The social elements of technical decision-making are not well understood, particular among expert committees. This is largely due to a lack of methodology for directly studying such interactions in real-world situations. This paper presents a method for the analysis of transcripts of expert committee meetings, with an eye towards understanding the process by which information is communicated in order to reach a decision. In particular, we focus on medical device advisory panels in the US Food and Drug Administration. The method is based upon natural language processing tools, and is designed to extract social networks from these transcripts, which are representative of the flow of information and communication on the panel. Application of this method to a set of 37 meetings from the FDA's Circulatory Systems Devices Panel shows the presence of numerous effects. Prominent among these is the propensity for panel members from similar medical specialties to use similar language. Furthermore, panel members who use similar language have the propensity to vote similarly. We find that these propensities are correlated – i.e., as panel members' language converges by medical specialty, panel members' votes also tend to converge. This suggests that voting behavior is mediated by membership in a medical specialty and supports the notion that voting outcome is, to some extent, dependent on an interpretation of the data associated with training.
['David A. Broniatowski', 'Christopher L. Magee']
Analysis of Social Dynamics on FDA Panels Using Social Networks Extracted from Meeting Transcripts