VALIDATE—Utilization of the Viz.ai mobile stroke care coordination platform to limit delays in LVO stroke diagnosis and endovascular treatment

Abstract

Thousands of hospitals worldwide have adopted mobile artificial intelligence (AI)-based stroke care coordination platforms. Studies exploring the benefit of these platforms have been scrutinized due to small sample size, serial cohort design, and measurement of metrics with multiple determinants. In this large multi-center study, we evaluated the ability of an AI-based stroke care coordination platform to expedite contact with the interventionalist (NIR) for potential thrombectomy.Acute stroke consultations seen by TeleSpecialists, LLC physicians at 166 facilities (17 states) utilizing Viz.ai software (AI) vs. no AI software (non-AI) were extracted from the TeleCare by TeleSpecialists™ database from December 1, 2021, through March 31, 2022. The primary outcome was time from patient arrival to first contact with the interventionalist to discuss need for potential thrombectomy (Arrival-to-NIR notification).A total of 14,116 cases were analyzed. Compared to the non-AI cohort, Arrival-to-NIR notification in the AI cohort was: (1) 39.5 min faster (44.13% reduction, p < 0.001) in the overall analysis; (2) 33.0 min faster (34.0% reduction, p < 0.001) in the non-thrombectomy (non-TC) facility subgroup analysis; and (3) 34.0 min faster (43.59% reduction, p < 0.001) in the thrombectomy capable (TC) facility subgroup analysis. IQR range comparison demonstrated a significant improvement in uniformity of stroke workflow across all AI subgroups. Significant, albeit small, confounding biases were revealed in the data. The presence of AI within the non-TC subgroup correlated with a lower acceptance rate for thrombectomy by the NIR (delta = −10.79% absolute and 23.17% relative reduction, p < 0.0001).While this study was limited by our inability to capture detailed neuroimaging timelines and patient outcomes, it suggests a potential significant benefit of AI-based stroke care coordination platforms and underscores the critical need to development robust “big data” systems to study the effects of AI, and other emerging technologies, on stroke systems of care.