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dc.contributor.authorVenkatesan, Srikanth
dc.date.accessioned2018-05-23T20:21:04Z
dc.date.available2018-05-23T20:21:04Z
dc.date.issued2017
dc.identifier.isbn9780355311044
dc.identifier.other1983460451
dc.identifier.urihttp://hdl.handle.net/10477/77680
dc.description.abstractCan medical advice from other participants in online health social networks impact patient safety? What can we do alleviate this problem? How does the accuracy of information on such networks affect the patients? There has been a significant increase , in recent years, in the use of online health social network sites as more patients seek to access health information and connect with other patients with the same or a similar disease online. Yet, the above questions have not been adequately addressed in the literature. This dissertation focusses on addressing these issues. Patient to patient portals (online health networks) are health social networks that typically bring together people with similar health ailments. They also provide an ideal platform for patients to give experiential evaluations on the effectiveness of care and treatments and offer advice to other patients. Further the portals are accessible 24x7. While health information from patient-to-patient portals empowers patients, it can also lead to a compromise in patient safety. Crowdsourcing measures also present real dangers to patient safety. The objective of this research is to investigate the factors that are associated with misinformation in online patient portals to provide recommendations that impact both policy and practice. More broadly we explore the impact of question related issues, attributes about the patient asking the question and the responder, disease related factors, network factors, and cohesive groups. Another important goal is to provide a mechanism to detect and predict potential misinformation in such patient portals. The analysis can be done at three levels: (a) Thread Level (a collectivity of discussions about a topic): Essay 1 (b) Response Level (a post): Essay 2 and (c) Group Level (Cohesive sub-groups): Essay 3. The next few sections detail each of these essays.
dc.languageEnglish
dc.sourceDissertations & Theses @ SUNY Buffalo,ProQuest Dissertations & Theses Global
dc.subjectCommunication and the arts
dc.subjectSocial sciences
dc.subjectCohesive subgroups
dc.subjectData quality
dc.subjectMisinformation
dc.subjectOnline health social networks
dc.titleData Quality in Online Health Social Networks for Chronic Diseases
dc.typeDissertation/Thesis


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