Viz.ai Implementation of Stroke Augmented Intelligence and Communications Platform to Improve Indicators and Outcomes for a Comprehensive Stroke Center and Network.
M E FigurelleD M MeyerE S PerrinezD PaulsonJ S PannellD R Santiago-DieppaA A KhalessiDivya S BolarJ BykowskiBrett C MeyerPublished in: AJNR. American journal of neuroradiology (2022)
There was an immediate improvement following Viz.ai implementation for both direct arriving and telemedicine transfer thrombectomy cases. In the greatest opportunity subset (direct arriving large-vessel occlusion-OffHours: direct arriving cases requiring team mobilization off-hours), we noted a 39% improvement. With Viz.ai, we noted that immediate access to images and streamlined communications improved door-to-groin time metrics for thrombectomy. These results have implications for future care processes and can be a model for centers striving to optimize workflow and improve thrombectomy timeliness.
Keyphrases
- acute ischemic stroke
- quality improvement
- artificial intelligence
- healthcare
- atrial fibrillation
- primary care
- palliative care
- deep learning
- machine learning
- current status
- convolutional neural network
- type diabetes
- optical coherence tomography
- cerebral ischemia
- adipose tissue
- electronic health record
- skeletal muscle
- insulin resistance
- brain injury
- subarachnoid hemorrhage
- health insurance