AMD. Studies on pathogenesis, treatment and prevention of age-related macular degeneration

Contains fulltext : 120582.pdf (Publisher’s version ) (Open Access)Radboud Universiteit Nijmegen, 20 december 2013Promotores : Hoyng, C.B., Daha, M.R. Co-promotores : Hollander, A.I. den, Klevering, B.J

Transformative effects of social media:How patients’ use of social media affects roles and relationships in healthcare

Social media have started changing the way that many industries work. However, there is a lack of understanding of these changes, particularly in the context of healthcare, which is known for high information asymmetry between healthcare providers and patients, and authoritative relationships. Yet, the high proliferation of social media in healthcare enables patients to easily communicate with one another, exchanging informational and emotional support. However, it remains unclear how social media is used by patients, how it affects their behavior, their identities and their relationships with healthcare providers. We conduct a literature review and four empirical studies. First, we conduct a systematic literature review on patients’ use of social media and effects of such use. Second, we explore patients’ use of various categories of social media and propose a taxonomy of interactions enabled by social media in healthcare. Third, we explore how the use of social media by two chronic disease patient communities changes their behaviors and identities as well as their relationships with their healthcare providers. Fourth, we explore how healthcare providers’ interactions with patients who use social media change those providers’ occupational identity. Finally, we test to what extent, and through which mechanism, patients’ use of social media changes their relationships with healthcare providers. Taken together, these findings provide a new explanation of the developing role of social media in changing - and strengthening - organization-customer relationships

Transformative effects of social media:How patients’ use of social media affects roles and relationships in healthcare

Social media have started changing the way that many industries work. However, there is a lack of understanding of these changes, particularly in the context of healthcare, which is known for high information asymmetry between healthcare providers and patients, and authoritative relationships. Yet, the high proliferation of social media in healthcare enables patients to easily communicate with one another, exchanging informational and emotional support. However, it remains unclear how social media is used by patients, how it affects their behavior, their identities and their relationships with healthcare providers. We conduct a literature review and four empirical studies. First, we conduct a systematic literature review on patients’ use of social media and effects of such use. Second, we explore patients’ use of various categories of social media and propose a taxonomy of interactions enabled by social media in healthcare. Third, we explore how the use of social media by two chronic disease patient communities changes their behaviors and identities as well as their relationships with their healthcare providers. Fourth, we explore how healthcare providers’ interactions with patients who use social media change those providers’ occupational identity. Finally, we test to what extent, and through which mechanism, patients’ use of social media changes their relationships with healthcare providers. Taken together, these findings provide a new explanation of the developing role of social media in changing - and strengthening - organization-customer relationships

Self-determination theory as an explaining mechanism for the effects of patient’s social media use

Social media enable patients to communicate with a large number of their peers, share experiences, and provide each other with emotional and informational support. Hence, patients can extend their traditional sources of information and build knowledge online with other patients. While limited research presents us with anecdotal evidence or make propositions of the social media effects, current research has not provided a theoretical explanation or empirically tested how social media changes doctor-patient interactions. We draw on self-determination theory to hypothesize the effects of social media use on doctor-patient interactions, namely self-management, empowerment and shared decision making. We propose the explanatory mechanism of self-determination theory through its key concepts of competence, autonomy and relatedness. We employ longitudinal survey in a newly established social media platform for diabetes patients to test our hypotheses

Social media enabled interactions in healthcare: Towards a taxonomy

Healthcare users and providers increasingly utilize social media to interact with one another. For a future understanding of when and how these interactions supplement or replace offline doctor-patient interactions, it is essential to understand who interacts, about what, and how these interactions can be categorized in a taxonomy. We draw on affordance theory and employ a mixed-methods approach to study social media interactions among healthcare users and providers. We first engage in qualitative content analysis, which is followed by cluster analysis. We identify five archetypal interactions and categorize these in a taxonomy that adds to current literature on how social media is utilized in the healthcare context. We also provide a clear and systematic overview of the interactions in different social media categories that can stimulate future research regarding doctor-patient interactions. Furthermore, we identify a new and distinct type of social media enabled interaction in healthcare, namely lifestyle support, focusing on prevention

