Abstract
The rise in both ageing and chronic disease populations has highlighted a pressing demand for better access to quality healthcare at home. Meanwhile, studies have shown that home-based treatments for older patients as a substitute for hospital care can produce better clinical outcomes and reduce healthcare expenditure. However, there remains a considerable question relating to the low adoption rate of home telehealthcare technologies due to a lack of robust evidence for their cost-effectiveness. In light of both the epoch-making advancements in smartphone-centric technologies and the pervasive uptake of smartphones, we set up as our core objective the cost-effective design of a real-time home healthcare telemonitoring system based on mobile cloud computing. Our second objective was to develop a simulation environment to produce robust evidence for cost-effectiveness of a telemonitoring system so as to explore technology choices in different settings prior to moving to full-scale trials on a more scientific basis. A proof-of-concept system consisting of three main monitoring functions, namely vital sign, safety (for fall detection) and movement pattern monitoring (for real-time indoor location tracking), was developed based on a smartphone. With the exception of vital sign monitoring design which was not regarded as a search problem, the results of the other two were promising with sensitivity and specificity for successfully detected falls and recognised non-fall activities being both 95.5% and an average estimation error of 0.47 metres for real-time indoor location tracking. A large number of patients and their activities of daily living, as well as real-world like telehealthcare scenarios involving a number of different stakeholders and telemonitoring interventions, were modelled and created through simulations. The cloud-based components of our proposed telemonitoring system were also modelled and simulated together with our proposed forward-looking unused capacity-based auto scaling (FLUCAS) algorithm to enhance system performance and scalability and reduce the costs. Economic evaluations of our proposed system were conducted based on a comparative cost-effectiveness analysis approach and the results of our simulation experiments. Although exploratory, this study not only offers some insight into the great potential of smartphone-centric technologies in support of a cost-effective design of real-time home healthcare telemonitoring, but also provides justifiable evidence for cost-effectiveness of telemonitoring.