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K. Sahu



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    EP04 - e-Poster Session 4 (ID 55)

    • Event: e-Health 2018 Virtual Meeting
    • Type: e-Poster Session
    • Track: Clinical Delivery
    • Presentations: 1
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      EP04.07 - Commercialization Landscape for mHealth and eHealth in Canada (ID 153)

      K. Sahu, School of Public Health and Health System, University of Waterloo; Waterloo/CA

      • Abstract
      • Slides

      Purpose/Objectives: The primary objective of this literature review was to understand the commercialization landscape relevant to eHealth and mHealth in Canada. In the recent few years, eHealth and mHealth focused apps have been growing at a significant rate and that has brought in a unique set of challenges in a knowledge-driven economy like Canada. This literature review will help to further identify those challenges and the kind of opportunities can come out of it from the discussion. As well as, this research will help bridge the gap between various important key stakeholders such as the academic world, clinical practitioners, and the medtech industry itself. The ultimate motivation for this discussion is to enable further collaboration among the key stakeholders pertaining eHealth and mHealth in Canada

      Methodology/Approach: 1. Scoping review of eHealth and mHealth publications with the focus on commercialization of such technology within the different healthcare systems in the world. 2. Interview key stakeholders who play a key role within Canada for commercializing new eHealth and mHealth technology. 3. Use Levac’s methodology to identify papers which describe barriers and opportunities to commercialization of eHealth and mHealth focused solutions in Canada

      Finding/Results: The initial literature searches have identified several gaps that have limited the commercialization of mHealth technology: 1. Lack of consistent terminology defining eHealth and mHealth technology within literature and in work practices, 2. Lack of definition between go to market tools vs. wearables vs. EHR’s vs. efficiency in Canadian and North American Markets, 3. Not enough multidisciplinary insight on mHealth and eHealth technology success in the long run in the current healthcare system. This literature review and discussion would be developing recommendations related to 1. How eHealth and mHealth are defined in what context, 2. What are tools, resources, and organizations in place within Canada to foster eHealth and mHealth focused innovation within the healthcare system? 3. What type of strategic initiatives need to take in order to foster longer implementation plan for new innovative eHealth and mHealth solutions to be better integrated.

      Conclusion/Implications/Recommendations: eHealth and mHealth technology use in Canada, both in the research and industry domains, have been increasing significantly in the last ten years. Such use of technology has shown to help various parts of the population be better aware of their health, help the healthcare team able to remotely monitor the patients’ vitals, and help the overall healthcare system become more proactive. Public healthcare in Canada is a very complex system and there are various stakeholders that play key roles when it comes implementing and deploying a new form of technology on a systems level. In this project, we are finding the current barriers and opportunities that are prevalent with implementing eHealth and mHealth within the Canadian landscape by doing an initial literature review and as well as interviewing key figures within the field in order to bring together relevant information for innovators.

      140 Character Summary: Understand how commercialization of mHealth and eHealth works within a public healthcare system like Canada, exploring barriers and enablers.

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    OS01 - Better Care Outcomes through Data and Analytics (ID 8)

    • Event: e-Health 2018 Virtual Meeting
    • Type: Oral Session
    • Track: Technical/Interoperability
    • Presentations: 1
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      OS01.01 - Enabling Public Health Surveillance Using IoT Data (ID 198)

      K. Sahu, School of Public Health and Health System, University of Waterloo; Waterloo/CA

      • Abstract
      • Slides

      Purpose/Objectives: The purpose of this project is to explore individual behavioral patterns, trends, and facilitate remote patient monitoring using data from IoT sensors. The focus of this study is to understand individual- and population-level behaviors based on smart thermostat data extracted from the ecobee database. This data will be used for remote patient monitoring, population-level insights, and monitoring of the progression of chronic diseases.

      Methodology/Approach: ecobee, a Canadian smart home technology company, currently has the technology to collect temperature and motion data from thermostats and remote sensors. The motion data from the thermostat and remote room sensors have the capacity to capture the amount of indoor physical activity, sleep patterns, sedentary behavior, and ultimately inform on various health conditions (such as dementia and mental health). Presently, our research team has access to data collected by ecobee’s Smart WiFi Thermostats owners who consented to having their data shared in the ecobee’s Donate Your Data (DYD) program. Our sample data comes from 10,251 households with a ranging number of remote sensors, predominantly located in the US and Canada, over a two-year period. For the purpose of creating a computational environment to store, transform, and deliver the data, this study will follow a typical big data software architecture. In order to explore and analyze the data, we are going to use Python machine learning.

      Finding/Results: Currently, we are in the process of analyzing ecobee’s entire DYD dataset. In phase-1, using machine learning, we will apply pattern recognition on the dataset and expect to identify variations in user behavior. Our algorithms will identify unique patterns at different levels, as for example intra- and inter-day variations. They will also be able to predict the micro- and macro-level behavior patterns, such as the time of the day or sleeping vs. awake. Ultimately, we will leverage these algorithms to identify abnormal behavior within a household to be used as an early warning for deviations health status. In the following phases, our prototype will display the statistics and summary of activity in a dashboard that public health agencies will be able to access and visualize the data in near-real time. Aggregate data will be displayed in near real-time to enable public health agencies to monitor activity across Canada’s different regions. At a later stage, deep learning will be used to identify challenging areas and forewarnings will be provided via map visualizations.

      Conclusion/Implications/Recommendations: This project is pioneering improvements to public health surveillance: (1) a novel data source; (2) new indicators for physical activity, sleeping habits, and sedentary behaviour; (3) novel tools/solutions for capturing data; and (4) creating newly linked datasets combined from a variety of sources including IoT sensors. Data collection has never been achieved on such a granular level (5-minute intervals) and in virtually real-time. The Public Health Agency of Canada (PHAC) has yet to use in-home monitoring via sensors. This study will open opportunities for PHAC to go beyond traditional methods for public health surveillance.

      140 Character Summary: Remote monitoring of health indicators at the population level using motion data from ecobee’s smart thermostat (IoT) and machine learning.

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