Medicine 2.0 Conference 

About: Medicine 2.0 Conference is an academic conference. The conference publishes majorly in the area(s): Health care & Population. Over the lifetime, 278 publications have been published by the conference receiving 4603 citations.

Pandemics in the Age of Twitter: Content Analysis of “Tweets” During the H1N1 Outbreak

Abstract: Background: Twitter is an instant micro-blogging service that allows users to post, read, and exchange information and thoughts easily with masses across the globe. In response to the 2009 Influenza A virus subtype H1N1 outbreak (aka "swine flu"), users produced thousands of posts on the subject. Media outlets have claimed that Twitter and other forms of social media have led to the viral distribution of mass misinformation and may be a threat to public health and government initiatives. However, quantifiable evidence of these claims has not been substantiated. Objective: This exploratory project aims to analyze the content of Twitter posts or “tweets” shared during the H1N1 outbreak to determine the types and quality of information that social media users are exchanging in pandemics. Methods: Using the Infovigil system, an emerging infoveillance system, we are continuously identifying and archiving health-related tweets. Between April 28 and May 11, 2009, we archived over 300,000 tweets containing the keywords or hashtags “swine flu”, “swineflu”, or “H1N1”. A random selection of tweets from each hour of each day were coded for content by two raters. A multi-axial coding scheme was created using an iterative process to reflect the range of data. Data analysis consisted of descriptive statistics and univariate analysis of content between days. Non-English posts and reposts (“retweets”) were excluded from the analysis. Results: Preliminary analysis of 400 tweets indicates that news posts were the most common type of information shared (46%) followed by public health education (19.18%) and H1N1-related humour (18.25%). 36.75% of all posts quoted news articles verbatim and provided URLs to the source. Only 7 cases could be identified as possible sources of misinformation. Conclusions: Contrary to anecdotal evidence, misinformation is not rampantly spread via Twitter. Instead, the service is being utilized to distribute news and information from credible sources and almost one of five tweets are of humorous nature. Contrary to some media reports of Twitter fueling an epidemic of misinformation, Twitter can and is already used to quickly disseminate pandemic information to the public. Further analysis of tweets collected during an epidemic will allow us to refine the Infovigil system for twitter-based syndromic surveillance []

The potential of social computing for biomedical research on rare diseases

Abstract: Subject: Results of our ongoing Study on the Potential of Social Computing for Biomedical Research in Rare Diseases. Rationale of our study: Rare Diseases (RDs) affect 6-8 % of the European population, approximately 25 million citizens. There exist between 5000 and 8000 RDs of which 80 % have a genetic origin. Because of their rarity, these diseases are hardly observed in basic diagnostic procedures and pathways by clinicians, resulting in under-diagnosing and/or on longer waiting periods needed to get the correct. Research on rare diseases (RDs) has traditionally been hindered by the fact that cases, clinicians, researchers and resources (pharmaceutical companies have lacked return on investment) are scattered. One of the key characteristics of social computing is its ability to enable powerfully user-created content. Social Computing, also known as the Web 2.0 has the potential to connect up all the actors and stakeholders, especially patients and biomedical researchers. These activities and networks are already allowing a critical mass of knowledge to be gathered, from both patients and researchers - albeit in an unstructured manner. Objectives: To assess the capability of social networks to enable sustainable research on rare diseases, by allowing patients to be knowledge generators, in synergy with biomedical researchers, and to propose a structured construct to dramatically increase this synergy. Specific objectives of our research include: a) the assessment of opportunities and challenges of social networks for research, from the point of view of patients, formal and informal carers, clinicians, researchers, industry and society; b) a proposal for a construct to structuring and making efficient this potential; c) the derivation of policy options at EU level as to develop this construct; d) the analysis of relevant implications for privacy and security of social computing related activities in this realm. Applied method: We have applied a 3-step methodology: 1) Browsing current experiences (there are scarce but there are some examples, e.g. Autism: IAN Project http://www.ianproject.org/ and OAR http://www.researchautism.org/); while specifically checking the relevant social computing-based applications, if any. 2) Holding an expert workshop to ascertain the opportunities and challenges; and 3) proposing a structure for this construct, partly based on Science 2.0 theories and also on some practical cases. Preliminary conclusions: Though RDs affect only 6-8% of European citizens, Information Society Technologies platforms using social computing approaches have a considerable potential for research on RDs, not only as regards its sustainability but also its profitability for both the pharmaceutical industry and the society at large. The body of knowledge on RDs has developed very slowly and is still largely an "uncharted territory". Based on the "Long Tail" theory [1], research on specific rare diseases through the application of social computing is worth - socially, clinically and economically. Cases examined preliminarily confirm our hypothesis. An international expert consultation will serve to systematise and validate our insights so far. References: 1. Anderson, Chris. The Long Tail - Why the Future of Business is Selling Less of More. Hyperion, New York. 2006. 2. RDCRN - Rare Diseases Clinical Research Network. http://rarediseasesnetwork.epi.usf.edu/ 3. Sauer I, Bialeck D, Efimova E, Schwartlander R, Pless G & Neuhaus P. "Blogs" and "Wikis" are valuable software tools for communication within research groups. Artificial Organs 29(1):82-89, Blackwell Publishing, Inc. 2005. 4. Singer, E. Social Networking Hits the Genome. http://www.technologyreview.com/Biotech /20464/ 5. Waldrop, M. Science 2.0 - Is Open Access Science the Future? Scientific American Magazine - April 21, 2008. (Also available at http://www.sciam.com/article.cfm?id=science-2-point-0). The potential of social computing for biomedical research on rare diseases [5 Cr3 1330 Cabrera] View SlideShare presentation or Upload your own. (tags: medicine20 20 ) []

