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Workflow improvements from automated large vessel occlusion detection algorithms are dependent on care team engagement.
J Neurointerv Surg
January 2025
Department of Neurology, UTHealth Houston McGovern Medical School, Houston, Texas, USA
Background: Automated machine learning (ML)-based large vessel occlusion (LVO) detection algorithms have been shown to improve in-hospital workflow metrics including door-to-groin time (DTG). The degree to which care team engagement and interaction are required for these benefits remains incompletely characterized.
Methods: This analysis was conducted as a pre-planned post-hoc analysis of a randomized prospective clinical trial.
Beyond the : Clinician Educational Interventions May Not Be Enough to Decrease Rates of Low-Value Spinal Imaging.
AJR Am J Roentgenol
January 2025
Assistant Professor, Department of Radiology and Imaging Sciences, University of Utah Health Sciences Center.
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