Social Media Enabled Interactions in Healthcare: Towards a Typology

Social media is increasingly used by healthcare users and providers to connect and communicate with each other. Such use is changing the interactions in healthcare and it is not clear what effects this may have for healthcare provision. Although it could be beneficial to both parties, it could also bring threats for healthcare providers and disrupt the healthcare system. Therefore, it is important to understand who interacts, about what and how these interactions can be categorized into a typology. In this way, we can attain a better grasp of the potential benefits and threats social media could have for healthcare providers and healthcare in general. We employ qualitative content analysis to six contrasting categories of social media and study interactions between healthcare users and with healthcare providers. We identify nine topics, propose six archetypical interactions on social media in the healthcare domain and propose how these archetypical interactions can be categorized in a typology. In this way, we answer a call for research within the information systems (IS) field in healthcare on who is using social media and in what ways. Thus, we provide a foundation for future research on the effects of social media in healthcare

Adapting the Standard SIR Disease Model in Order to Track and Predict the Spreading of the EBOLA Virus Using Twitter Data

A method has been developed to track infectious diseases by using data mining of active Twitter accounts and its efficacy was demonstrated during the West African Ebola outbreak of 2014. Using a meme based n-gram semantic usage model to search the Twitter database for indications of illness, flight and death from the spread of Ebola in Africa, principally from Guinea, Sierra Leone and Liberia. Memes of interest relate disease to location and severity and are coupled to the density of Tweets and re-Tweets. The meme spreads through the community of social users in a fashion similar to nonlinear wave propagation- like a shock wave, visualized as a spike in Tweet activity. The spreading was modeled as a system isomorphic to a modified SIR (Susceptible, Infected, Removed disease model) system of three coupled nonlinear differential equations using Twitter variables. The nonlinear terms in this model lead to feedback mechanisms that result in unusual behavior that does not always reduce the spread of the disease. The resulting geographic Tweet densities are coupled to geographic maps of the region. These maps have specific threat levels that are ported to a mobile application (app) and can be used by travelers to assess the relative safety of the region they will be in

GWAS study using DNA pooling strategy identifies association of variant rs4910623 in OR52B4 gene with anti-VEGF treatment response in age-related macular degeneration

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/Pooled DNA based GWAS to determine genetic association of SNPs with visual acuity (VA) outcome in anti-vascular endothelial growth factor (anti-VEGF) treated neovascular age-related macular degeneration (nAMD) patients. We performed pooled DNA based GWAS on 285 anti-VEGF treated nAMD patients using high density Illumina 4.3 M array. Primary outcome was change in VA in Early Treatment Diabetic Retinopathy Study (ETDRS) letters after 6 months of anti-VEGF treatment (patients who lost ≥5 ETDRS letters classified as non-responders and all remaining classified as responders). GWAS analysis identified 44 SNPs of interest: 37 with strong evidence of association (p < 9 × 10−8), 2 in drug resistance genes (p < 5 × 10−6) and 5 nonsynonymous changes (p < 1 × 10−4). In the validation phase, individual genotyping of 44 variants showed three SNPs (rs4910623 p = 5.6 × 10−5, rs323085 p = 6.5 × 10−4 and rs10198937 p = 1.30 × 10−3) remained associated with VA response at 6 months. SNP rs4910623 also associated with treatment response at 3 months (p = 1.5 × 10−3). Replication of these three SNPs in 376 patients revealed association of rs4910623 with poor VA response after 3 and 6 months of treatment (p = 2.4 × 10−3 and p = 3.5 × 10−2, respectively). Meta-analysis of both cohorts (673 samples) confirmed association of rs4910623 with poor VA response after 3 months (p = 1.2 × 10−5) and 6 months (p = 9.3 × 10−6) of treatment in nAMD patients