From a visual inspection, it is apparent that the teams are quite different in structure. Team 1 has a large number of connections between individuals of different roles. The attending physician has frequent interactions with more members of the team than does anyone else. The family has frequent contact with both nurses, both of whom have connections with several other members of the team, including the attending physician, the residents, and the pharmacist. With the exception of the respiratory therapist, all team members have contact with at least four other team members across various roles.
By contrast, Team 2 is characterized by a relatively small number of daily connections between team members. In this team, it is the nursing staff, rather than the medical staff, who seem to be central. As in Team 1, the patient's family has frequent contact only with the nursing staff. In this case, however, the patient's family reports they have contact only with a nurse who is relatively isolated from the rest of the team. Thus, this family has a relatively greater distance from the rest of the team than does the first patient's family.
It is unclear whether different team structures are more appropriate to some clinical situations than others. Although the relatively unconnected, nurse-centered structure of Team 2 might be more appropriate for stable patients with little day-to-day change in their clinical status, it remains unclear whether such a structure may nonetheless be more prone to errors in handoffs of information. Such differences in team structure might also affect family members' perceptions of the quality of care. SNA provides indices of team functioning that could be used as predictor variables in later studies of quality of care.
Departmental structure of research-mentoring advisory committees
We assembled lists of the advisory committees for 53 federal career development awardees at our institution. This represented all such awardees who were active at our institution at some point during the period 2004 through 2006, where the awardee continued to serve on our faculty at the beginning of 2007. A total of 157 faculty served as committee members. Such membership not only allows the awardees to benefit from a greater diversity of expertise, but also provides an opportunity for faculty from different departments and disciplines to meet and interact with one another. Furthermore, if an awardee's committee members sit on multiple other committees, this provides the awardee with greater opportunity to indirectly network with other awardees.
Such archival data contain large amounts of information, which would be challenging to interpret by simply examining all 53 mentorship-committee membership lists. SNA, however, can provide depictions of these data that are readily interpretable and quantifiable. In fact, this sort of committee-membership data permits examination of two different kinds of networks. First, the network of connections between awardees with one another via shared committee members, and second, the network of connections between faculty via shared committee membership. Both types of networks can be coded by departmental affiliation. This permits inferences about the degree to which different departments, as a whole, are associated with one another via their respective members' committee work. Important to understanding the relationships in this type of network is that the individuals are not connected directly to one another by individual choice but, rather, through their joint memberships in formally convened groups.
Figure 3 displays the network of awardees, with different degrees of shading or no shading to distinguish each academic department. Each circle represents an awardee. Each line in the diagram represents a shared committee member between pairs of awardees; thicker lines indicate more than one shared member. The size of the nodes is proportional to the total number of committee members that awardees share with other awardees. Note that this network does not contain information about the committee members' departmental affiliations.
Several general conclusions can be drawn from this diagram. First, awardees in departments 4 and 19 have the greatest diversity in their committee memberships, as evidenced by their awardees' committee members' frequently shared membership in other committees and generally larger circle sizes. Second, awardees in these departments frequently share committee members with awardees in other departments. By contrast, department 10 represents the opposite situation. Several awardees are not at all connected to the network, indicating that none of these individuals' committee members serve on any other committees. Furthermore, at least a few of these awardees share more or less the exact same committee, as indicated by the thick line connecting them. Thus, it seems that awardees in this department have committees comprising individuals who are largely chosen from within their own departments, and that many of these committee members did not have the experiences of serving on other committees during this time. Those that do seem to have more committee memberships were largely serving on other committees within their own departments. Finally, department 18 is heterogeneous, with some awardees' committees composed of individuals who serve on committees of several other departments, while other awardees have committees composed largely of individuals who do not have other committee experience.
