Stroke Detection and Triage Automation Workflow

This workflow automates the most time-sensitive bottleneck in acute stroke care: the manual review and communication loop between imaging, radiology, and neuro-interventional teams. By deploying high-speed, multimodal AI agents to analyze non-contrast CT and CTA/MRA scans for large vessel occlusion (LVO) and hemorrhage, it converts imaging data into actionable, prioritized alerts within minutes. The operational upside is direct: shaving 15-30 minutes off door-to-needle time directly correlates with reduced disability and mortality, while also optimizing expensive cath lab and specialist utilization across the health system.

Implementation requires deep integration with hospital alerting (Spok, TigerConnect), EHRs (Epic, Cerner), and PACS. The orchestrator, built on frameworks like LangGraph, must manage inference pipelines, enforce quality gates, and route low-confidence studies for human review. Critical controls include redundant alerting, override logging, and real-time performance monitoring to ensure the system augments, rather than replaces, clinical judgment while operating under strict HIPAA and patient safety governance.

The workflow is triggered by a completed non-contrast head CT or MRI study arriving in PACS. An orchestrator, typically built on LangGraph or a custom microservice, immediately retrieves the DICOM series and relevant patient context from the EHR via FHIR APIs. It validates study quality and dispatches the imaging data to a high-performance inference service hosting specialized models for large vessel occlusion (LVO) and hemorrhage detection, running on GPU-accelerated infrastructure to meet sub-minute latency requirements.

Outputs are fused into a structured finding with a confidence score and an urgency tier (e.g., Critical, Routine). For Critical findings, the orchestrator triggers immediate alerts via integrated hospital communication systems (e.g., Spok, TigerConnect) to the neuro-interventional team, while simultaneously logging a preliminary note in the EHR and updating the radiologist's worklist for final sign-off. The architecture mandates immutable audit logs of all AI inferences, physician overrides, and alert actions to satisfy clinical governance, with performance monitoring built into the pipeline to detect model drift against a gold-standard registry of expert annotations.

This workflow automates the initial, time-sensitive bottleneck in acute stroke care: the manual review of a non-contrast CT scan to rule out hemorrhage and identify a candidate for thrombectomy. The operational upside is direct—every minute saved in detection and triage preserves an estimated 1.9 million neurons. The architecture must orchestrate high-speed inference from PACS ingestion, apply deterministic rules for LVO scoring, and integrate with hospital alert systems (e.g., paging, secure chat) and the EHR to pull relevant patient context, all while maintaining a full audit trail for clinical governance and liability.

Implementation requires deploying containerized inference models on hospital-grade GPU clusters, with the orchestrator built on LangGraph for state management across preprocessing, parallel model inference, and scoring. Critical controls include a human-in-the-loop gate for all positive detections before definitive treatment, with overrides logged to the EHR. Integration points are HL7/FHIR for EHR context and REST APIs to hospital communication platforms. Observability is non-negotiable: dashboards must track door-to-alert time, model confidence drift, and false-negative rates, triggering retraining pipelines when performance degrades beyond predefined clinical safety thresholds.

Governance begins with a locked-down pilot environment, where the AI's LVO and hemorrhage detection outputs are silently logged against a gold-standard neuroradiologist panel. No alerts are triggered to clinical teams during this validation phase. The orchestration layer must integrate with the hospital's PACS and alerting middleware (e.g., Spok, Epic Haiku) but remain in 'monitor-only' mode. This phase establishes baseline performance metrics, false-positive rates, and refines the confidence scoring logic that will later gate autonomous alerts, ensuring the system meets safety thresholds before affecting patient flow.

Rollout proceeds in stages: first to non-critical hours, then expanding to 24/7 coverage. Each alert must be accompanied by the original DICOM, the AI segmentation overlay, and a confidence score in the communication. A parallel audit system logs every AI finding and subsequent clinician action, creating a feedback loop for continuous model validation and drift detection. The final governance layer is a weekly multidisciplinary review board (neuroradiology, neurology, IT) that examines false negatives, overrides, and system latency to iteratively tighten protocols and ensure the automation sustains its reduction in door-to-needle time without compromising diagnostic safety.