Workflow improvements from automated large vessel occlusion detection algorithms are dependent on care team engagement.

Abstract

Background: 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 a randomized prospective clinical trial. ML-based LVO detection software was implemented at four comprehensive stroke centers (CSCs) from January 1, 2021, to February 27, 2022. Patients were included if they underwent endovascular thrombectomy for LVO acute ischemic stroke. ML software utilization was quantified as the total number of active users and the ratio of the number of comments to the number of patients analyzed by the software by site per week. Primary outcome was the reduction in DTG relative to pre-ML implementation by hospital utilization level. Data are expressed as median (IQR).

Results: Among 101 patients who met the inclusion criteria, the median age was 71 years (IQR 59-79), with 48.5% being female. CSC 4 had the greatest number of total active users per week (32.5 (27.5-34.5)), and comment-to-patient ratio per week (5.8 (4.6-6.9)). Increased ML software utilization was associated with improvements in DTG reduction. For every 1 unit increase in the comment-to-patient ratio, DTG time decreased by 2.6 (95% CI -5.09 to -0.13) min, while accounting for site-level random effects. Number of users-to-patient was not associated with a reduction in DTG time (β=-0.22, 95% CI -1.78 to 1.33).

Conclusions: In this post-hoc analysis, user engagement with software, rather than total number of users, was associated with site-specific improvements in DTG time.