Stroke LVO Imaging Triage and Transfer Coordination
AI-assisted workflow for detecting suspected large vessel occlusions on stroke imaging, notifying care teams, and coordinating hub-and-spoke thrombectomy triage and transfers across hospital networks to reduce treatment decision delays and unnecessary transfers.
Business Blueprint
GROUNDEDAI flags suspected large-vessel-occlusion stroke cases on imaging, alerts the right clinicians at once, and helps hub-and-spoke networks make transfer decisions faster.
The Problem
Stroke networks need to deliver time-critical thrombectomy triage across geography, staffing constraints, mixed systems, and complex transfer handoffs; the old sequential workflow delays shared clinical decisions and transfer coordination.
Stroke medical directors and stroke program leaders
They are accountable for delivering world-class stroke care when geography, resources, and time work against the network.
Stroke coordinators and transfer coordinators
They manage a workflow that previously moved from CT to radiology read, teleneurology consult, transfer center, manual image transfer, specialist review, and then transfer.
Emergency, radiology, and neurology teams
They need to move from waiting on one another to reviewing the same suspected stroke case in real time.
Hub thrombectomy teams
They receive transfer demand from many spokes and need earlier image access and clearer clinical insight before accepting or redirecting transfers.
Cost of Inaction
Continuing the old workflow means longer door-in-door-out and door-to-puncture intervals, less transfer optimisation, and missed cost-saving opportunity; one deployment reported: "Probabilistic cost analysis estimated savings of $3.6 million (95% CI $1.5M to $6.1M) per 1000 AI-enabled spoke transfers."
Process Fit
Diagnostic & decision supportAs-Is
Suspected stroke patients arrive at a spoke site, receive CT imaging, wait for radiology interpretation and teleneurology consultation, then move through transfer-center coordination, manual image transfer, specialist review, and transfer decisions.
To-Be
The AI reviews stroke imaging immediately, notifies emergency, radiology, and neurology stakeholders in parallel, makes imaging accessible to the hub, and lets teams review and decide together in real time.
Human Checkpoints
- Radiology review of imaging findings before care activation or escalation — Radiologist
- Neurology and stroke specialist review of suspected LVO and treatment path — Neurologist or stroke specialist
- Transfer acceptance and routing decision after hub team reviews imaging — Hub stroke team and transfer center
Systems Touched
Business Cycle
Upstream
- Spoke hospitals must have rapid CT imaging and a radiology workflow for suspected stroke patients.
- The network needs defined hub-and-spoke relationships for thrombectomy transfer routing.
- Care teams need agreed notification coverage so emergency, radiology, neurology, and hub reviewers receive the alert at the same time.
Downstream
- Transfer decisions move from incomplete information toward clearer clinical insight.
- Door-in-door-out time and LVO transfer time improve after implementation.
- EVT utilisation can increase at AI-enabled spokes.
Value Evidence
- Door-in-door-out timeREDUCED
Door-in-door-out time reduced by 32 minutes
- Large vessel occlusion transfer timeREDUCED
Large vessel occlusion transfer time reduced by ~30% (133 → 94 minutes)
- Treatment initiation speedIMPROVED
Faster treatment initiation (door-to-needle time improved by ~16%)
- Door-in-door-out time versus non-AI spokesREDUCED
AI-enabled spokes demonstrated significantly shorter DIDO times compared with non-AI spokes (median 103 (92-118) vs 134 (103-162) min; adjusted difference -41.6 min
- Door-to-puncture timeREDUCED
and shorter DTP times (21 (14-43) vs 40 (18-65) min; adjusted difference -10.9 min (95% CI -17.9 to -3.7); p=0.003, Q=0.009).
- EVT utilisation rateINCREASED
AI-enabled spokes had increased EVT rates by +17.8% (39.3% to 57.1%) compared with +1.1% in non-AI spokes (41.3% to 42.4%, P interaction =0.006).
