Abstract 035: Workflow Improvements from Automated LVO Detection Algorithms Are Dependent on Care Team Engagement

Abstract

Introduction Automated machine learning (ML)‐based large vessel occlusion (LVO) detection algorithms have been shown to improve in‐hospital workflow metrics including door to groin time (DTG). The degree to which care team engagement and interaction are required for these benefits remains incompletely characterized. Methods This analysis was conducted as a pre‐planned post hoc analysis of our multicenter, prospective randomized clinical trial (NCT05838456). ML‐based LVO detection software was implemented at 4 comprehensive stroke centers in the greater Houston area in a stepped fashion between Jan 2021 and March 2022. Patients were included in this analysis if they underwent EVT for LVO AIS. Patients were excluded if they presented as inter‐hospital transfers or inpatient stroke alerts, as these workflows diverge considerably from ER‐based ones. ML‐software utilization quantified as the number of software interactions including imaging viewing and/or HIPAA‐compliant text messaging and was trichotomized at the hospital level into low/medium/high. Primary outcome was the reduction in DTG relation to pre‐ML implementation by hospital utilization level. Secondary outcomes included CT to groin puncture time, and sensitivity analyses in subsets of patients who did and did not have an LVO alert sent. Results Among 243 patients that met inclusion criteria, median age was 70 (IQR 58‐79), 50% were female and median NIHSS was 17 (IQR 11‐22). ML‐software utilization varied considerably with high‐utilization centers (n=2) interacting with mean 160 times, medium (n=1) 64 times and low center (n=1) 42 times during the study period. We observed a reduction on DTG of 11 minutes in the high‐interaction center (p<0.01) but no significant reduction in DTG in the medium or low‐interaction centers. Similarly, time from CT scan initiation to groin puncture fell in the high‐utilization centers by 32 minutes (p<0.05) but no significant change in others. Without adjusting for hospital‐level software utilization, there was no statistically significant reduction in DTG for patients analyzed by the ML software versus those that were not (p=0.35). Conclusion ML‐based workflow improvements are dependent on care team adoption and utilization.