Background We aimed to evaluate the impact of implementing an artificial intelligence (AI) -enabled acute ischaemic stroke triage system on workflow efficiency and transfer optimisation in a large academic healthcare network. Methods A prospectively maintained database was reviewed comparing equivalent time periods before and after AI-enabled triage platform implementation (January 2021–December 2022). The primary analysis compared workflow metrics between AI-enabled and non-AI spokes during the same calendar period (2022) to control for temporal confounding. Benjamini-Hochberg correction was applied for multiple comparisons, and analyses were adjusted for age and baseline National Institutes of Health Stroke Scale. Evaluated outcomes included door-in-door-out (DIDO) times, door-to-puncture (DTP) times, endovascular therapy (EVT) utilisation rates, cost analysis and clinical outcomes at discharge. Results The study included 4548 admissions with 844 EVT patients (394 pre-implementation, 450 post-implementation) across four hub centres. In the primary same-period analysis (2022), 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 (95% CI −60. 9 to −24. 1) ; p0. 05). Probabilistic cost analysis estimated savings of 3. 6 million (95% CI 1. 5M to 6. 1M) per 1000 AI-enabled spoke transfers. Clinical outcomes, including functional status and mortality at discharge, were similar between groups (all Q>0. 05). Conclusion Implementation of an AI-enabled triage platform was associated with significant reductions in workflow times and increased EVT utilisation, with effects specific to AI-enabled spokes rather than secular trends alone. The proportion of transfers who did not proceed to EVT decreased in AI-enabled spokes, though counterfactual outcomes for non-transferred patients remain unknown. Clinical outcomes at discharge were unchanged.
Doheim et al. (2026) studied this question.