Social networks for eHealth solutions on cloud

Deb, Briti; Srirama, Satish Narayana

doi:10.3389/fgene.2013.00171

Cited by 11 publications

(3 citation statements)

References 6 publications

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“…While some HSNs make efforts to adhere to the HIPPA (Health Insurance Portability and Accountability Act, 1996) they are not legally obliged to, as users are voluntarily providing the HSN with their PHI (Li, 2013). When data is stored in the cloud, a user's country or state health and/or consumer laws also offers no protection given the difficulty in establishing legal jurisdiction where "data possession", "custody" and "ownership" can be challenged (Deb & Srirama, 2013). The findings reveal that without engaging in levels 1 and 2 -perception and comprehension of situation awareness -that participants' satisfaction with the decision at level 3 would be negatively impacted (see Figure 3).…”

Section: Participant Id Participant Commentmentioning

confidence: 99%

Comprehension, Perception, and Projection

Rowan

O’Connor

Lynch

et al. 2021

Journal of Organizational and End User Computing

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Health social networks (HSNs) allow individuals with health information needs to connect and discuss health-related issues online. Political-technology intertwinement (e.g. GDPR and Digital Technology) highlights that users need to be aware, understand, and willing to provide electronic consent (eConsent) when sharing personal information online. The objective of this study is to explore the ‘As-Is’ factors which impact individuals’ decisional autonomy when consenting to the privacy policy (PP) and Terms and Conditions (T&Cs) on a HSN. We use a Situational Awareness (SA) lens to examine decision autonomy when providing eConsent. A mixed-methods approach reveals that technical and privacy comprehension, user perceptions, and projection of future consequences impact participants’ decision autonomy in providing eConsent. Without dealing with the privacy paradox at the outset, decision awareness and latterly decision satisfaction is negatively impacted. Movement away from clickwrap online consent to customised two-way engagement is the way forward for the design of eConsent.

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Section: Participant Id Participant Commentmentioning

confidence: 99%

Comprehension, Perception, and Projection

Rowan

O’Connor

Lynch

et al. 2021

Journal of Organizational and End User Computing

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show abstract

“…Many researchers have seen the need for such a role and have offered possible solutions. Deb and Srirama 63 proposed a three-tier ecosystem to improve the shortcomings of cloud-enabled social networks for eHealth Solutions. Bahga and Madisetti 64 developed a cloud-based approach for interoperable EHRs.…”

Section: Big Data Applicationsmentioning

confidence: 99%

Big Data Application in Biomedical Research and Health Care: A Literature Review

Luo

Gopukumar

et al. 2016

Biomed Inform Insights

415

214

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Big data technologies are increasingly used for biomedical and health-care informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. The cost of acquiring and analyzing biomedical data is expected to decrease dramatically with the help of technology upgrades, such as the emergence of new sequencing machines, the development of novel hardware and software for parallel computing, and the extensive expansion of EHRs. Big data applications present new opportunities to discover new knowledge and create novel methods to improve the quality of health care. The application of big data in health care is a fast-growing field, with many new discoveries and methodologies published in the last five years. In this paper, we review and discuss big data application in four major biomedical subdisciplines: (1) bioinformatics, (2) clinical informatics, (3) imaging informatics, and (4) public health informatics. Specifically, in bioinformatics, high-throughput experiments facilitate the research of new genome-wide association studies of diseases, and with clinical informatics, the clinical field benefits from the vast amount of collected patient data for making intelligent decisions. Imaging informatics is now more rapidly integrated with cloud platforms to share medical image data and workflows, and public health informatics leverages big data techniques for predicting and monitoring infectious disease outbreaks, such as Ebola. In this paper, we review the recent progress and breakthroughs of big data applications in these health-care domains and summarize the challenges, gaps, and opportunities to improve and advance big data applications in health care.

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“…Dönitz and Wingender ( 2012 ) describe a web-based service that can be accessed from any application to make use of standard ontologies, removing a significant burden to application development. At a higher level, Deb and Srirama ( 2013 ) provide us with a view of how the data and ontologies currently being produced might be linked and accessed via cloud infrastructures and describe some of the problems this raises in the domain of human eHealth.…”

mentioning

confidence: 99%

Editorial: biological ontologies and semantic biology

Hancock

2014

Front. Genet.

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Social networks for eHealth solutions on cloud

Cited by 11 publications

References 6 publications

Comprehension, Perception, and Projection

Comprehension, Perception, and Projection

Big Data Application in Biomedical Research and Health Care: A Literature Review

Editorial: biological ontologies and semantic biology

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