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.

How does artificial intelligence in radiology improve efficiency and health outcomes?

“…The AI solutions aid in the detection of large-vessel occlusions in acute stroke and alert the stroke team to decrease the time to treatment. Preliminary studies have shown that patients might experience less disability and are thus potentially in less need of extra care later in life [24]. These potential benefits mostly lie in the long term, although the costs for the software are made in the short term by, for example, the radiology department.…”

“…AI software is used to analyze CTs and CT angiograms and notify radiologists, hub centers or even the intervention team directly when a large vessel occlusion or intracranial hemorrhage is present. Some preliminary prospective studies have shown the potential positive impact of AI in stroke care by reducing time between the CT angiography and the intervention from 281 min to 243 min, on average, and reducing length of stay (levels 4, 5) [24,25].…”

New Technology Add-On Payment (NTAP) for Viz LVO: a win for stroke care

“…10 The median loss in net monetary benefit of thrombectomy per minute was calculated to be $1059, and saving 10 min on average across the USA would save $249 million annually. 9 Implementation of Viz LVO has been demonstrated to save 66 min on average, 11 suggesting a significant return on investment for CMS.…”

Single Site performance of AI software for stroke detection and Triage

“…In recent months, several publications demonstrated the real-world impact of one such system, Viz LVO (Figure 1, left), demonstrated a reduction in time-to-treatment, reduced length of stay and improved patient outcomes, when comparing pre-and post-implementation of the AI software. In one study, Hassan et al 15 have found a significant reduction of 66 minutes in the median time from CTA acquisition at a primary stroke center (PSC) to arrival at a comprehensive stroke center (CSC), and a reduction of 2.5 days in the length of Neuro-ICU stay (p=0.00086) when comparing pre-and post-Viz LVO implementation. Morey et al 16 demonstrated both statistically significant time savings as well as statistically significant improvement in patient outcomes, as measured by NIHSS at discharge as well as 5-day and 90-day follow-ups .…”