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Stanford Medicine X | Symplur Signals Research Challenge Winner Announcement

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Stanford Medicine X | Symplur Signals Research Challenge Winner Announcement

In February of this year we invited patients, physicians, researchers, technologists, informaticians, and all health care stakeholders to submit proposals to our Stanford Medicine X | Symplur Signals Research Challenge: a joint initiative designed to spark scholarly research activity in health care social media.

We challenged people to submit proposals addressing one of the questions below:

  • How is social media transforming health care in 2015?
  • How is social media being used to innovate medical education?
  • How did social media contribute to the Medicine X conference in 2014 (#MedX)?

We received an unprecedented amount of submissions, and ten finalists were selected to submit research abstracts. We consulted thoughtful, independent peer reviewers and careful deliberations from our award jury. Our judges for the 2015 Research Challenge were:

  • Kevin A. Clauson, Pharm.D, College of Pharmacy at Lipscomb University
  • Chris Paton BMBS, University of Oxford
  • Francisco J. Grajales PhD, The University of British Columbia

With judgement criteria based on methodological rigor, innovativeness, and alignment with the contest rules and core themes, we are thrilled to announce the grand prize winning research abstract, “Defining digital communities of practice using a Netnographic framework for hashtag analytics.”

The winning team—Damian Roland PhD of the University of Leicester, Daniel Cabrera MD of Mayo Clinic College of Medicine, and Jesse Spur BN of the Royal Brisbane and Women’s Hospital—worked together to substantiate an online healthcare community through systematic deduction techniques. Using Symplur Signals analytics tool, they validated the Free Open Access Medical education (FOAM) community as a digital community of practice (dCOP). You can read about their findings below.

On Sunday September 27th, 2015, Dr. Roland will present their research to conference attendees on the Medicine X main stage, broadcasted to our global audience. On behalf of Medicine X and Symplur, we congratulate them on their dedication to the intersection of health care and social media, and we proudly award their research the grand prize  for the Stanford Medicine X | Symplur Signals Research Challenge!



We would also like to recognize our runner-up abstract, “Social media versus citation metrics as measures of dissemination within medical education.” From the University of Ottawa Faculty of Medicine, Christopher J. Ramnanan Ph.D., Timothy J. Wood Ph.D., and John J. Leddy, Ph.D. worked as a team to determine that social media metrics cannot be compared with citation metrics in measuring academic impact. Their findings are attached below.



Finally, we would like to recognize all of our finalists for the Stanford Medicine X | Symplur Signals Research Challenge. Their research pursuits were deeply rooted in a passion for improving the landscapes of medicine and technology; the authors and their abstract titles are below to acknowledge each team’s participation and commitment to interdisciplinary research.


Title: Efficacy of Social Media Initiatives for Suicide Prevention and At-Risk Account Identification
First Author: Mandi Bishop
Corresponding Author: Lauren Still
Co-author: Nick Kypreos, PhD


Title: Twitter as Healthcare Advisor
First Author: Christophe Giraud-Carrier, PhD, Brigham Young University
Corresponding Author: Christophe Giraud-Carrier, PhD, Brigham Young University
Co-authors:Kyle Prier, MHS, Brigham Young University


Title: An Antivax Outbreak: The Twitter Dynamics of the 2015 Disneyland Measles Outbreak
First Author: Saul Hymes, MD, Stony Brook Children’s Hospital
Corresponding Author: Saul Hymes, MD, Stony Brook Children’s Hospital
Co-author: Christina Gagliardo, MD, Maimonides Infants and Children’s Hospital of Brooklyn


Title: Content Analysis of Tweets of Pregnant Women with Diabetes
First Author/Corresponding Author: Iris Thiele Isip Tan MD, MSc, University of the Philippines Medical Informatics Unit
Co-authors: Helen V. Madamba MD, MPH-TM, Cebu Doctors University University College of Medicine
Rene James P. Balandra Jr. BS Computer Science, University of the Philippines National Telehealth Center


Title: Understanding Patient Behavior at Medical Conferences: Motives, Message, and Meaning
First Author: Loran Cook, B.S. Urban Policy Studies, Georgia State University, Billian’s HealthDATA
Corresponding Author: Jessica Clifton, B.S. Psychology, University of Georgia, Billian’s HealthDATA
Co-authors: Jennifer Dennard, B.S. Journalism, University of Georgia, HISTalk


