IntroductionNetwork analysis has been adopted across the scientific spectrum, from the social sciences to biochemistry, with applications in empirical research, modeling and management, to name a few.1,2,3,4 The network structure of operational subgroups has been examined previously, to our knowledge, a complete analysis of the operating room integrating all relevant participants does not has not yet taken place.5 When studying a network, several definitions are worth reviewing (Table 1). Networks can be directed or undirected, indicating whether an edge has a defined source and target or simply denoting the existence of a connection, and weighted or unweighted, referring to the value assigned to an edge for transmitting information related to the nature of a link. From the structure that arises from the nodes and their corresponding edges, several traits dealing with their importance in a network can be discussed.9 Although there are many and varied measures, perhaps the most commonly discussed centralities are degree, proximity, betweenness and eigenvector. it is to these that we will limit our analysis. Additionally, empirical detection of existing subgroups or “communities” in the network will also be examined. Although the clustering coefficient, a common measure of the social embeddedness of a node in a network, can be applied to undirected networks, it cannot be applied to weighted networks and therefore has not been examined.10 Centrality measures and weighting Based on Table 1, it is worth mentioning some aspects of the different centralities and their relationships. Nodes with a high degree of centrality are connected directly to proportionally larger parts of the network and are able to transfer information quickly.7 Nodes with high betweenness centrality can...... middle of paper.. .... probability that two distinct communities can be merged by moving the nodes one by one is very low. »22 We include this caveat so that others interested in applying similar techniques to their own organizational structures are explicitly aware of the potential limitations of community detection. inform and guide how new informational changes can best be implemented. An analysis across a variety of institutional types and sizes would be helpful in identifying any generalizable management concepts. Additionally, analyzing subgroups and subcommunities can help identify key structures that might affect the flow of tacit and explicit information. For example, this could be useful in an academic center where identifying and optimizing tacit information flows could improve and make more coherent the education of its residents..