The Experts below are selected from a list of 22092 Experts worldwide ranked by ideXlab platform
Ying Wang - One of the best experts on this subject based on the ideXlab platform.
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Opportunity Exploitation in Mobile Health Entrepreneurship Research-in-Progress
2020Co-Authors: Ying WangAbstract:Mobile Health poses an entrepreneurial opportunity for Healthcare providers, especially physicians who run their clinics individually or jointly. Based on entrepreneurship literature, this study examines the adoption of Mobile Health technologies in terms of the factors that influence the decisions of physicians to exploit the opportunity. Compared with other Health information technologies, the direct users of Mobile Health technologies are patients rather than clinicians. Thus this study discusses the important roles that demand-side factors related to patient-centered care play in physicians’ adoption of Mobile Health technologies. To facilitate future empirical studies, it proposes a research model of Mobile Health entrepreneurship with testable research propositions. The framework fills the gap in existing technology adoption studies that typically do not differentiate technology adopters and end-users. It also contributes to the entrepreneurship literature that considers mainly the characteristics of entrepreneurs in the investigation of opportunity exploitation.
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ICIS - Opportunity Exploitation in Mobile Health Entrepreneurship
2014Co-Authors: Ying WangAbstract:Mobile Health poses an entrepreneurial opportunity for Healthcare providers, especially physicians who run their clinics individually or jointly. Based on entrepreneurship literature, this study examines the adoption of Mobile Health technologies in terms of the factors that influence the decisions of physicians to exploit the opportunity. Compared with other Health information technologies, the direct users of Mobile Health technologies are patients rather than clinicians. Thus this study discusses the important roles that demand-side factors related to patient-centered care play in physicians’ adoption of Mobile Health technologies. To facilitate future empirical studies, it proposes a research model of Mobile Health entrepreneurship with testable research propositions. The framework fills the gap in existing technology adoption studies that typically do not differentiate technology adopters and end-users. It also contributes to the entrepreneurship literature that considers mainly the characteristics of entrepreneurs in the investigation of opportunity exploitation.
Miodrag Potkonjak - One of the best experts on this subject based on the ideXlab platform.
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mHealthMon: Toward Energy-Efficient and Distributed Mobile Health Monitoring Using Parallel Offloading
Journal of Medical Systems, 2013Co-Authors: Jong Hoon Ahnn, Miodrag PotkonjakAbstract:Although Mobile Health monitoring where Mobile sensors continuously gather, process, and update sensor readings (e.g. vital signals) from patient's sensors is emerging, little effort has been investigated in an energy-efficient management of sensor information gathering and processing. Mobile Health monitoring with the focus of energy consumption may instead be holistically analyzed and systematically designed as a global solution to optimization subproblems. This paper presents an attempt to decompose the very complex Mobile Health monitoring system whose layer in the system corresponds to decomposed subproblems, and interfaces between them are quantified as functions of the optimization variables in order to orchestrate the subproblems. We propose a distributed and energy-saving Mobile Health platform, called mHealthMon where Mobile users publish/access sensor data via a cloud computing-based distributed P2P overlay network. The key objective is to satisfy the Mobile Health monitoring application's quality of service requirements by modeling each subsystem: Mobile clients with medical sensors, wireless network medium, and distributed cloud services. By simulations based on experimental data, we present the proposed system can achieve up to 10.1 times more energy-efficient and 20.2 times faster compared to a standalone Mobile Health monitoring application, in various Mobile Health monitoring scenarios applying a realistic mobility model.
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Toward energy-efficient and distributed Mobile Health monitoring using parallel offloading
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013Co-Authors: Jong Hoon Ahnn, Miodrag PotkonjakAbstract:Although Mobile Health monitoring where Mobile sensors continuously gather, process, and update sensor readings (e.g. vital signals) from patient's sensors is emerging, little effort has been investigated in an energy-efficient management of sensor information gathering and processing. Mobile Health monitoring with the focus of energy consumption may instead be holistically analyzed and systematically designed as a global solution to optimization subproblems. We propose a distributed and energy-saving Mobile Health platform, called mHealthMon where Mobile users publish/access sensor data via a cloud computing-based distributed P2P overlay network. The key objective is to satisfy the Mobile Health monitoring application's quality of service requirements by modeling each subsystem: Mobile clients with medical sensors, wireless network medium, and distributed cloud services. By simulations based on experimental data, we present the proposed system can achieve up to 10.1 times more energy-efficient and 20.2 times faster compared to a standalone Mobile Health monitoring application, in various Mobile Health monitoring scenarios applying a realistic mobility model.