Title: The Canary in the Coal Mine Tweets: Twitter Insights on Opioid Misuse
First Author: Urmimala Sarkar, MD, MPH, San Francisco General Hospital
Corresponding Author:
Urmimala Sarkar, MD, MPH, San Francisco General Hospital
Gem M. Le, PhD, MHS, Epidemiologist, San Francisco General Hospital
Brian Chan, MD, MPH, Clinical Fellow, San Francisco General Hospital
Byron C. Wallace, PhD, Assistant Professor, University of Texas at Austin, Austin


Title: Healthcare hashtag index development: identifying global impact in social media
First Author: Luís Pinho-Costa, MD, Fânzeres Family Health Unit, Gondomar (Portugal)
Corresponding Author: Luís Pinho-Costa, MD, Fânzeres Family Health Unit, Gondomar (Portugal)
Co-authors: Kenneth Yakubu, FWACP FMCFM MB.BS, Department of Family Medicine, University of Jos (Nigeria)
Kyle Hoedebecke, MD CKTP RMT, Department of Family Medicine, Robinson Health Clinic (USA)
Liliana Laranjo, MD MPH, Portuguese School of Public Health (Portugal)
Christofer Patrick Reichel, MD, Austrian Association for General Practitioners and Family Medicine, Vienna (Austria)
Maria del C Colon-Gonzalez MD, Department of Family & Community Medicine, University of Texas Health Science Center San Antonio (USA)
Ana Luísa Neves, MD MSc, Faculty of Medicine, Imperial College London (UK) / Department of Social Sciences and Health, Unit of Family Medicine, University of Porto (Portugal)
Hassna Errami, MD, Department of Family Medicine, University of Toulouse, Paul Sabatier (France)


Title: Exploring public health and healthcare stakeholder engagement using network and content analyses
First Author: Kristina M. Rabarison, DrPH, MS – Centers for Disease Control and Prevention, Division of Population Health
Corresponding Author: Kristina M. Rabarison, DrPH, MS – Centers for Disease Control and Prevention, Division of Population Health
Co-authors: Merriah A. Croston, MPH – Centers for Disease Control and Prevention, Division of Population Health



Grand Prize Winning Abstract

Defining digital communities of practice using a Netnographic framework for hashtag analytics”



Networks are vital in improving patient care through fostering collaboration, stimulating engagement and promoting learning(1). The advent of social media as an educational tool is based on the assumption that activity in online networks can lead to the emergence of digital communities of practice (dCoP). A CoP was defined by Wenger(2) as having a community, a domain (knowledge) and a practice (application of the knowledge), later Aveling et al.(3) refined and applied the concept to healthcare.

No medical education dCoP has been proven to exist, however an international movement directed to create, collaborate and curate medical knowledge has arisen from the critical care and emergency medicine communities in social media. This group is self-denominated FOAM (Free Open Access Medical education)(4–6).

We aim to prove that the FOAM community, via their Twitter #FOAMed hashtag, constitutes a dCoP. We hypothesize it should be possible to identify the community, the domain, and the practice of the network. The proof will be tested by using a Netnographic(7) approach to the analysis of the relevant Twitter hashtag provided by the Symplur Signals analytic tool(8,9).


The database for #FOAMed was interrogated from March 1 2013 to February 28th 2015. In order to reduce potential bias in analysis, a protocol was submitted to the Symplur Signals team on the 18th May 2015 clearly defining an a priori analytical strategy. The aim to define data consistent with a refined definition of a healthcare dCoP and identify #FOAMed metrics that support this conceptualization.


During the period of activity there were one thousand million Twitter© impressions from over more than 290000 individual tweets (Table 1).  Table 2 summarises the findings from the #FOAMed metrics for each domain of a health dCoP (Table 2).


The analytics evidence a community with a large numbers of users and a remarkable level of engagement and persistence in participation. The network centrality analysis (Figure 1) and conversation identifiers (Figure 2) as well as the degree distribution of the nodes illustrate a rich and diverse network. The diversity accurately represents multidisciplinary healthcare teams including physicians, non-physicians healthcare providers, medical students, primary researchers, and organizations with members distributed around the world. The proof of the emergence of a dCoP oriented to creation, curation and dissemination of knowledge is a paradigm change in medical education. The concept of education based on principals of communities of practice have been described(10) but the existence of a formal dCoP in medical education has never been shown before, although its theoretical benefits have been proposed and anxiously anticipated(11,12)


Using a Netnography framed methodology, we examined the Free Open Access Meducation (FOAM) movement, as defined by the Twitter #FOAMed hashtag, and evaluated its performance as a dCoP using previously validated criteria. It demonstrated concordance with all aspects of a community of practice. This supports the proposition that this innovative use of social media is a powerful educational process likely to expedite knowledge translation for its participants and impact clinical practice with resulting patient benefit.

