Exploring a role for MCRDR in enhancing telehealth diagnostics

Abstract

In-home telehealth devices are becoming increasingly popular when it comes to supporting the health management of home-based patients. With the devices capable of highly active monitoring, using sensors which produce large amounts of data, the deployment of telehealth devices into the home highlights the need for improved ways to collate, classify and dynamically interpret data safely and effectively. For clinicians working at a distance, the amounts of data generated by all in-home patient telematics devices poses questions on how best to intelligently filter, analyze and interpret this data to make diagnoses and respond to changes in patient conditions. In order to manage this issue, expert systems, applied for decades in other health fields, might play a role. In this paper, we explore how one type of expert system, Multiple Classification Ripple Down Rules (MCRDR), might address the issues. This paper begins by reviewing the capabilities of expert systems. Specifically, MCRDR is reviewed and its integration with an example telehealth device, MediStation, is explored. The range of potential benefits which might accrue when MCRDR and the MediStation are linked is identified as are some research and development challenges. Moving forwards, a simple simulator is introduced as one approach which is shown to be effective at exploring this exciting area of research. This paper takes the first steps towards introducing expert systems into the uHealth field and presents a simulator for this purpose.