Globally, an increasing number of elderly people living alone in the community have existing medical conditions and are in need of immediate support during medical emergencies. The most critical parameter of any medical emergency is response time. However, the arrival of ambulance often is delayed and may result in rapid deterioration of the patient's condition. On the other hand, the number of emergency calls to ambulances made by panicked elderly persons is often determined to be 'false alarms' resulting in wastage of ambulance resources.
In response to such scenario, Dr Sayan Ray and Dr Akbar Hossain of MIT's School of Digital Technologies are designing and developing a Technology-Assisted Medical Emergency System (TAMES) to support the elderly during medical emergencies. TAMES will integrate a list of informal caregivers, in the form of friends, families and neighbours, together with the existing system of formal caregivers such as ambulance paramedics and medical professionals, to provide an end-to-end solution. The informal caregivers, as first responders in medical emergencies, can initially attend the patients before the arrival of the ambulance and can accordingly escalate the calling of the ambulance and can arrange any further actions required. To shortlist and select the most appropriate informal caregiver for a specific emergency, a context-aware recommendation system will be developed, as part of TAMES, based on different contextual parameters of patients and informal caregivers. The system uses IoT-based monitoring of patients and machine learning techniques to shortlist informal caregivers. This project has the potential to benefit not only the elderly and medical emergency services, but also District Health Boards, rest homes and retirement villages.