Professor Nguyen Van Vinh Chau, Vice Director of Ho Chi Minh City Department of Health
Associate Professor Louise Thwaites
Characterising and caring for patients with life-threatening new and emerging infectious diseases is a global priority. New technologies – wearable devices, cloud computing, and AI/machine learning – have the potential to transform how we characterise and care for these diseases. Ho Chi Minh City is suffering rapidly rising numbers of COVID-19 cases (>2000/day), which is placing enormous strain on its health care system.
The primary challenge is the rapid identification and optimal management of those with severe disease and low blood oxygen (hypoxia). There is a severe shortage of basic monitoring equipment outside of intensive care units (ICUs), which is causing fatal delays in the identification and treatment of patients needing oxygen and ICU care.
A Vietnamese tech company, iPARAMED, has developed a remote monitoring platform (see figure 1). It enables real-time monitoring of oxygen saturation (SpO2), respiratory rate, temperature, and ECG, using affordable, battery-operated, FDA-approved, wearable devices. Data are monitored centrally, enabling clinical staff to identify those with clinical warning signs and hypoxia rapidly. There is an urgent need to develop, evaluate and, if successful, implement such a system in hospital wards in Viet Nam, which can be scalable to monitor many thousands of patients safely and continuously. Such a system could rapidly be extended to multiple units caring for severely ill and critical care patients in LMICs in SE Asia, Africa, and elsewhere.
We propose a 6-month pilot project, starting immediately, with the following:
The project objective will be to provide proof of principle that a team within a low- and middle-income setting can set up a unique remote monitoring platform during a pandemic that will immediately aid clinical care and capture data for innovative research methodologies that improve clinical outcomes.
The latter will demonstrate the development of clinical decision support systems by AI/machine learning within such settings at scale. If shown to be effective, the platform will be scaled to the rest of the city and potentially the whole country.