LAUSR

Key Points Question Does prediction of suicide risk improve when combining face-to-face screening with electronic health record–based machine learning models? Findings In this cohort study of 120 398 adult patient encounters, an ensemble learning approach combined suicide risk predictions from the Columbia Suicide Severity Rating Scale and a real-time machine learning model. Combined models outperformed either model alone for risks of suicide attempt and suicidal ideation across a variety of time periods. Meaning These findings suggest that health care systems should attempt to leverage the independent, complementary strengths of traditional clinician assessment and automated machine learning to improve suicide risk detection.