Telemedicine technologies offer the opportunity to frequently monitor patients' health and optimize management of chronic illnesses. Given the diversity of home telemedicine technologies, it is essential to compose heterogeneous telemedicine components and systems for a much larger patient population through systems of systems. The objective of this research is to thoroughly investigate the heterogeneity in large-scale telemedicine systems for cardiology patients. To accomplish this task, this research seeks to develop (i) a novel open source platform medical device interface adapter that can seamlessly interconnect medical devices that conform to interoperability standards, such as IEEE 11703, to smart phones for real-time data processing and delivery; (ii) a set of novel supporting technologies for wireless networking, data storage, and data integrity checking, and (iii) a learning-based early warning system that adaptively changes patient and disease models based on medical device readings and context.
Cardiovascular disease is a major health problem and the leading cause of death in the United States. Telemedicine technologies developed in this project have the potential to dramatically improve the quality of home health care with low cost for medical community. This research acquires a clear understanding of how to address heterogeneity issues in telemedicine systems, which allows effectively compose and interconnect individual telemedicine components together with low cost. For developers, this research significantly reduces the design and development costs for building interoperable large-scale telemedicine systems. The research content is being integrated into undergraduate, graduate system courses, clinician training course as well as high school research projects.