Figure 4 complements Figure 3 by providing information about committee members' affiliations with one another. Because a network of 157 separate individuals is difficult to interpret, committee members have been collapsed within departments, which are represented by circles in the diagram. The thickness of lines between departments is proportional to the number of committees that shared members from that pair of departments. This view provides an overall summary of the number of times that members of each department jointly served on awardee committees.
These results mirror those of the awardee network. There are large numbers of faculty connections between departments 4 and 19. In general, department 19 seems to be the most well connected, with large numbers of frequent connections with several other departments. Furthermore, department 13, although having few connections with the rest of the network, brings together the relatively isolated departments 14 and 22, thus forming a small subnetwork within the larger network. This depiction not only provides an economical way of looking at all the committee memberships simultaneously but also suggests an inherent methodology for tracking efforts to improve interdisciplinarity; if such data were collected and analyzed yearly, this would provide an index by which institutional change could be measured. These data also help identify departments that may need some additional effort to become more interdisciplinary in their support of new researchers.
Leadership of a multidisciplinary research institute
As part of a multiyear NIH award to establish a Clinical Translational Sciences Institute (CTSI), our institution defined a series of 11 key functions (i.e., the major administrative units within the CTSI), each with its own directors (from one to four) and staff. The shared purpose of these key functions is to promote translational research by giving investigators access to institution-wide services such as funding opportunities for pilot studies and for the development of novel research methodologies; research support services such as study design consulting, informatics support, and a clinical research center; and education and training in the evolving disciplines of clinical and translational research.
As part of an initial evaluation of the structure of the CTSI, we asked all the key function directors to rate their current state of knowledge of the role of the other 10 key functions, as well as their anticipated frequency of interaction with the other key functions. We then averaged these responses, creating a mean level of knowledge and anticipated interaction of each possible pair of key functions. This allowed us to produce a customized report for each key function that described the strength of their relationships with each of the other ten key functions.
To produce a network diagram of the key function directors' levels of knowledge about one another's key functions (Figure 5), we assessed a number of possible cutoffs for the means mentioned in the preceding paragraph. An arrow was drawn between key functions if the mean response for that pair was greater than the cutoffs. Note that these connections are thus bidirectional; if directors of a pair of key functions both indicate a high level of knowledge or anticipated interaction with one another, then the connection between them is represented by a two-headed arrow. On the other hand, the relationship would be one-way if one key function gave a high response about the second, but if that response were not reciprocated.
In the figure, the size of the nodes is proportional to the number of incoming arrows (i.e., for the respective key function, the degree to which other key function directors report a high level of understanding of the role of that key function within the CTSI). Figure 6 represents the same data, but with the size of the nodes proportional to the number of outgoing arrows (i.e., the degree to which the directors of the respective key functions report that they understand the roles of the other key functions). From inspection of Figures 5 and 6, it is apparent that although key functions 2, 3, 4, 6, and 9 are well understood by many other key function directors, their own directors report relatively less understanding of the other key functions. By contrast, key functions 1 and 10 are relatively less well understood, although their directors report a relatively high level of understanding of the role of many other key functions. Furthermore, the diagrams also illustrate potential weaknesses in relationships between specific pairs of key functions. Thus, these results reveal specific gaps in knowledge that could be addressed early through targeted interventions.
Figure 7 presents data about key function directors' anticipated interactions with one another. The size of the nodes is proportional to each key function's betweenness, which is a measure of the degree to which each key function is on a pathway between all other possible pairs of key functions. Thus, those with high betweenness values are more critical to connecting other key functions that would otherwise have little contact with one another. Key functions 9 and 12 owe much of their high betweenness to the fact that they connect the larger network to the smaller subnetwork comprising key functions 2, 3, 6, and 8. Key function 6 is also prominent because of its exclusive connections with key functions 2 and 3. Finally, key function 11 also uniquely connects numerous other key functions, although this is not immediately apparent from simply inspecting the arrows between nodes. These analyses can thus be used to identify deficits and strengths in the network. If efforts are made to promote different interrelationships between key functions, these diagrams can serve as a baseline from which to assess institutional change.
