Decentralised Community-Based Hub-Intermediary-Spoke Model for Rapid Cardiac Ultrasound Triage for Early Heart Failure Detection: Findings From the Heart2Miss Initiative

Abstract

Abstract Aims To evaluate the feasibility and system-level performance of Heart2Miss, a decentralised community-based triage model deploying AI-powered point-of-care ultrasound (AI-POCUS) via a hub-intermediary-spoke approach in diabetes primary care for early heart-failure (HF) detection. Methods and Results In this prospective study, 1,000 adults with diabetes and no known HF were screened over seven months across six primary care clinics (spokes); 985 with complete data were analysed. Novice biomedical and bioscience graduates underwent four-week training to perform focused three-view handheld AI-POCUS. Images were AI-analysed and verified through the hub–intermediary–spoke pathway. The primary outcome was detection of previously undiagnosed HF. Secondary outcomes included reduction in tertiary-centre burden through the hub–intermediary–spoke pathway and novice sonographer performance. 11.1% (n = 109) had Stage B (pre-HF) and 1.0% (n = 10) Stage C HF (symptomatic HF). Rapid triage ruled out abnormality in 77.3% at the spoke and a further 12.6% after intermediary TTE confirmation, reducing tertiary diagnostic burden by 89.9%. Only 1.0% required tertiary referral. Regarding novice performance, >90% analysable scans were achieved for left-ventricular parameters and >85% for left-atrial volume. After 400 scans, scan time fell from 11.0 ± 5.3 min to 8.3 ± 4.4 min (Δ 2.31 min, 95% CI 1.52–3.11; p < 0.001), and complete three-view capture improved from 88.0% to 92.2% (p = 0.035). Conclusion This decentralised hub–intermediary–spoke model combining AI-POCUS, telehealth verification, and a task-shifted bioscience workforce enabled early HF detection while substantially reducing specialist workload, supporting digital health-enabled workforce innovation and pathway redesign in resource-constrained settings.