This evaluation examines the relationships among members of a Quality Improvement (QI) initiative intended to improve the care and management of children with Asthma in the local community. QI methods typically involve teams of people working across multiple professional and organizational boundaries to enact change in a complex system of care. While the overall success of the initiative will be evaluated based on the improvement in health outcomes, a more immediate evaluation need was to assess a pathway to future success, namely, the connections between individuals involved in the initiative. Connections were important because the standard work responsibilities of individuals tends to occurs within existing work relationships while the QI work requires that individuals form new working relationships. Social network analysis is a methodology that visualizes and quantifies the relationships in networks of people and was used here to evaluate the connectedness between people. Individuals involved in the initiative indicated the extent to which they interacted with every other member of the initiative prior to beginning work on the initiative and presently. The connections between people were evaluated using a density measure and were found to have significantly increased for several QI teams. The role of persons in the network was evaluated using several centrality measures and was found to remain essentially stable. Implications for evaluation and the improvement of improvement initiatives are discussed.

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Connections 22(2): 1-11.
Social Network Analysis: How Does a Division-Wide Project Impact Interactions?
Daniel McLinden1, EdD; Tom Dewitt1, MD; Anne McCranie2, PhD Candidate; Stacey Farber1, PhD, Lisa Vaughn1, PhD
1General & Community Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
2 Department of Sociology, Indiana University, Bloomington, IN
ABSTRACT
In a large Quality Improvement (QI) project made up of multiple teams, success required that individuals work
together to improve medical and quality of life outcomes in the community. A leading indicator of success could
be a measure of the relationships among the network of people that make up the initiative and to examine those
relationships Social Network Analysis (SNA) was applied. All participants of a division-wide asthma quality
improvement project, including members of the clinical, research, and education sections, were surveyed with
regard to their interaction with each other in order to analyze the perceived network structure prior to and after
implementation of the QI effort. Results indicate that interactions increased within and between teams of
participants. These findings illustrate the need to focus on the interpersonal interactions and teamwork
necessary to achieve effective outcomes in quality improvement projects and illustrate the use of SNA as a
method to measure status and change in the interpersonal aspects of QI.
METHODS
Data collection
Instrumentation. Individuals responded to a survey questionnaire that listed persons on their team and
individuals on other teams. For each individual listed the respondent was asked to assess the frequency of
their interaction with each of the persons listed.
Timing. The optimum timing for data collection would be to obtain a pre-intervention baseline however, because
the initiative was already underway at the time that SNA was proposed an independent pre- and post-
measure was not possible. Therefore, respondents were asked to indicate both their current status and to
their recall of their prior status.
Respondents. The initiative was comprised of multiple teams and each team was focused on a different facet
of the initiative; Coordination of Care, Self Management, Environment, Finances, and Registry Update. All
members of the initiative were asked to participate and fifty one persons were identified as potential
respondents. Several persons were on multiple teams and since each questionnaire was linked to both the
person and the team, these people were asked to complete multiple questionnaires so the total number of
survey questionnaires distributed was 61.
Data Structure
Binary Matrix. Each respondent in the QI initiative evaluated interactions with every other person in the
initiative for four time periods; daily, weekly, monthly, or over several months and assessed their interaction
before the QI intervention and currently. Respondents indicated if interactions were present or not for each
of these time periods. Data was coded as 0 for no interaction and 1 for an interaction. The results was eight
binary matrices.
Valued Matrix. Data was coded based on the frequency option that was selected. If a respondent responded
“daily,” this response was coded as a four and indicated a relationship that was more frequent when
compared to other categories, “weekly” was coded as three, “monthly” as two, “several months” as one and
“never” or missing data was coded as a zero. This coding resulted in two matrices, one matrix of
interactions before the initiative and one matrix for current interactions.
Analysis
Data was entered into Ucinet software (Borgatti, Everett, Freeman, 2002) and this software was used for
statistical analyses and NetDraw software (Borgatti, 2002) was used to visualize the networks.
Visualization of the network
Positioning points in 2-dimenisions for visualization. Points were positioned using Gower Metric Scaling
with equal node repulsion applied simultaneously to both the “before” and “current” matrices.
Linking nodes. In a network map, connections between actors in the system are shown by lines. If one
person (Person A) in a dyad (A,B) indicates a relationship to another person (Person B), then a line is
drawn from A to B with an arrowhead pointing at B to indicate that the direction of the relationship is from
A to B. If Person B indicates that there is a relationship as well, then the line has an arrowhead at both
ends indicating the direction of the relations is also from B to A. This analysis includes both respondents
and nonrespondents.
Quantitative analysis of the network
Network Density. Density is a measure of the number of ties in a network relative to the total ties possible.