- Transfer-related costREDUCED
Probabilistic cost analysis estimated savings of $3.6 million (95% CI $1.5M to $6.1M) per 1000 AI-enabled spoke transfers.
ROI Estimator
EstimateKPI
Projected Annual Change — Large vessel occlusion transfer time
—
Based on observed result at 1 operator — verify against your own baseline.
Adoption Journey
LEVEL 1 — QUICK WIN
Gate: Prove value on one high-volume spoke-to-hub suspected LVO pathway.
Outcome: A small group can validate that AI alerts and shared image access support faster parallel review without changing final clinical accountability.
LEVEL 2 — STANDARD
Gate: Prove value in live production across emergency, radiology, neurology, and transfer-center handoffs.
Outcome: The organization gets a standard operating model for AI-triggered review, human confirmation, hub consultation, and transfer decisioning.
LEVEL 3 — ADVANCED
Gate: Prove value across multiple referral sites with different EMRs, imaging systems, and workflows.
Outcome: The network can run common LVO triage and transfer metrics across a broader hub-and-spoke footprint.
LEVEL 4 — ENTERPRISE
Gate: Prove value as a stroke coordination layer across triage, imaging review, transfer readiness, and performance management.
Outcome: The business gets a platform-style stroke command workflow that connects detection, notification, human decision, transfer coordination, and outcome monitoring.
Detailed per-level builds in the solution spectrum below
Risk & Governance
AI alert is mistaken or incomplete.
Posture: Keep radiology and stroke specialists as required human reviewers; treat the AI as triage acceleration, not an autonomous diagnosis or transfer order.
Fragmented EMRs, imaging systems, and workflows create inconsistent adoption across spokes.
Posture: Standardize integration requirements, alert routing, site onboarding checklists, and downtime procedures before scaling beyond the first sites.
Faster notification could drive unnecessary transfers if the workflow does not require multidisciplinary confirmation.
Posture: Require hub review of imaging and documented transfer decision criteria before activating transport.
Reported performance may not translate to every hospital in the network.
Posture: Track local DIDO, DTP, EVT utilisation, transfer volume, and clinical outcomes before and after rollout at each wave.
Operating Intelligence
How it works
AI runs the operating engine in real time.
Humans govern policy and overrides.
Measured outcomes feed the optimization loop.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Sense
Step 2
Optimize
Step 3
Coordinate
Step 4
Govern
Step 5
Execute
Step 6
Measure
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI senses, optimizes, and coordinates in real time. Humans set policy and override when needed. Measurements close the loop.
The Loop
6 steps
Sense
Take in live demand, capacity, and constraint signals.
Optimize
Continuously compute the best next allocation or action.
Coordinate
Push those actions into systems, channels, or teams.
Govern
Humans set policies, objectives, and overrides.
Authority gates · 1
The system must not make a final diagnosis, determine thrombectomy eligibility, or accept a transfer without hub clinician judgment. [S1]
Why this step is human
Policy decisions affect the entire operating envelope and require organizational authority to change.
Execute
Run the approved operating loop continuously.
Measure
Measured outcomes feed back into the optimization loop.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Stroke LVO Imaging Triage and Transfer Coordination implementations:
Key Players
Companies actively working on Stroke LVO Imaging Triage and Transfer Coordination solutions:
Real-World Use Cases
AI-assisted large vessel occlusion detection for stroke thrombectomy triage
The software looks at CT angiography brain scans and flags possible major stroke-causing blood clots so the stroke team can act faster.
AI-powered stroke transfer triage and care coordination for suspected large vessel occlusion
The system reviews stroke brain vessel imaging and quickly alerts the right care team so they can decide whether a patient should stay at the current hospital or be transferred for advanced stroke treatment.
AI-orchestrated hub-and-spoke stroke care workflow
When a possible stroke patient gets a CT scan at a smaller hospital, AI checks the images right away and alerts the emergency, radiology, neurology, and transfer teams at the same time so they can decide quickly whether the patient needs treatment or transfer to the hub hospital.