Adventist Health + Rideout achieves 44% reduction in interfacility stroke transfer times with Viz.ai
“Adventist Health + Rideout achieves 44% reduction in interfacility stroke transfer times with Viz.ai” documents a Patient Flow & Hospital Operations deployment in Hospital & Health System at Adventist Health Rideout. www.viz.ai reports dido time reduction: 44% (202 min → 113 min); this directory has not independently verified that result.
Evidence at a glance
- Evidence status:
- Automated evidence gate passed
- Deployment timeframe:
- Not reported by source
- Reported outcome metrics:
- 3 cited below
- Directory entry published:
- Source link checked:
The source-link check confirms reachability, not independent re-verification of every claim.
Source-reported figures — cited source: www.viz.ai
The Challenge
As a regional primary stroke center within a hub-and-spoke network, Adventist Health + Rideout faced manual bottlenecks that caused significant delays in transferring large vessel occlusion (LVO) stroke patients to comprehensive stroke centers. Average door-in-door-out (DIDO) times were 202 minutes, well above the Joint Commission's 120-minute benchmark. Delays in LVO identification and care-team notification were the primary drivers of poor outcomes.
The Solution
The hospital deployed the Viz.ai platform as part of a comprehensive quality improvement initiative, combining real-time imaging analysis and automated care coordination with standardized transfer protocols and partnership with a comprehensive stroke center. Viz.ai enabled automated LVO detection from CTA imaging and immediate care-team notification, replacing a manual, multi-step transfer process with a real-time automated workflow.
Results
Average DIDO times dropped from 202 minutes to 113 minutes — a 44% reduction that exceeded the Joint Commission's 120-minute benchmark by nearly 6%. Time from CTA completion to LVO detection fell by 84%, and care-team notification time was reduced from 45 minutes to just 7 minutes, accelerating access to endovascular reperfusion therapy.
Key Takeaways
- AI-driven imaging analysis can dramatically compress the critical window between stroke detection and transfer decision, directly impacting neurological outcomes.
- Combining technology deployment with standardized protocols and inter-hospital partnerships amplifies the impact beyond what either intervention achieves alone.
- Regional and community hospitals can meet or exceed national benchmarks for stroke care with the right AI-powered coordination tools.
Explore Related
Vendor
Details
- Industry
- Hospital & Health System
- AI Technology
- Computer Vision & Medical Imaging
- Company Size
- MidMarket
- Company
- Adventist Health Rideout
- Evidence status
- Automated evidence gate passed
- Deployment timeframe
- Not reported by source
- Directory entry published
- Source link checked
Cited source
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