Samuel Alan Stewart*, Dalhousie University, Halifax, Canada Syed Sibte Raza Abidi, Dalhousie University, Halifax, Canada Track: Research Presentation Topic: Web 2.0-based medical education and learning Presentation Type: Oral presentation Submission Type: Single Presentation Building: LKSC Conference Center Stanford Room: Lower Auditorium 130 Date: 2011-09-18 10:30 AM – 12:00 PM Last modified: 2011-08-12

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Background
Web 2.0 provides new and valuable tools to the world of medicine, providing clinicians with the means to cross the physical and temporal boundaries that prevent face-to-face communication, allowing them to share their experiential and clinical knowledge with one another. Moving forward with web 2.0 tools in the healthcare community, it is important to understand how clinicians communicate online, so that we may improve the provision of these services, and ultimately facilitate the formation of virtual communities of practice. Social Network Analysis (SNA) can provide insight into how these web 2.0 communication networks function. This paper explores the potential for SNA methods to explain the patterns that occur in web communications, including identifying content experts and isolating potential subgroups of interest. These results are incorporated into VECoN, a novel network visualization tool designed to improve the standard network exploration process by presenting the network graphically and incorporating SNA statistics into the presentation.
Methods
The Pediatric Pain Mailing List has over 700 members that use it to communicate with their peers from around the world to ask questions and share experiences about the provision of pediatric pain management. This mailing list will be analyzed using SNA techniques in order to identify content experts and isolate potential subgroups of interest. These results will then be presented in a Java-based network visualization tool called VECoN.
Results
Using micro-level SNA techniques, potential content experts were found based on their centrality and prestige measures. Clustering algorithms have not been as successful in isolating potential subgroups, which may be due to the choice of algorithm selected, or to the lack of existing subgroups. The results, when presented in the VECoN system, provide new and valuable insight to the users about the structure of the network.
Conclusions
Medicine 2.0 technologies can provide valuable online tools for facilitating knowledge sharing, and understanding the flow of knowledge in these virtual communities is key to developing new systems. SNA provides the necessary tools for understanding the flow of communication within these networks. It has provided a list of potential content experts within the list, it has recognized several active subgroups, and it has partitioned the network into disparate groups of potentially different clinicians. Though the VECoN project is only in its beta stages, preliminary results are promising. The network has been visualized, and SNA tools have been added. The ultimate goal of the VECoN system is to provide a novel network exploration tool to help users make new connections within the PPML community, expanding the scope and connectivity of the virtual community.