Decentralised Community-Based Hub-Intermediary-Spoke Model for Rapid Cardiac Ultrasound Triage for Early Heart Failure Detection: Findings From the Heart2Miss Initiative
Diana Hui-Ping Foo, John Jui-Ping Yeo, Yenny Yen Yen Yeo, Stephanie Tayas Bumphray, Rose Hui-Chin Jong, Liana Lantong Sumbu, Claudia Lennya Jana, Jennett Michael, Maziah Ishak, Peter Jerampang, Maila Mustapha, Sally Suriani Ahip, Lenny Hamden, Macnicholson Igo, Mohammad Nor Azlan Sulaiman, Jawing Chunggat, Francesca Gelang Chong, Deborah Berenai Enggong, Yeequant Chung, Daniel Ishak, Angela Anthony Jalin, Sonesh Kalra, Alan Yean-Yip FongAbstract
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.