The implementation of artificial intelligence significantly reduces door-in-door-out times in a primary care center prior to transfer

Abstract

Introduction Viz LVO 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. This analysis was performed to determine whether AI software can reduce the door-in-door-out (DIDO) time interval within the primary care center (PSC) prior to transfer to the comprehensive care center (CSC). Methods We compared the DIDO time interval for all LVO transfer patients from a single-spoke PSC to our CSC prior to (February 2017 to November 2018) and after (November 2018 to June 2020) incorporating AI. Using a stroke database at a CSC, demographics, DIDO time at PSC, modified Rankin Scale (mRS) at 90-days, mortality rate at discharge, length of stay (LOS), and intracranial hemorrhage rates were examined. Results There were a total of 63 patients during the study period (average age 69.99 ± 13.72, 55.56% female). We analyzed 28 patients pre-AI (average age 71.64 ± 12.28, 46.4% female), and 35 patients post-AI (average age 68.67 ± 14.88, 62.9% female). After implementing the AI software, the mean DIDO time interval within the PSC significantly improved by 102.3 min (226.7 versus 124.4 min; p = 0.0374). Conclusion The incorporation of the AI software was associated with a significant improvement in DIDO times within the PSC as well as CTA to door-out time in the PSC. More extensive studies are warranted to expand on the ability of AI technology to improve transfer times and outcomes for LVO patients.