Ali Samadani1, Daniel Schulman1, Portia Singh1, Mladen Milosevic1
12:00 - 14:00 | Tue 7 Nov | Auditorium Foyer, E1/E2, Upper Atrium Space | TPO.2
Personal emergency response systems (PERS) such as Philips Lifeline help seniors maintain independence and age in place. PERS can use predictive analytics to help risk stratification and promote response-efficient emergency services. This paper presents a framework for estimating significant associations between Lifeline user characteristics and occurrence of emergency events. Predictive variables including demographics, health conditions, environmental, and user-specific lifeline history were identified and their associations to emergency events were delineated. The predictive variables can help with 1)~identifying individuals at high risk and 2)~management and prioritization of care and preventive services, which can result in reducing adverse health events and improving user's quality of life.