Social media in public health care: Impact domain propositions

Andersen, Kim Normann; Medaglia, Rony; Henriksen, Helle Zinner

doi:10.1016/j.giq.2012.07.004

Cited by 84 publications

(56 citation statements)

References 27 publications

(37 reference statements)

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“…It supplies the environment for personalization of goods and services and the creation of information that is responsive to people's preferences [5]. As a result, people are choosing to interact with each other [15] to get information because it is immediate and authentic, rather than enduring a long lag time [16] to receive sanitized information from companies, governments and other public and private organizations [11]. Contrary to what many executive leaders may have initially thought, the Internet and social media is unstoppable [11], [17]- [19].…”

Section: Technology Evolutionmentioning

confidence: 99%

“…This explosion of content stretches human cognitive powers to their limits and makes it difficult for anyone to stay current [11]. Therefore, in the face of this extraordinary growth it is not surprising that patients are adopting social computing to manage information overload and augment knowledge so they can play a more empowered and active role in managing their health [9], [13], [15], [20], [22]- [25]. Uptake of social computing for health is reflected in the numbers.…”

Section: Technology Evolutionmentioning

confidence: 99%

“…This online engagement has "…a positive effect on [their] perceived quality of life and well-being" [20, p. 146]. Twenty percent of those U.S. users also create health-related content [15], [20], [23], [25]- [27]. Finally, IBM further projects that by 2016 one billion health-related apps will be downloaded per year [21].…”

Section: Technology Evolutionmentioning

confidence: 99%

“…Individual healthcare professionals are slow to adapt, either due to a genuine lack of knowledge, or a fear of potential consequences, such as lost productivity and legal problems [15]. Regardless, healthcare organizations cannot ignore the inevitable arrival of social media and the accompanying increase in patient expectations.…”

Section: Technology Evolutionmentioning

confidence: 99%

See 3 more Smart Citations

Social Media in Healthcare

Gamache-OLeary¹,

Grant²

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Section: Technology Evolutionmentioning

confidence: 99%

Section: Technology Evolutionmentioning

confidence: 99%

Section: Technology Evolutionmentioning

confidence: 99%

Section: Technology Evolutionmentioning

confidence: 99%

See 2 more Smart Citations

Social Media in Healthcare

Gamache-OLeary¹,

Grant²

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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“…On the side of practitioners, reservation results from the uncertainties on ethical and legal consequences that social media interactions with patients can have (cf. Andersen et al, 2012). On the side of patients, reservation is mainly due to protection of privacy and the control over personal data.…”

Section: Healthcare Service Deliverymentioning

confidence: 99%

Social Media Use by Governments

2014

OECD Working Papers on Public Governance

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This working paper takes a comparative snapshot of social media use in and by OECD governments. The focus is on government institutions, as opposed to personalities, and how they manage to capture the opportunities of new social media platforms to deliver better public services and to create more open policy processes. The analysis is based on a large amount of empirical data, including a survey of OECD governments on policies and objectives in this area. Major challenges are discussed, notably those related to the uncertainty of institutions on how to best leverage social media beyond "corporate" communications. The paper proposes tools to guide decision makers: a checklist of issues to be considered by government institutions, a set of potential indicators to appraise impacts, and a range of options for more in-depth policy analysis. 2 EXECUTIVE SUMMARYPresence and activity on social media is no longer a question of choice for most governments as those new platforms empower individuals and non-traditional interest groups. Politicians were first to react to these changes by using platforms such as Twitter, Facebook and blogs to rally support. Government institutions are slowly catching up and increasingly experiment with social media. Governments are witnessing how social media empower individuals, as well as traditional and newly-formed interest groups to influence political agendas and policy processes. Most prominent topics discussed on social media include elections and political campaigns, disaster and emergency situations, political unrest. But social media are more than just a global place for discussions, they have become a vehicle for the organisation of collective action.

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Epidemiological challenges in pandemic coronavirus disease (COVID‐19): Role of artificial intelligence

Dasgupta

Bakshi

Mukherjee

et al. 2022

WIREs Data Min & Knowl

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World is now experiencing a major health calamity due to the coronavirus disease (COVID‐19) pandemic, caused by the severe acute respiratory syndrome coronavirus clade 2. The foremost challenge facing the scientific community is to explore the growth and transmission capability of the virus. Use of artificial intelligence (AI), such as deep learning, in (i) rapid disease detection from x‐ray or computed tomography (CT) or high‐resolution CT (HRCT) images, (ii) accurate prediction of the epidemic patterns and their saturation throughout the globe, (iii) forecasting the disease and psychological impact on the population from social networking data, and (iv) prediction of drug–protein interactions for repurposing the drugs, has attracted much attention. In the present study, we describe the role of various AI‐based technologies for rapid and efficient detection from CT images complementing quantitative real‐time polymerase chain reaction and immunodiagnostic assays. AI‐based technologies to anticipate the current pandemic pattern, prevent the spread of disease, and face mask detection are also discussed. We inspect how the virus transmits depending on different factors. We investigate the deep learning technique to assess the affinity of the most probable drugs to treat COVID‐19. This article is categorized under: Application Areas > Health Care Algorithmic Development > Biological Data Mining Technologies > Machine Learning

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Social media in public health care: Impact domain propositions

Cited by 84 publications

References 27 publications

Social Media in Healthcare

Social Media in Healthcare

Social Media Use by Governments

Epidemiological challenges in pandemic coronavirus disease (COVID‐19): Role of artificial intelligence

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