Upkar Varshney - One of the best experts on this subject based on the ideXlab platform.
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AMCIS - A Taxonomy for Mobile Health Implementation and Evaluation
2016Co-Authors: Alan Thorngeng Yang, Upkar VarshneyAbstract:We develop a taxonomy of research papers on the topic of Mobile Health project implementation and evaluation. The paper begins with a literature review on the topics of taxonomy, Mobile Health, and project evaluation. Following this review is an analysis of the research opportunities in the information systems field and an argument for the application of a categorization system guided by empirical evidence, specifically a taxonomy. We then create a taxonomy of the Mobile Health project literature guided by design science principles and existing guidelines on taxonomic development. From this development, we present multiple observations on the state of the literature in the field and present two theoretical links for future research.
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Mobile Health
Decision Support Systems, 2014Co-Authors: Upkar VarshneyAbstract:Mobile Health has been receiving a lot of attention from patients, Healthcare professionals, application developers, network service providers and researchers. Mobile Health is more than just some Healthcare applications on a Mobile phone and it can involve sensors and wireless networks in monitoring various conditions, Mobile devices to access numerous Healthcare services, Healthcare professionals to make decisions and provide emergency care, and for the elderly to manage their daily activities in independent living. More specifically, m-Health can result in major advances in (a) expanding Healthcare coverage, (b) improving decision making, (c) managing chronic conditions and (d) providing suitable Healthcare in emergencies. To help realize these advances, there are major research challenges that need to be addressed. We classify these challenges in four categories of (a) patients related, (b) Healthcare professionals related, (c) IT related and (d) applications related challenges. Within each category, we identify several research problems, and we present some high-level and preliminary solutions along with an agenda for future research. The paper may provide a platform for future research and decision-making related to patients, Healthcare professionals, applications, and infrastructure. These decisions will significantly impact how future Mobile Health services will be designed, developed, evaluated, and adopted globally. We present an integrated view of Mobile Health.Mobile Health can lead to many significant improvements in Healthcare.There are many important challenges in Mobile Health.We classify Mobile Health challenges in four categories.For each category, the challenges and possible solutions are discussed.
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A model for improving quality of decisions in Mobile Health
Decision Support Systems, 2014Co-Authors: Upkar VarshneyAbstract:Abstract The rapid and wide-scale introduction of Mobile technologies in Healthcare is resulting in an emerging area of Mobile Health. m-Health has major implications for patients, Healthcare professionals, developers, infrastructure providers and regulators in both developed and developing countries. Mobile technologies can not only support instant and ubiquitous access to information, Healthcare professionals and patients, but can also create many interesting challenges, including additional complexity and potential for various errors. In this paper, we address how Mobile Health can be more effectively supported by Mobile technologies. More specifically, we present two sets of enhancements: (a) context-awareness and processing and (b) improved presentation of information to Healthcare professionals. These enhancements are then applied in the conceptual design of a Mobile Health alert generation and processing system. To evaluate the effectiveness of the proposed enhancements, we develop and utilize an analytical model. Using multiple metrics, including the number of alerts generated and probability of error in alert-response, we show that the proposed enhancements can improve the quality of Mobile Health. We hope that other researchers design, implement and evaluate additional enhancements in Mobile technologies for m-Health. While we do not present any prototypes of the systems, the work presented in the paper can lead to prototypes and testing of systems in the future for Mobile Health.
Jong Hoon Ahnn - One of the best experts on this subject based on the ideXlab platform.
