--- title: >- Greater Houston primary stroke centers: Automated LVO detection with parallel transfer notification slug: houston-stroke-transfer stable_id: 8e195367ffc0725c company: Greater Houston primary stroke centers function_code: clinical_ops pattern_codes: - coordination_compression - exception_based_operations evidence_strength: verified publication_tier: showcase freshness: current reviewed_at: '2026-08-23' updated_at: '2026-08-22' source_quality_summary: 4 peer reviewed; publication outcomes are verified. caveat_summary: >- Observational pre/post design. Post-implementation n=35. Corresponding author reports consulting fees from Viz.AI outside the submitted work. Pre-AI median DIDO was long relative to some published networks, so effect size may not travel to faster centers. Wide confidence interval. Adjustment covariates were limited. A secular-trend test found no significant DIDO decline over calendar time (coefficient −0.05, 95% CI −0.33 to 0.23), which argues against unexplained process improvement as the whole explanation but does not prove causality. EVT after transfer rose 41.2% to 62.9% univariably (p=0.033) and was not significant after adjustment (OR 2.13, 95% CI 0.88–5.13); that secondary outcome is excluded from this catalog case. collections: - human-still-decides - queue-eliminated - regulated-autonomy bundle_version: 1.0.0 bundle_fingerprint: sha256:c23c6cc2b88153d008ea8fda928f632ce0011fc2d4c5036672a16e5d895bab93 canonical_url: https://brianletort.ai/transformations/houston-stroke-transfer --- # Greater Houston primary stroke centers: Automated LVO detection with parallel transfer notification Large-vessel occlusion at a non-thrombectomy primary stroke center has to be recognized and the receiving team activated through sequential imaging review and conventional communication before transfer can start. Function: Clinical operations. Patterns: Coordination compression; Exception-based operations. Evidence: verified. Freshness: current. Reviewed: 2026-08-23. Updated: 2026-08-22. Source quality: 4 peer reviewed; publication outcomes are verified. ## Before 1. **Emergency and telestroke team** — Receives a code-stroke patient and obtains noncontrast CT plus CTA under standard care. (control: Institutional stroke order sets and imaging protocols) 2. **Radiology / interpreting clinician** — Reviews CTA for large-vessel occlusion without an automated LVO alert. (control: Conventional PACS interpretation) 3. **Stroke and transfer coordinators** — Notifies the care team and arranges emergent transfer to a comprehensive stroke center after LVO is recognized. (control: Phone and conventional messaging; transfer eligibility judged by humans) ## After 1. **Emergency and telestroke team** — Receives a code-stroke patient; NCCT and CTA are acquired under the same standard-of-care protocols. (control: Automatic image transmission from all ED stroke workups) 2. **Viz.AI LVO algorithm** — Analyzes CTA for LVO in under five minutes on average and alerts the care-team mobile app. (control: Published algorithm performance NPV 0.99, PPV 0.65; humans must verify imaging) 3. **Stroke clinicians** — Verifies the mobile PACS finding, messages the care team, and transfers eligible patients. (control: Human confirmation of imaging and treatment eligibility; AI does not independently authorize transfer) ## Decision rights Humans retain transfer and EVT-eligibility decisions. The model does not independently dispatch the patient; clinicians must confirm imaging because positive predictive value is 0.65. ## Exception path False-positive alerts are discarded after human image review. Patients without LVO, in-hospital code strokes, and inbound transfers were outside this workflow. Nighttime evaluations at all seven sites used telestroke. ## Outcomes - **Primary-stroke-center door-in-door-out time (univariable median)** (verified): 210 minutes (IQR 140–328.5) → 133 minutes (IQR 100–167); p<0.001. Observational pre/post design. Post-implementation n=35. Corresponding author reports consulting fees from Viz.AI outside the submitted work. Pre-AI median DIDO was long relative to some published networks, so effect size may not travel to faster centers. - **Adjusted DIDO time (multivariable linear regression)** (verified): Pre-AI DIDO in the same cohort → 106-minute reduction (95% CI −165 to −48); p<0.001. Wide confidence interval. Adjustment covariates were limited. A secular-trend test found no significant DIDO decline over calendar time (coefficient −0.05, 95% CI −0.33 to 0.23), which argues against unexplained process improvement as the whole explanation but does not prove causality. EVT after transfer rose 41.2% to 62.9% univariably (p=0.033) and was not significant after adjustment (OR 2.13, 95% CI 0.88–5.13); that secondary outcome is excluded from this catalog case. ## Executive lesson In a hub-and-spoke transfer workflow, the scarce resource is parallel activation, not a faster read of the same image. Moving LVO detection and team notification off the sequential critical path cut DIDO by more than an hour; the downstream treatment-rate claim did not survive adjustment and should not be sold as the result. ## Anti-pattern Publishing a vendor percentage from a bundled quality-improvement program as if it were the isolated effect of the detection model, or treating a non-significant adjusted EVT-rate change as a confirmed clinical outcome. ## Questions for leaders - Which notification still sits on the sequential critical path after the model fires? - If the secondary clinical endpoint is not significant after adjustment, what operating metric is actually the decision-relevant outcome? - How will false positives be handled when the model's positive predictive value is 0.65? ## Sources - [Machine Learning–Enabled Automated Large Vessel Occlusion Detection Improves Transfer Times at Primary Stroke Centers](https://doi.org/10.1161/svin.123.001119) — Stroke: Vascular and Interventional Neurology (AHA / SVIN) - [Machine Learning-Enabled Automated Large Vessel Occlusion Detection Improves Transfer Times at Primary Stroke Centers (author-posted full text)](https://digitalcommons.library.tmc.edu/cgi/viewcontent.cgi?article=4648&context=uthmed_docs) — Texas Medical Center DigitalCommons / McGovern Medical School - [Abstract P266: The Implementation of Artificial Intelligence Significantly Reduces Door-In Door-Out Times in Primary Care Center Prior to Transfer](https://doi.org/10.1161/str.52.suppl_1.p266) — Stroke (AHA International Stroke Conference abstracts) - [Abstract 27: Effect Of Automated Large Vessel Occlusion Detection On Door-in-door-out Times At Primary Stroke Centers: A Multi-center Prospective Cohort Study](https://doi.org/10.1161/str.54.suppl_1.27) — Stroke (AHA International Stroke Conference abstracts)