Discussion and Conclusions
The examples presented in the previous section highlight some ways that the tools of SNA can shed light on a range of a medical center's organizational dynamics. In addition to these purely visual representations of data, SNA also permits computation of a range of numerical indices, both for individuals and for networks as a whole, and thus helps identify trends in network structure over time.
The results of SNA must be interpreted in light of other sources of information about organizational structure and function, which are typically more qualitative and nuanced. Nonetheless, with its standardized methodology and reporting formats, SNA provides a useful complement to such subjective impressions and provides standard metrics for assessment of change in organizational structure over time. Furthermore, SNA data are relatively simple and rapid to collect and analyze, thus offering an appealing complement to more labor intensive methods involving observers or structured interviews of network members. SNA is also relatively free of observer bias. Thus, SNA provides a promising addition to the set of tools currently used to assess and evaluate organizational structure.
The authors thank Tina McCoy, Nicole O'Dell, Kathleen Holt, and Nikki Murray for their help in data collection.
The research reported here was made possible by Grant Number UL1 RR024160 from the National Center for Research Resources (NCRR), a component of the NIH and the NIH Roadmap for Medical Research.
This report's contents are solely the responsibility of the authors and do not necessarily represent the official view of NCRR or NIH. Information on NCRR is available at (http://www.ncrr.nih.gov). Information on Reengineering the Clinical Research Enterprise can be obtained from (http://nihroadmap.nih.gov/clinicalresearch/overview-translational.asp).
2 Carey TS, Howard DL, Goldmon M, et al. Developing effective interuniversity partnerships and community-based research to address health disparities. Acad Med. 2005;80:1039–1045.
3 Kindig DA, Dunham NC, Eisenberg JM. Needs and challenges for health services research at academic health centers. Acad Med. 1999;74:1193–1201.
5 Sostman HD, Forese LL, Boom ML, et al. Building a transcontinental affiliation: A new model for academic health centers. Acad Med. 2005;80:1046–1053.
6 Cohen JJ, Siegel EK. Academic medical centers and medical research: The challenges ahead. JAMA. 2005;294:1367–1372.
7 Holmes EW, Burks TF, Dzau V, et al. Measuring contributions to the research mission of medical schools. Acad Med. 2000;75:303–113.
8 Wasserman S, Faust K. Social Network Analysis. New York, NY: Cambridge University Press; 1994.
9 Durland MM, Fredericks KA, eds. Social Network Analysis in Program Evaluation. San Francisco, Calif: Jossey-Bass; 2006.
10 de Nooy W, Mrvar A, Batagelj V. Exploratory Social Network Analysis With Pajek. New York, NY: Cambridge University Press; 2005.
11 Scott J, Tallia A, Crosson JC, et al. Social network analysis as an analytic tool for interaction patterns in primary care practices. Ann Fam Med. 2005;3:443–448.
12 Hallinan MT, Williams RA. Interracial friendship choices in secondary schools. Am Sociol Rev. 1989;54:67–78.
13 Loomis CP, Morales JO, Clifford RA, Leonard OE. Turrialba: Social Systems and the Introduction of Change. Glencoe, Ill: The Free Press; 1953.
14 Burt RS, Minor MJ, eds. Applied Network Analysis: A Methodologic Introduction. Beverly Hills, Calif: Sage; 1983;195–222.
15 Michael JH. Labor dispute reconciliation in a forest products manufacturing facility. Forest Prod J. 1997;47:41–45.
16 Christakis NA, Fowler JH. The spread of obesity in a large social network over 32 years. N Engl J Med. 2007;357:370–379.
© 2009 Association of American Medical Colleges
17 Keating NL, Ayanian JZ, Cleary PD, Marsden PV. Factors affecting influential discussions among physicians: A social network analysis of a primary care practice. J Gen Intern Med. 2007;22:794–798.