Supporting Section


  1.  Greenhalgh T, Wieringa S. Is it time to drop the “knowledge translation” metaphor? A critical literature review. J R Soc Med. 2011 Dec 1;104(12):501–9.
  2.  Wenger E. Communities of Practice: Learning, Meaning, and Identity [Internet]. Cambridge University Press; 1999. Available from:
  3.  Aveling E-L, Martin GP, Armstrong N, Banerjee J, Dixon-Woods M. Quality Improvement through Clinical Communities: Eight Lessons for Practice. J Health Organ Manag [Internet]. 2012 [cited 2015 Apr 24];26(2).
  4.  Cadogan M, Thoma B, Chan TM, Lin M. Free Open Access Meducation (FOAM): the rise of emergency medicine and critical care blogs and podcasts (2002–2013). Emerg Med J. 2014; 31
  5.  Nickson CP, Cadogan MD. Free Open Access Medical education (FOAM) for the emergency physician. Emerg Med Australasia. 2014;26(1):76–83.
  6.  Sherbino J, Frank JR. @SirBill: the power of social media to transform medical education. Postgrad Med J. 2014 Oct;90(1068):545–6.
  7.  Kozinets RV. The Field Behind the Screen: Using Netnography for Marketing Research in Online Communities. J Mark Res. 2002;39(1):61–72.
  8.  The Healthcare Hashtag Project [Internet]. Symplur. [cited 2014 Oct 1]. Available from:
  9.  Symplur Signals [Internet]. Symplur. [cited 2015 Jun 14]. Available from:
  10.  Li LC, Grimshaw JM, Nielsen C, Judd M, Coyte PC, Graham ID. Evolution of Wenger’s concept of community of practice. Implement Sci. 2009 Mar 1;4(1):11.
  11.  Wilcock PM, Janes G, Chambers A. Health care improvement and continuing interprofessional education: Continuing interprofessional development to improve patient outcomes. J Contin Educ Health Prof. 2009;29(2):84–90.
  12.  Stamps D. Communities of Practice. Learning Is Social, Training Is Irrelevant? Training 1997 Jan;34(2):34–42.


Figure 1 – #FOAMed network centrality analysis from dates March 1st 2013 to February 28th 2015


Caption: A Network centrality graph shows users most central to the conversation. The larger the node, the more frequently mentioned the user is. Edges between nodes indicate direct communications between the users. This calculation used the 300 top members in the community.


Figure 2: #FOAMed conversation identifier analysis from dates March 1st 2013 to February 28th 2015


Caption: The Conversation Identifiers report graphs the connections among Twitter users most central to the conversation over a set period of time.


Table 1. Overview metrics for the #FOAMed analysis from dates March 1st 2013 to February 28th 2015

Metric Total Per Month Per Week Per Day Per Hour
Tweets 295832 12174 2841 406 17
Users who tweeted 36908 1518.85 354.398 50.6283 2
Tweets per user 8.0154 0.3299 0.0769 0.0101 0.0005
Impressions 1032777612 42501136 9916932 1416705 59029.4
Impressions per user 27982.5 1151.54 268.693 38.3848 1.59936

Number are rounded to the nearest whole number (except tweets per user which was rounded to 4 decimal places)

Table 2. Results of #FOAMed analysis to illustrate each proof related to a domain of the definition of a community of practice.

