Remote patient monitoring involves the collection of data from wearable sensors that typically requires analysis in real time. The real-time analysis of data streaming continuously to a server challenges data mining algorithms that have mostly been developed for static data residing in central repositories. Remote patient monitoring also generates huge data sets that present storage and management problems. Although virtual records of every health event throughout an individual's lifespan known as the electronic health record are rapidly emerging, few electronic records accommodate data from continuous remote patient monitoring. These factors combine to make data analytics with continuous patient data very challenging. In this chapter, benefits for data analytics inherent in the use of standards for clinical concepts for remote patient monitoring is presented. The openEHR standard that describes the way in which concepts are used in clinical practice is well suited to be adopted as the standard required to record meta-data about remote monitoring. The claim is advanced that this is likely to facilitate meaningful real time analyses with big remote patient monitoring data. The point is made by drawing on a case study involving the transmission of patient vital sign data collected from wearable sensors in an Indian hospital.
Remote patient monitoring involves the collection of data from wearable sensors that typically requires analysis in real time. The real-time analysis of data streaming continuously to a server challenges data mining algorithms that have mostly been developed for static data residing in central repositories. Remote patient monitoring also generates huge data sets that present storage and management problems. Although virtual records of every health event throughout an individual's lifespan known as the electronic health record are rapidly emerging, few electronic records accommodate data from continuous remote patient monitoring. These factors combine to make data analytics with continuous patient data very challenging. In this chapter, benefits for data analytics inherent in the use of standards for clinical concepts for remote patient monitoring is presented. The openEHR standard that describes the way in which concepts are used in clinical practice is well suited to be adopted as the standard required to record meta-data about remote monitoring. The claim is advanced that this is likely to facilitate meaningful real time analyses with big remote patient monitoring data. The point is made by drawing on a case study involving the transmission of patient vital sign data collected from wearable sensors in an Indian hospital.
Quality of life is reducing due to numerous reasons such as poor eating habits, tobacco consumption, sedentary lifestyle and stress which all taken together to lead to several and sometimes serious health problems. The scenario becomes worse in rural areas due to the limited availability of clinical facilities. Here, people have to visit hospitals, specialist doctors in cities for proper treatment and this results in waste of time, money and resources. To mitigate such problems, wearable sensors based remote patient monitoring system using IoT and data analytics has been proposed. The proposed system is specifically aimed for cardiovascular diseases and can be used to monitor the health condition of a patient even when he is at home, on the farm or any other place. The system also incorporates data analytics for the monitoring of the historical and current status of the patient’s health. The system is implemented using low-cost and compact components such as Arduino Nano, ESP8266, MAX30100, DSB1820 etc.
Wearable Sensors for COVID-19: A Call to Action to Harness Our Digital Infrastructure for Remote Patient Monitoring and Virtual Assessments