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mHealthMon: Toward Energy-Efficient and Distributed Mobile Health Monitoring Using Parallel Offloading
Journal of Medical Systems, 2013Co-Authors: Jong Hoon Ahnn, Miodrag PotkonjakAbstract:Although Mobile Health monitoring where Mobile sensors continuously gather, process, and update sensor readings (e.g. vital signals) from patient's sensors is emerging, little effort has been investigated in an energy-efficient management of sensor information gathering and processing. Mobile Health monitoring with the focus of energy consumption may instead be holistically analyzed and systematically designed as a global solution to optimization subproblems. This paper presents an attempt to decompose the very complex Mobile Health monitoring system whose layer in the system corresponds to decomposed subproblems, and interfaces between them are quantified as functions of the optimization variables in order to orchestrate the subproblems. We propose a distributed and energy-saving Mobile Health platform, called mHealthMon where Mobile users publish/access sensor data via a cloud computing-based distributed P2P overlay network. The key objective is to satisfy the Mobile Health monitoring application's quality of service requirements by modeling each subsystem: Mobile clients with medical sensors, wireless network medium, and distributed cloud services. By simulations based on experimental data, we present the proposed system can achieve up to 10.1 times more energy-efficient and 20.2 times faster compared to a standalone Mobile Health monitoring application, in various Mobile Health monitoring scenarios applying a realistic mobility model.
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Toward energy-efficient and distributed Mobile Health monitoring using parallel offloading
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013Co-Authors: Jong Hoon Ahnn, Miodrag PotkonjakAbstract:Although Mobile Health monitoring where Mobile sensors continuously gather, process, and update sensor readings (e.g. vital signals) from patient's sensors is emerging, little effort has been investigated in an energy-efficient management of sensor information gathering and processing. Mobile Health monitoring with the focus of energy consumption may instead be holistically analyzed and systematically designed as a global solution to optimization subproblems. We propose a distributed and energy-saving Mobile Health platform, called mHealthMon where Mobile users publish/access sensor data via a cloud computing-based distributed P2P overlay network. The key objective is to satisfy the Mobile Health monitoring application's quality of service requirements by modeling each subsystem: Mobile clients with medical sensors, wireless network medium, and distributed cloud services. By simulations based on experimental data, we present the proposed system can achieve up to 10.1 times more energy-efficient and 20.2 times faster compared to a standalone Mobile Health monitoring application, in various Mobile Health monitoring scenarios applying a realistic mobility model.
Mohammad Nabil Almunawar - One of the best experts on this subject based on the ideXlab platform.
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Pervasive Mobile Health
Encyclopedia of Information Science and Technology Fourth Edition, 2020Co-Authors: Muhammad Anshari, Mohammad Nabil AlmunawarAbstract:Pervasive Mobile Health is Mobile Health that provides Healthcare services accessible regardless of time and place with patients can continuously be connected through their smart Mobile devices. It offers Healthcare providers a more comprehensive perspective of patients' condition and thus aid in achieving complex Healthcare goal(s) such as building lasting relationships with patients. The service can be further extended to accommodate customers' participation in Health and Healthcare processes to improve Healthcare services by extending roles of patients. The advancement of the Web technologies, especially social networks, push eHealth to embrace Mobile devices (mHealth) and personalize customers centric services with a possibility to extend and improve services by enabling active participation of patients, patient's families, and the community at large in Healthcare processes and personal Health decision making. This chapter addresses some important concepts of mHealth, challenges, future trends, and some related terminologies to provide a holistic view of mHealth.
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Pervasive Mobile Health
Advanced Methodologies and Technologies in Medicine and Healthcare, 2020Co-Authors: Muhammad Anshari, Mohammad Nabil AlmunawarAbstract:Pervasive Mobile Health is Mobile Health that provides Healthcare services that are accessible regardless of time and place where patients can continuously be connected through their smart Mobile devices. It offers Healthcare providers a more comprehensive perspective of patients' conditions and thus aids in achieving complex Healthcare goal(s) such as building lasting relationships with patients. The service can be further extended to accommodate customers' participation in Health and Healthcare processes to improve Healthcare services by extending roles of patients. The advancement of the web technologies, especially social networks, push e-Health to embrace Mobile devices (m-Health) and personalize customer-centric services with a possibility to extend and improve services by enabling active participation of patients, patients' families, and the community at large in Healthcare processes and personal Health decision making. This chapter addresses some important concepts of m-Health, challenges, future trends, and some related terminologies to provide a holistic view of m-Health.
