دورية أكاديمية

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

التفاصيل البيبلوغرافية
العنوان: VALIDATE—Utilization of the Viz.ai mobile stroke care coordination platform to limit delays in LVO stroke diagnosis and endovascular treatment
المؤلفون: Thomas Devlin, Lan Gao, Oleg Collins, Gregory W. Heath, Morgan Figurelle, Amanda Avila, Caitlyn Boyd, Hira Ayub, Theresa Sevilis
المصدر: Frontiers in Stroke, Vol 3 (2024)
بيانات النشر: Frontiers Media S.A., 2024.
سنة النشر: 2024
المجموعة: LCC:Medicine
مصطلحات موضوعية: artificial intelligence, deep learning, LVO detection, care coordination, acute stroke care, Medicine
الوصف: BackgroundThousands 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.MethodsAcute 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).ResultsA 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).ConclusionsWhile 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2813-3056
Relation: https://www.frontiersin.org/articles/10.3389/fstro.2024.1381930/full; https://doaj.org/toc/2813-3056
DOI: 10.3389/fstro.2024.1381930
URL الوصول: https://doaj.org/article/b4ccaad8db1b4d6a93502c53504c2609
رقم الأكسشن: edsdoj.b4ccaad8db1b4d6a93502c53504c2609
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:28133056
DOI:10.3389/fstro.2024.1381930