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).

The investigators seek to study if identifying patients at high predicted risk of suicide in clinical settings where suicide risk screening only happens sporadically, if at all, will improve face-to-face screening rates and documentation of suicide risk assessment in their EHRs.

The investigators will measure the VSAIL-prompted BPA's effectiveness in real-world clinical settings to increase rates of face-to-face suicide risk screening. VSAIL requires only data already collected in routine clinical encounters and is calculated in real- time (seconds) at the start of a clinical visit (inpatient or outpatient) at VUMC.

VSAIL does not replace clinical judgment in treating suicidality, but the investigators seek to measure whether VSAIL increases the rates at which the important problem of suicide is addressed and screened effectively.

The investigators seek to compare an active, Interruptive intervention, a VSAIL-prompted BPA pushed to providers, to a passive, non-interruptive visual prompt to determine if 1) CDS driven by automated risk modeling improves face-to-face screening rates and 2) whether or not that CDS needs to be interruptive or non-interruptive to be effective. In the latter case, effective non-interruptive CDS would improve care without worsening "alert fatigue." For equipoise, risk scores for all patients in the study sites would be made available in Epic flowsheets for review by providers if they choose to do so.

In the first phase, The investigators will pilot this CDS in Neurology outpatient clinics for six months. If study goals are met, The investigators will scale the CDS intervention trial across non-mental health specialty settings at VUMC over the following 18 months.

Inclusion Criteria:

1. > 18 years of age
2. A visit in Neurology (first phase) or other non-mental health specialty setting at VUMC (second phase)

Exclusion Criteria:

1) A CSSRS conducted within one week of the visit in another care setting at VUMC (recent screening)

Those randomized to intervention arm will have the intervention BPA prompt. Those randomized to the control arm will not have an associated BPA prompt. Risk predictions for all participants in study settings will be visible in eStar flowsheets for equipoise.