Abstract WP45: VISIION-S: Viz.ai Implementation Of Stroke Augmented Intelligence And Communications Platform To Improve Indicators And Outcomes For A Comprehensive Stroke Center And Network - Sustainability

Background: As Comprehensive Stroke Centers (CSCs) strive to improve neurointerventional (NIR) times, process improvements have been put in place to streamline workflows. Our prior publication (VISIION) demonstrated an improvement in key performance indicators (KPIs) in our CSC. The purpose of this study is to analyze whether the positive results demonstrated were sustainable. Methods: Sequential stroke NIR patients being Direct Arriving LVO (DALVO) and telemedicine transfer LVO (BEMI) cases were assessed, including subgroups of DALVO-OnHours, DALVO-OffHours, BEMI-OnHours, and BEMI-OffHours. We analyzed times for the original 6 months pre (6/10/20-1/15/21) and compared them to a 17 months post-implementation (1/16/21- 6/25/22) to evaluate for sustainability. Mann-Whitney U was utilized. Results: 150 NIR cases were analyzed pre (n=47) v. post (n=103) Viz.ai implementation (DALVO-OnHours 7 v. 20, DALVO-OffHours 10 v. 25, BEMI-OnHours 13 v. 20, BEMI-OffHours 17 v. 38). For Door-to-groin (DTG) assessments, improvement was noted for DALVO-OffHours 39% (157min,96min;p Conclusions: Consistent with our initial 6 month post-implementation pilot, we noted sustainability over a 17 month period with sustained reduction in KPIs for numerous key NIR subgroups. In the greatest opportunity subgroup (DALVO-OffHours), requiring team mobilization off hours without benefit of telemedicine transfer lead time, we noted a significant reduction in all 5 time metrics. Our sustainability finding is important to show that process improvements continued even after the immediate period, making a Hawthorne effect less likely and adding credibility to the results. Models such as this, could be useful for other centers striving to optimize workflow and improve NIR times.