A Scalable Patient Monitoring System Using Apache Storm

The growth in wearable medical sensor-based technologies has made it possible to capture high volume physiological data of patients, both within and outside the hospital. The acquired physiological data are analyzed, usually in real-time, using a patient monitoring application for early disease detection or to detect any other changing conditions of a patient. In some cases, it is desirable to have a distributed, scalable patient monitoring system to which the physiological data of different patients can be submitted for online analysis. Such a system should be able to support the concurrent analysis of multiple data streams of different patients, allowing a clinician to remotely monitor more than one patient from a single location. This type of system also conserves resources, since in this case, there is no need to provision computational resources for every single patient being monitored. In this paper, we explore the usability of Apache Storm, an open-source real-time processing engine, in the development of such a scalable patient monitoring system. The contribution of this work, therefore, is to demonstrate that it is possible to achieve a more resource-efficient alternative to the isolated patient monitoring systems by using a distributed real-time computation platform, Apache Storm, to develop a scalable health monitoring system that can support the concurrent monitoring of multiple patients. To show how the proposed system can be developed, we describe a prototype implementation of a multi-tenant health monitoring application that monitors the arrhythmia status of multiple patients, based on a simple ECG analysis, using Apache Storm.