Adventist Health + Rideout achieves 44% reduction in interfacility stroke transfer times with Viz.ai

Adventist Health Rideout deployed Computer Vision & Medical Imaging for Patient Flow & Hospital Operations in Hospital & Health System. As reported by www.viz.ai: 44% (202 min → 113 min) dido time reduction.

Maintained by Peter Korpak, Lead EditorHow evidence is checked
44% (202 min → 113 min)DIDO Time Reduction
84%CTA-to-Detection Time Reduction
45 min → 7 minCare-Team Notification Time

Source-reported figures (cited source: www.viz.ai)

Adventist Health + Rideout
Metric Before After Impact
DIDO Time 202 min 113 min 44% reduction
CTA-to-Detection Time — — 84% reduction
Care-Team Notification Time 45 min 7 min 84% reduction

What Adventist Health Rideout was trying to fix

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.

What Adventist Health Rideout deployed

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.

Evidence for Adventist Health Rideout's Patient Flow & Hospital Operations deployment

Reported outcome metrics
3 cited below
Cited source
www.viz.ai
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