Definition of Community of Practice #FOAMed Proof Symplur Results
Emergence of a community:i) Are formed of interdependent groups and individuals The #FOAMed community demonstrate growing number of users (nodes) with increasing engagement and flow of information (ties) between them. The community is formed by more than 36000 members, with an average of 1537 new users per month. The overall members’ geographic location is in Anglophone countries, with smaller groups in the Europe, Latin America and AsiaNetwork centrality calculation (using 300 top members, with a node distance of 250 and a node attractions of 180) showed nodes with more influence and clusters of users associated to them (Figure 2).
ii) Consist of members who may cross clinical and organisational boundaries; #FOAMed hashtag is used by a variety of individuals and organisations We extracted a representative sample of the top 10% more active user (by number of Tweets), studying 3691 members of the community (Appendix A). We used a Symplur Signals semantic analysis tool, surveying keywords on the users’ profiles and organizing by healthcare category. From this sample, 1740 (48.95) were not able to be categorized, a total of 918 (24.8%) were identified as physicians, 368 as non-physician healthcare provider (10.1%), 425 as organizations (11.7%) and a smaller number of patients and researchers/academics
Emergence of a domaini) Members are united by the common purpose of bridging the gap between best scientific evidence and current clinical practice (Knowledge Creation). The #FOAMed community is united by the explicit goal of the creation and dissemination of medical knowledge to achieve an environment of free open access medical education and therefore knowledge translation. A word analysis shows the most common words used in building the units of content circulate around the concepts of education, free access, ultrasound, trauma, ECGs and importantly, the methods of dissemination of the community: Twitter©, YouTube©, Vimeo©, iTunes©, WordPress©
ii) Members exploit the characteristics of network-managed knowledge for creation and dissemination (Distributed network). #FOAMed generates a large and distributed network with a high degree distribution of nodes creating knowledge and distributing the content in a free manner based on the meaning and importance of the knowledge by the members of the group or subgroups. The analysis of the top 100 retweets from the most active members is consistent with the network centrality calculations, identifying members with a more central role in the fabric of the community as a function of retweets of the content generated by them. The members appear as network hubs around which clusters of users centralize.
iii) Communities use the power of the communal knowledge, contextual meaning and shared worked to solve problems (Shared knowledge) The #FOAMed community is created by creation and participation, while forming a regimen of competence where understanding of the group, engagement with other and the creation of resources if primordial. Shared (content showed a very concentrated distribution of sources for the knowledge, demonstrating a common discourse of what is relevant for the community; some being curation of traditional publication (National Center for Biotechnology Information) or open access platforms sites such as, or platforms that work for dissemination of content (Twiter© or YouTube©).
Emergence of a practicei) Members are committed to translate knowledge into praxis (Knowledge translation and practice change management). The #FOAMed community is able to create knowledge translation and convert the information created and managed in the network into practice change. The overall sentiment measurement of the top tweets of the FOAM community showed a largely neutral tone with values of 0.386 for positive, 0.296 for neutral and 0.318 for negative. In terms of a language analysis, it appears to be focused on actionable items. The terms “need”, “use”, “care” and “education” are quite frequent in the language use. The distribution of the knowledge can be observed in the use of the retweets and is consistent with actionable items and information, aimed to education
ii) Communities operate in vertical and horizontal structures and hierarchies (Hierarchical structures). The #FOAMed community has members (nodes) with a high centrality but also contains members of low centrality with high degrees of distribution. The network centrality displays the relative intensity and closeness of the ties between the nodes (Figure 1), the chart shows members of the network with increased importance and centrality, but at the same time the degree of distribution is high, which suggests a flat hierarchy. This horizontal hierarchy can be also inferred from the high number of participants in the discussion
iii) Members use social control and negotiation mechanisms to assign value and achieve change (Value and change). The #FOAMed community is able to manage content and its relevance as a function of the impact (sharing, strong) of the knowledge created and also as a function of the vetting process of high centrality nodes. The creation of value can be described as a function of the retweets, as well as the general number of tweets and tweets per user (Table 1). The deliverables are manifested by the large number of tweets with links and the redirection to Internet domains related to knowledge

The definition of a community of practice is spread across emergence of a community, emergence of a domain and an emergence of a practice.

Runner-up Abstract

Social media versus citation metrics as measures of dissemination within medical education.”


Main Section

Introduction: Social media (SoMe) metrics may be useful tools to assess educational scholarship beyond traditional scholarship metrics such as citation counts (1). While SoMe metrics may be indicative of academic influence and impact (2), how to account for them as forms of dissemination, especially in comparison to traditional measures, is unclear (3-5).  For example, individuals who are highly followed and also highly cited could indicate that SoMe metrics are related to dissemination but for individuals who have a low citation:follower ratio it is less clear how SoMe metrics relate to dissemination (6).  The purpose of this study is to characterize the relationship between SoMe and citation metrics for articles and individuals in medical education. These results may inform medical educators as to the value of SoMe metrics as measures of scholarship in relation to the value of traditional indices.

