Artificial intelligence-assisted software significantly decreases all workflow metrics for large vessel occlusion transfer patients, within a large spoke and hub system

Background: Artificial intelligence (AI) software is increasingly applied in stroke diagnostics. Viz LVO (large vessel occlusion) is an AI-based software that is FDA-approved for LVO detection in CT Angiography (CTA) scans. We sought to investigate differences in transfer times [from peripheral (spoke) to central (hub) hospitals] for LVO patients between spoke hospitals that utilize Viz LVO and those that do not. Methods: In this retrospective cohort study, we used our institutional database to identify all suspected/confirmed LVO transferred patients from spokes (peripheral hospitals) within and outside of our healthcare system, from January 2020 to December 2021. The “Viz transfers” group includes a ll LVO transfers from spokes within our system where Viz LVO is readily available, while the “Non-Viz transfers” group (control group) is comprised of all LVO transfers from spokes outside our system, without Viz LVO. Primary outcome included all available time metrics from peripheral CTA commen cement. Results: In total, 78 patients required a transfer. Despite comparable peripheral hospital door to peripheral hospital CTA times [20.5 (24.3) vs. 32 (45) min, p=0.28] and transfer (spoke to hub) time [23 (18) vs. 26 (13.5), p=0.763], all workflow metrics were statistically significantly s horter in the Viz-transfers group. Peripheral CTA to interventional neuroradiology team notification was 12 (16.8) vs. 58 (59.5), p
Source: Cerebrovascular Diseases Extra - Category: Neurology Source Type: research