Social uses of personal health information within PatientsLikeMe TM , an online patient community: What can happen when patients have access to one another's data

Abstract: Background: This project investigates the ways in which patients respond to the shared use of what is often considered private information: personal health data There is a growing demand for patient access to personal health records The predominant model for this record is a repository of all clinically relevant health information kept securely and viewed privately by patients and their healthcare providers While this type of record does seem to have beneficial effects for the patient-physician relationship, the complexity and novelty of these data coupled with the lack of research in this area means the utility of personal health information for the primary stakeholders -- the patients-is not well documented or understood Objective: PatientsLikeMe® is an online community built to support information exchange between patients The site provides customized disease-specific outcome and visualization tools to help patients understand and share information about their condition We begin this paper by describing the components and design of the online community We then identify and analyze how users of this platform reference personal health information within patient-to-patient dialogues Methods: Patients diagnosed with amyotrophic lateral sclerosis (ALS) post data on their current treatments, symptoms, and outcomes These data are displayed graphically within personal health profiles and are reflected in composite community-level symptom and treatment reports Users review and discuss these data within the Forum, private messaging, and comments posted on each others' profiles We analyzed member communications that referenced individual-level personal health data to determine how patient peers use personal health information within patient-to-patient exchanges Results: Qualitative analysis of a sample of 123 comments (about 2% of the total) posted within the community revealed a variety of commenting and questioning behaviors by patient members Members referenced data to locate others with particular experiences to answer specific health-related questions, proffer personally acquired disease-management knowledge to those who are most likely to benefit from it, and foster and solidify relationships based on shared concerns Conclusions: Few studies examine the use of personal health information by patients themselves This project suggests how patients who choose to explicitly share health data within a community may benefit from the process, helping patients engage in dialogues that may inform disease self-management We recommend that future designs make each patient's health information as clear as possible, automate matching of people with similar conditions and using similar treatments, and integrate data into online platforms for health conversations Keywords: personal health records; data visualization; personal monitoring; technology; healthcare; self-help devices; personal tracking; social support; online support group; online health community; Social Uses of Personal Health Information Within PatientsLikeMe (4 Aud 1000 Frost Massagli) View SlideShare presentation or Upload your own (tags: medicine20 phr ) []

A Changing Landscape of Physician Quality Reporting: Analysis of Patients’ Online Ratings of Their Physicians

Abstract: Background: Americans increasingly post and consult online physician rankings, yet little is known about the prevalence of ratings, and the information value of the ratings. Objective: To describe trends in patients’ online ratings over time, across specialties, to identify what physician characteristics influence online ratings, and to examine how the value of ratings reflects physician quality. Methods: We used data from RateMDs.com, which included over 386,000 national ratings from 2005 to 2010 and provided insight into the evolution of patients’ online ratings. We obtained physician demographic data from the US Department of Health and Human Services’ Area Resource File. We also matched patients’ ratings with physician-level data from (1) the Virginia Medical Board; and (2) patient survey in three major cities, to examined the probability of being rated and resultant rating levels. Results: We estimate that 1 in 6 practicing US physicians received an online review by January 2010. Obstetrician/gynecologists were twice as likely to be rated (P < .001) as other physicians. Online reviews were generally quite positive (mean 3.93 on a scale of 1 to 5). Based on the Virginia physician population, long-time graduates were more likely to be rated, while physicians who graduated in recent years received higher average ratings (P < .001). Patients gave slightly higher ratings to board-certified physicians (P = .04), those who graduated from highly rated medical schools (P = .002), and those without malpractice claims (P = .1). Conclusions: Online physician rating is rapidly growing in popularity and becoming commonplace with no evidence that they are dominated by disgruntled patients. There exist statistically significant correlations between the value of ratings and physician experience, board certification, education, and malpractice claims, suggesting positive correlation between online ratings and physician quality. Additionally, the online ratings are positively correlated with patient surveys. However, the magnitude is small. Understanding whether they truly reflect better care and how they are used by patients will be critically important. []

Consensus statement on defining and measuring negative effects of Internet interventions

Abstract: Internet interventions have great potential for alleviating emotional distress, promoting mental health, and enhancing well-being. Numerous clinical trials have demonstrated their efficacy for a number of psychiatric conditions, and interventions delivered via the Internet will likely become a common alternative to face-to-face treatment. Meanwhile, research has paid little attention to the negative effects associated with treatment, warranting further investigation of the possibility that some patients might deteriorate or encounter adverse events despite receiving best available care. Evidence from research of face-to-face treatment suggests that negative effects afflict 5–10% of all patients undergoing treatment in terms of deterioration. However, there is currently a lack of consensus on how to define and measure negative effects in psychotherapy research in general, leaving researchers without practical guidelines for monitoring and reporting negative effects in clinical trials. The current paper therefore seeks to provide recommendations that could promote the study of negative effects in Internet interventions with the aim of increasing the knowledge of its occurrence and characteristics. Ten leading experts in the field of Internet interventions were invited to participate and share their perspective on how to explore negative effects, using the Delphi technique to facilitate a dialog and reach an agreement. The authors discuss the importance of conducting research on negative effects in order to further the understanding of its incidence and different features. Suggestions on how to classify and measure negative effects in Internet interventions are proposed, involving methods from both quantitative and qualitative research. Potential mechanisms underlying negative effects are also discussed, differentiating common factors shared with face-to-face treatments from those unique to treatments delivered via the Internet. The authors conclude that negative effects are to be expected and need to be acknowledged to a greater extent, advising researchers to systematically probe for negative effects whenever conducting clinical trials involving Internet interventions, as well as to share their findings in scientific journals.