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Digital health for chronic disease management
Published in Ben Y.F. Fong, Martin C.S. Wong, The Routledge Handbook of Public Health and the Community, 2021
Kelvin K.F. Tsoi, Martin C.S. Wong
The adoption of digital solutions in health care permits all-rounded access to health information and continuous tracking of individuals’ health status, with a constant collection of individual health data. A world of health data, from generic health status information, to blood pressure data and even mobility data, could be well captured and stored using digital technology. Biometric data obtained during exercise has become increasingly important, as they are able to reflect personal fitness and health conditions, including muscular strength, cardiorespiratory endurance, flexibility and body composition. With further analysis, these data could generate precious insights for clinical diagnosis, management and even inform non-clinical decision-making, such as optimal plans for lifestyle modifications. Thereupon, such digital infrastructure contributes to the establishment of ‘Smart Health’, with a health management information system, telemedicine system and decision-making system as a whole to enhance population health.
Technical Overview and Features of the Varian IDENTIFY™ System
Published in Jeremy D. P. Hoisak, Adam B. Paxton, Benjamin Waghorn, Todd Pawlicki, Surface Guided Radiation Therapy, 2020
Raymond Schulz, Chris Huyghe, Lisa Hampton, Delena Hanson, Michael Stead, Thomas Speck
IDENTIFY is designed as a secondary data management system, complementing the user’s OIS while simultaneously managing patient biometric data and IDENTIFY-specific data. In this context, the patient is required to be previously registered in the OIS system in order to prevent duplicate data entry and ensure the integrity of the entered data.
Changing the way we work
Published in Adam Staten, Euan Lawson, GP Wellbeing, 2017
A large scale trial, the Whole Systems Demonstrator programme, found that telehealth, in the form trialled, was not cost effective compared to routine care, but a major weakness of this, and other telehealth studies, is that the biometric data are usually not sent to the GP.10 This is something that clearly warrants some research before the potential impact of telehealth on general practice can be assessed.
Digital health technology used in emergency large-scale vaccination campaigns in low- and middle-income countries: a narrative review for improved pandemic preparedness
Published in Expert Review of Vaccines, 2023
Paula Mc Kenna, Lindsay A. Broadfield, Annik Willems, Serge Masyn, Theresa Pattery, Ruxandra Draghia-Akli
Data privacy and security measures are essential for the ethical use of digital health technology [18]. A large amount of health data and sensitive, personally identifying information is collected with the implementation of digital health tools and must be sufficiently protected [18]. Maintaining high levels of data safety and security is especially true for the use of biometric data for which additional privacy concerns exist [74]. The WHO Global Strategy on Digital Health [18], the Principles for Digital Development [21], the DPG standard from the DPG Alliance [23], and the Digital Global Good (DGG) standard maintained by Digital Square [75] all emphasize the need to be transparent about how data will be collected and used, to plan for and thwart security breaches, and to protect data against harmful or inappropriate use. These guidelines recommend addressing the protection of privacy and data security of digital tools already at the design stage [18,21]. These considerations are critical, in light of data breaches in health data, including electronic medical record systems, and the increasing cost of recovering data and system access [76]. Some of the tools identified in this review have DPG or DGG labels (Table 2), with the possibility that additional tools are undergoing the review process to receive these labels.
VR in the Prison System: Ethical and Legal Concerns
Published in AJOB Neuroscience, 2022
Even if the data is used solely for the seemingly benign use of simply operating the virtual reality device, the collection of such data raises concerns regarding the privacy and security of that data. Under US law, prisoners are presumed to retain some, albeit limited, privacy rights that are balanced with the security concerns of the correctional facility.1 Some of the collected data may be considered particularly sensitive, requiring special protection. To wit, the collection of biometric data may specifically run afoul of new and emerging biometric laws in the United States. Consider the California Privacy Rights Act of 2020 (CPRA) which will come into force in 2023. Under California’s law biometric information can only be collected for a limited number of use cases.2
A systematic perspective on the applications of big data analytics in healthcare management
Published in International Journal of Healthcare Management, 2019
Sachin S. Kamble, Angappa Gunasekaran, Milind Goswami, Jaswant Manda
Big data aggregation refers to the collection of big data from multiple sources and its transformation in required data formats. The healthcare data is voluminous and heterogeneous coming from various internal and external sources [11]. The external sources include social media, blogs, remote sensors, and meters. The internal sources of data consist of the biometric data (x-rays, scans, etc.) and human-generated data (electronic health records, prescriptions, notes, and interviews). The data is available in structured or in an unstructured format and collected through the health system’s unit [41] and needs to transform into usable forms using transformation engines, pooled and stored in the database before performing the big data analytics [1].