Densities of two networks with the same actors were compared using the t-test statistic (Sniders &
Borgatti, 1999) for paired samples and estimating error using a bootstrap with 5000 samples. The density
of the entire network was compared for each time period (i.e., daily, weekly, monthly, more than monthly).
Centrality. Centrality is measure of person’s involvement in and contribution to a network (Borgatti, 2005)
and multiple measures were used. In-degree and out-degree centrality and betweenness measures were
calculated. In-degree is the number of incoming links to a person; out-degree is the number of linking
outgoing from a person; betweenness measures the extent to which a person connects other people in the
network (Cross & Parker, 2004).
RESULTS
Response rate. The response rate was 64% overall (N=39) and varied by team between 40% and 86%.
Density. The density of the network increased with the number of reported interactions increasing for the time
periods of weekly, monthly, and less often than monthly when comparing before to during the initiative; with
the exception of the daily time period all other differences were significant (Table 1). These findings
suggest that interactions were unchanged in the brief time period of one day but increased with increasing
time. Although significant, the difference for the longest time period might not be meaningful as the time
period is too long to be of practical importance in an organizational intervention of this type. Focusing on
the monthly time period, the next largest absolute difference, a t-test of the difference for each of the teams
was calculated and differences were significant for the Coordination, Self-management, and Registry
teams (Table 2).
Network maps. Figures 1 and 2 are the network maps for monthly interactions for “before” and “current”
respectively. These maps visually show the increase in density when going from “before” to “current.”
Centrality. A correlation coefficient was calculated for multiple centrality measures: in-degree and out-degree
before (r=0.75), in-degree and out-degree current (r=0.79), in-degree before and in-degree current (r=0.94),
out-degree before and out-degree current (r=0.87). Likewise, the correlation of betweenness in the “before”
time period with the measure in the current time period was near perfect (r=0.99). All correlations were
significant. While the number of linkages between persons may have increased from “before” to “current”
(i.e., density), this change was not the result of dramatic changes to the centrality of persons. In other
words the most connected persons before the QI intervention were most likely to be the most connected
persons during the intervention.
Table 1: t values for comparison of overall average density between before
and current time periods.
*Difference is significant at p<0.05 for a one-tailed test that density increases
from before to current. t statistic assumes paired samples.
*Difference is significant at p<0.05 for a one-tailed test that density increases
from before to current. t statistic assumes paired samples.
Table 2: t values for comparison of team density between before and current
for Monthly interactions.
Figure 2: Network map for monthly connections after the initiative
CONCLUSIONS
Large scale interventions in healthcare involve many people, in many roles, in multiple parts of an organization.
The connections between people or lack of connections facilitate or constrain information flows, enhance or
hinder transactions, increase or decrease access to resources, and so on. Understanding these connections
provides stakeholders with information on barriers to change and opportunities to improve the intervention. In
this case SNA demonstrated that interactions increased and pointed to specific teams and time periods for
which these changes were most salient. This analysis, although retrospective, demonstrated that persons most
prominent in the network remained relatively stable prior to and during the intervention. Although the
conclusions need to be tempered by the timing of data collection, these results indicate that individuals are
engaged in the QI work and this finding is important to long-term success of the intervention. Additionally, these
results suggest that SNA may be a useful adjunct to large scale QI implementations.
Figure 1: Network map for monthly connections before the initiative
Timing
Prior
Density
Current
Density Difference t
statistic
95%
Confidence
Interval
Daily 0.0212 0.0196 -0.0016 -0.61 -0.0066, 0.0034
Weekly 0.0961 0.1067 0.0106 1.66* -0.0023, 0.0234
Monthly 0.1627 0.2102 0.0475 3.50* 0.0212, 0.0737
Less often
than monthly
0.2529 0.3235 0.0706 4.25* 0.0380, 0.1031
Team
Prior
Density
Current
Density Difference t
statistic
95%
Confidence
Interval
Coordination 0.0917 0.1583 0.0667 2.22* 0.0077, 0.1256
Self -
Management 0.5179 0.75 0.2321 2.80* 0.0699, 0.3944
Environment 0.3667 0.3333 -0.0333 -0.46 -0.1747, 0.1081
Finance 0.1333 0.1667 0.0333 0.54 -0.0880, 0.1547
Registry 0.25 0.5833 0.3333 2.51* 0.0734, 0.5933
Mixed teams 0.1 0.1182 0.0182 0.98 -0.0181, 0.0544
This project was approved by the Institutional Review Board (2008-0624).
Environmental
Registry Update
Self Management
Coordination of Care
Mixed
Financial
Environmental
Environmental
Registry Update
Registry Update
Self Management
Self Management
Coordination of Care
Coordination of Care
Mixed
Mixed
Financial
Financial