Decentralized community-based hub-intermediary-spoke model for rapid cardiac ultrasound triage for early heart failure detection: findings from the Heart2Miss initiative.
Authors
Affiliations (11)
Affiliations (11)
- Clinical Research Centre, Sarawak General Hospital, Kuching 93586, Malaysia.
- Klinik Kesihatan Kota Sentosa, Kuching 93250, Malaysia.
- Klinik Kesihatan Petra Jaya, Kuching 93050, Malaysia.
- Klinik Kesihatan Tanah Puteh, Kuching 93450, Malaysia.
- Klinik Kesihatan Jalan Masjid, Kuching 93000, Malaysia.
- Klinik Kesihatan Kota Samarahan, Kota Samarahan 94300, Malaysia.
- Klinik Kesihatan Batu Kawa, Kuching 93250, Malaysia.
- Us2.ai, Singapore, Singapore.
- Department of Neurosurgery, Neuroscience Institute, Penn State Hershey Medical Centre, Hershey 17033, USA.
- AstraZeneca (Malaysia) Sdn Bhd, Selangor, Malaysia.
- Department of Cardiology, Sarawak Heart Centre, Kota Samarahan 94300, Malaysia.
Abstract
To evaluate the feasibility and system-level performance of Heart2Miss, a decentralized 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. In this prospective study, 1000 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 4-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% (<i>n</i> = 109) had Stage B (pre-HF) and 1.0% (<i>n</i> = 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; <i>P</i> < 0.001), and complete three-view capture improved from 88.0% to 92.2% (<i>P</i> = 0.035). This decentralized 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.