13234
clinical study

Enhancing Stroke Care Efficiency through AI Vendor Transition: A Comparative Study of Workflow Metrics Before and After Implementation

Materials & Methods

This retrospective analysis examined 354 acute stroke cases to compare workflow metrics before and after transitioning AI vendors. The pre-implementation phase, covering Oct. to Nov. 2022, used Vendor A (RapidAI-IschemaView), while the post-implementation phase, spanning Oct. to Nov. 2023, assessed Vendor B (Aidoc). A one-month exclusion buffer (Sept. 2023) allowed for the onboarding and implementation of the new AI system. The study measured key workflow metrics, including door-to-CTA completion time and time to intervention, to evaluate the efficiency of the AI transition.

Results

The transition to Vendor B resulted in workflow improvements across key performance metrics. The door-to-CT initiation time decreased from 18.5 minutes to 9.5 minutes, while the time from door-to-CT results improved significantly, reducing by 24% (from 29.5 to 22.5 minutes) for the neurovascular team and by 25% (from 32 to 24 minutes) for the radiology team. Although the door-to-CTA completion time showed only a slight 5% improvement, the most impactful change was the reduction in median door-to-skin puncture time, which decreased by 34% from 113 minutes to 75 minutes, aligning with the institutional target. Additionally, the number of thrombectomies performed per month doubled, increasing from 2.5 to 5 cases.

Conclusions

This study demonstrates that transitioning to a new AI vendor improved workflow efficiency in acute stroke care, particularly by reducing door-to-skin puncture time and meeting institutional benchmarks. The findings highlight the importance of selecting an AI vendor that optimally integrates with clinical workflows to maximize efficiency. Future research will focus on a more detailed analysis of the specific contributions of AI-driven workflow optimization and diagnostic capabilities in improving stroke care outcomes.

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