Early experience utilizing artificial intelligence shows significant reduction in transfer times and length of stay in a hub and spoke model

Abstract

BACKGROUND: Recently approved artificial intelligence (AI) software utilizes AI powered large vessel occlusion (LVO) detection technology which automatically identifies suspected LVO through CT angiogram (CTA) imaging and alerts on-call stroke teams. We performed this analysis to determine if utilization of AI software and workflow platform can reduce the transfer time (time interval between CTA at a primary stroke center (PSC) to door-in at a comprehensive stroke center (CSC)). METHODS: We compared the transfer time for all LVO transfer patients from a single spoke PSC to our CSC prior to and after incorporating AI Software (Viz.ai LVO). Using a prospectively collected stroke database at a CSC, demographics, mRS at discharge, mortality rate at discharge, length of stay (LOS) in hospital and neurological-ICU were examined. RESULTS: There were a total of 43 patients during the study period (median age 72.0 ± 12.54 yrs., 51.16% women). Analysis of 28 patients from the pre-AI software (median age 73.5 ± 12.28 yrs., 46.4% women), and 15 patients from the post-AI software (median age 70.0 ± 13.29 yrs., 60.00% women). Following implementation of AI software, median CTA time at PSC to door-in at CSC was significantly reduced by an average of 22.5 min. (132.5 min versus 110 min; p = 0.0470). CONCLUSIONS: The incorporation of AI software was associated with an improvement in transfer times for LVO patients as well as a reduction in the overall hospital LOS and LOS in the neurological-ICU. More extensive studies are warranted to expand on the ability of AI technology to improve transfer times and outcomes for LVO patients.

Ähnliche Arbeiten