-report data collected in the ED and randomly assign the clinicians of 4,000 patients to receive (experimental condition) or not receive (control condition) the predicted probability that their patient will make a suicide attempt after ED discharge. We will test the impact of this intervention on the suicide attempt rate and clinician decision-making. The SIG also will examine clinician acceptability and adherence, prediction model improvement, and the development of treatment optimization rules regarding patients' likelihood of benefiting from hospitalization versus alternative treatments. Our three Exploratory Projects (EXP): (EXP1) will use the SIG prediction model to identify ED patients at risk of suicidal behavior and experimentally test the effectiveness of an enhanced outreach intervention administered in collaboration with a community partner – Samaritans of Boston; (EXP2) will implement and test EHR-based risk algorithms in two inpatient units with a special focus on the use of social determinants of health to improve prediction among under-represented adolescents; and (EXP3) will test a just-in-time adaptive intervention using an innovative micro-randomized trial ...