Impact of an artificial intelligence-driven triage system on workflow and transfer efficiency: stratified analysis of 4548 admissions to four thrombectomy hubs receiving transfers from sixty spokes.

Clinical Snapshot

Key Findings

• P Value: DIDO: p<0.001, Q<0.001; DTP: p=0.003, Q=0.009

• Effect Size: DIDO: -41.6 minutes; DTP: -10.9 minutes; EVT utilisation increased by 17.8% in AI-enabled spokes

• Primary Outcome: DIDO time reduction of 41.6 minutes (95% CI -60.9 to -24.1 minutes) in AI-enabled versus non-AI spokes

• Nnt Or Sensitivity: Cost savings estimated at $3.6 million per 1000 AI-enabled spoke transfers (95% CI $1.5M to $6.1M)

• Confidence Interval: DIDO: 95% CI -60.9 to -24.1 minutes; DTP: 95% CI -17.9 to -3.7 minutes

Clinical Application

Requires AI platform implementation, staff training, and integration with existing stroke protocols Highly relevant to Australian stroke networks; aligns with National Stroke Foundation guidelines for time-critical care and could support PBS cost-effectiveness for stroke interventions Acute ischaemic stroke patients in hub-spoke healthcare networks with potential for endovascular therapy

Abstract

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); p<0.001, Q<0.001) 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). A difference-in-differences analysis demonstrated that DIDO improvements were specific to AI-enabled spokes (-27 min; 95% CI -62 to -4; p=0.029). EVT utilisation was also significantly higher in AI-enabled versus non-AI spokes where 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%, Pinteraction=0.006). DTP improvements were more pronounced at community hubs (86 (48-108) to 51 (22-77) min; adjusted difference -24.9 min; p=0.021, Q=0.041) compared with academic hubs (60 (23-87) to 55 (22-73) min; adjusted difference -15.5 min; p<0.001, Q=0.002). Subgroup analyses demonstrated consistent DIDO benefits across age, stroke severity and sex strata with no significant treatment effect heterogeneity (all P-interaction >0.05). 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.

References

1. 1.Doheim, M. F., Starr, M., Bhatt, N. R., Rocha, M., Al-Bayati, A. R., Sultany, A., Romero, C., Kenmuir, C. L., Henry, S., & Nogueira, R. G. (2026). Impact of an artificial intelligence-driven triage system on workflow and transfer efficiency: stratified analysis of 4548 admissions to four thrombectomy hubs receiving transfers from sixty spokes. Journal of Neurology, Neurosurgery, and Psychiatry. https://doi.org/10.1136/jnnp-2025-337903