A Pragmatic Randomized Controlled Trial of Machine Learning-driven Clinical Decision Support to Improve Suicide Risk Screening in Ambulatory Care Settings

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.