Methods: Using Symplur analytic resources, Canadian Conference on Medical Education (CCME) tweets for the 2012, 2013, and 2014 conferences were analyzed to generate a list of medical educators. We also looked at Canadian university-associated medical education centers to identify academics without a Twitter presence at the CCME. From this list of 229 medical educators, we correlated their Twitter metrics with Scopus-generated citation metrics. Additionally, we correlated citations with article-level metrics (altmetrics, which incorporates SoMe data and bibliography program data) for all papers (n=364) published between Dec. 2012 and May 2013 in three general medical education journals with the highest impact factors: Academic Medicine, Medical Education, and Advances in Health Sciences Education.

Results: For individuals, the correlation values between citation metrics and followers were 0.008 (P<0.90) and 0.009 (P<0.89) for the career and the 2010-2014 periods, respectively (Fig. 1).  The citation/follower ratio tended to be lowest for the most active CCME Twitter users and many of the most highly cited individuals had either low amounts of Twitter followers or were not on Twitter at all. For papers, the main altmetrics associated with medical education papers were Twitter mentions and Mendeley (a bibliography application) downloads, while other platforms were seldom used for dissemination (Table 1). The correlations between citations and all altmetrics studied were low and did not reach significance (P<0.05) for any altmetric, including the most commonly used SoMe metric, Twitter mentions (Fig. 2).

Discussion: There were no significant relationships between any of the SoMe metrics and citations, on either an individual- or an article-level. Before using SoMe measures as a form of dissemination for scholarship, several issues were identified that should be addressed.  For example, author retweets may influence paper Twitter counts, and Twitter mention and Mendeley download counts may only be a small fraction of the number of times a paper is accessed.

Conclusion: Our data suggests that, both for individuals and for papers, SoMe metrics are not reflective of traditional, citation-based measures of academic impact. While it is possible that SoMe metrics are related to paper access counts, this relationship should be confirmed in future studies before SoMe metrics can be used as tools to assess academic dissemination.



  1. Lafferty NT, Manca A. Perspectives on social media in and as research: A synthetic review. Int Rev Psychiatry 2015; 27(2): 85-96.
  2. Stewart B. Open to influence: what counts as academic influence in scholarly networked Twitter participation. Learn Media Technol 2015; Epub ahead of print.
  3. Eysenbach G. Can tweets predict citations? Metrics of social impact based on Twitter and correlation with traditional metrics of scientific impact. J Med Internet Res 2011; 13(4):e123.
  4. Haustein S, Peters I, Sugimoto CR, Thelwall M, Larviere V. Tweeting biomedicine: An analysis of tweets and citations in the biomedical literature. J Assoc Inform Science Technol 2014; 65(4): 656-669.
  5. Fox CS, Bonaca MA, Ryan JJ, Massaro JM, Barry K, Loscalzo J. A randomized trial of social media from Circulation. Circulation 2015; 131(1): 28-33.
  6. Hall N. The Kardashian index: a measure of discrepant social media profile for scientists. Genome Biol 2014; 15(7): 424.


Figure 1. Citations as a function of Twitter followers for a population of Canadian medical educators.  A list of 229 medical educators affiliated with Canadian medical schools was generated from the Symplur database of individuals who tweeted at the Canadian Conference on Medical Education (CCME) at the 2012, 2013, and 2014 meetings (using hashtags #CCME2012, #CCME13, or #CCME14). Medical educators associated with Canadian medical school-affiliated medical education centers were also included, to represent individuals who are not active on Twitter during the CCME. A) Career citations as a function of Twitter followers. B) Citations in the most recent completed 5 year period (2010-2014) as a function of Twitter followers.


Figure 2. Citations as a function of Twitter mentions for medical education articles published between December 2012 and May 2013 in Academic Medicine, Medical Education, and Advances in Health Sciences Education. No significant relationship was identified between citations and Twitter mentions in any of these three journals.


Table 1. Social media tools used in the dissemination of medical education journal articles published between December 2012 and May 2013.  The articles characterized were published in three of the highest impact, general interest medical education journals. The social media data was generated for each article using the Altmetrics tool.




Stanford Medicine X is the leading academic conference on emerging technology and medicine. As a world-leader in social media engagement, Medicine X 2014 generated more than 160,000,000 social media impressions, trended #1 on Twitter and reached an estimated 5,500,000 unique individuals during the three day conference in September 2014.

Symplur is the leading social media healthcare analytics firm. Its research analytics tool, Signals, aims to empower decision-making with real-time access to insights from over a billion healthcare social media data points. Symplur Signals is a web-based platform that invites an unparalleled voyage deep into the analytics of the global Twitter based conversations swirling around the topic of healthcare.

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