Clinical Decision Support to Prevent Suicide

Suicide kills 132 Americans every day. The first step of suicide prevention is risk identification and prognostication. Researchers like this study team have developed and validated predictive models that use routinely collected Electronic Health Record (EHR) data like past diagnoses and medications to predict future suicide attempt risk. The study team\'s model based in machine learning is known as the Vanderbilt Suicide Attempt and Ideation Likelihood (VSAIL). VSAIL has been validated prospectively and externally to predict suicide attempt risk with a number needed to screen (NNS) of 271 for suicide attempt and 23 for suicidal ideation. NNS is the number of people who need to receive a test result to prevent one outcome - lower NNS is better. This study will evaluate the effectiveness of a Clinical Decision Support System called Vanderbilt Safecourse using VSAIL to prompt a novel Best Practice Advisory (BPA) to prompt face-to-face screening with a validated suicide screening instrument like the Columbia Suicide Severity Rating Scale (CSSRS).