Viz.ai Implementation of Stroke Augmented Intelligence and Communications Platform to Improve Indicators and Outcomes
M E Figurelle1, D M Meyer1, E S Perrinez1
1From the University of California, San Diego, San Diego, California.
Background And Purpose:
Comprehensive stroke centers continually strive to narrow neurointerventional time metrics. Although process improvements have been put in place to streamline workflows, complex pathways, disparate imaging locations, and fragmented communications all highlight the need for continued improvement.
Materials And Methods:
This Quality Improvement Initiative (VISIION) was implemented to assess our transition to the Viz.ai platform for immediate image review and centralized communication and their effect on key performance indicators in our comprehensive stroke center. We compared periods before and following deployment. Sequential patients having undergone stroke thrombectomy were included. Both direct arriving large-vessel occlusion and Brain Emergency Management Initiative telemedicine transfer large-vessel occlusion cases were assessed as were subgroups of OnHours and OffHours. Text messaging thread counts were compared between time periods to assess communications. Mann-Whitney U and Student t tests were used.
Results:
Eighty-two neurointerventional cases were analyzed pre vs. post time periods: (DALVO-OnHours 7 versus 7, DALVO-OffHours 10 versus 5, BEMI-OnHours 13 versus 6, BEMI-OffHours 17 versus 17). DALVO-OffHours had a 39% door-to-groin reduction (157 versus 95 minutes, P = .009). DALVO-All showed a 32% reduction (127 versus 86 minutes, P = .006). BEMI-All improved 33% (42 versus 28 minutes, P = .036). Text messaging thread counts improved 30% (39 versus 27, P = .04).
Conclusions:
There was an immediate improvement following Viz.ai implementation for both direct arriving and telemedicine transfer thrombectomy cases. In the greatest opportunity subset (direct arriving large-vessel occlusion-OffHours: direct arriving cases requiring team mobilization off-hours), we noted a 39% improvement. With Viz.ai, we noted that immediate access to images and streamlined communications improved door-to-groin time metrics for thrombectomy. These results have implications for future care processes and can be a model for centers striving to optimize workflow and improve thrombectomy timeliness.
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