Utilization of Artificial Intelligence to Improve Door-In Door-Out Times for Mechanical Thrombectomy-Eligible Patients at a Hub-and-Spoke Community-Based Comprehensive Stroke Center: A Single Case Study Presentation

Background: Delays in the transfer of patients with hyperacute stroke may lead to treatment ineligibility due to the degree of cerebral hypoxic injury suffered. Cloud-based artificial intelligence applications may improve transfer times and expand access to advanced therapies. One case between a community-based comprehensive stroke center (CSC) and a primary stroke center (PSC) using a third-party telemedicine service and shared cloud-based artificial intelligence application may provide insight in opportunities to improve stroke systems of care. Case Presentation: A 62-year-old female with a past medical history of hypertension, current everyday tobacco smoker, and marijuana user presented to an outlying emergency department (ED) with dense left-sided hemiplegia affecting the arm and leg, right-sided gaze preference, and severe dysarthria. Her last known well (LKW) time was 0900 hours. CNS imaging revealed a right middle cerebral artery occlusion, visible to members of the CSC stroke team through the use of a cloud-based artificial intelligence cell phone application. The patient was treated with intravenous thrombolytics at the PSC, and she was transferred to the CSC, where she underwent a diagnostic cerebral arteriogram with carotid artery stenting. Later, Magnetic Resonance Imaging (MRI) of the brain revealed a 3.5 cm x 2.5 cm hemorrhagic lesion in the right frontal lobe and diffusion restriction in the right frontal and right posterior temporal lobes. The patient’s hospital stay was three days and, at the time of discharged, her modified Rankin score and NIHSS were zero. She was discharged on dual antiplatelet therapy, statin therapy, and nicotine replacement. Utilization of Artificial Intelligence: Transfer delays are complicated by organizing care at PSC and CSC and can be lengthy when communication across different facilities and subspecialties. Implementing cloud-based AI image sharing in stroke systems of care has reduced DIDO times by providing rapid imaging interpretation, streamlining communication, and enhancing coordination between PSCs and CSCs. Conclusions: Our case presentation showed how a hub-and-spoke model combined with cloud-based AI utilization can improve DIDO times and enhance stroke systems of care.