Towards remote monitoring and remotely supervised training

The growing number of elderly and people with chronic disorders in our western society puts such a pressure on our healthcare system that innovative approaches are required to make our health care more effective and more efficient. One way of innovating healthcare can be obtained by introducing new services that support and enable these elderly and people with chronic disorders in a more independent living and in self management with respect to their disorders. Examples of such services are remote monitoring and remotely supervised training (together RMT). Remote monitoring focuses on continuous monitoring of the health status with the assurance of assistance whenever required. Remotely supervised training focuses on efficient and effective individually tailored training anywhere and anytime with intensity not feasible in an intramural setting. It is expected that services of remote monitoring and remotely supervised treatment will become important for at least patients (safety, more in control, convenience), health care insurances (efficiency, cost reduction) and healthcare service providers (more effective care). RMT systems are in general quite complex distributed Information and Communication Technology (ICT) systems. RMT systems integrate ambulant sensing to measure relevant biosignals and (possibly) subject’s context information, secure data transport and storage, appropriate decisions support systems to assist in both technical and clinical decision making but also feedback on information to both patients and care providers. Feedback is essential for patients to make them aware of their health status, to give them a feeling of safety and to motivate and enable them to change/improve their health status. Feedback of information to healthcare professionals enables them in making appropriate decisions and to monitor changes/improvements in a patient’s health status. Despite this apparent complexity, these systems must be very dependable to be accepted and used in a healthcare setting. During the past years knowledge and experience has been gained with the development of the building blocks of RMT systems. In parallel, experience has been gained with respect to the challenges involved when using RMT systems in a clinical environment. Examples are: activity monitoring in low back pain, monitoring of spasticity, myofeedback in subjects with neck shoulder and lower back pain and post rehabilitation home training. Until now, the main focus has been on the technical realization of the sensing and transportation part of it. The development of intelligent decision support systems is still in its infancy and clinical validation studies and models how to implement these services and how to make them profitable are largely lacking. In conclusion, the combination of Biomedical Engineering with Information and Communication Technology has opened a new extensive area of research and development with a high potential to have substantial impact on our future healthcare