Workflow improvements from automated large vessel occlusion detection algorithms are dependent on care team engagement
In this post-hoc analysis, user engagement with software, rather than total number of users, was associated with site-specific improvements in DTG time.
Authors
- Sunil A. Sheth (ORCID: https://orcid.org/0000-0003-0602-8509)
- Saagar Dhanjani (ORCID: https://orcid.org/0009-0007-1771-2732)
- Anjan Nagesh Ballekere
- Luca Giancardo (ORCID: https://orcid.org/0000-0002-4862-2277)
- Ananya S. Iyyangar (ORCID: https://orcid.org/0009-0005-8048-3296)
- Ngoc Mai Le (ORCID: https://orcid.org/0000-0002-5395-7127)
- Eunyoung Lee (ORCID: https://orcid.org/0000-0002-9440-9119)
- E. Ebirim
- Hussain Azeem
- J. N. Samaha (ORCID: https://orcid.org/0009-0004-9964-2325)
Institutions
- The University of Texas Health Science Center (US)
- The University of Texas Medical Branch at Galveston (US)
- Rice University (US)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- Journal of NeuroInterventional Surgery
- Published
- 2025-01-27
- DOI
- https://doi.org/10.1136/jnis-2024-022896
- Citations
- 1
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- National Institutes of Health