Use of social network analysis methods to study professional advice and performance among healthcare providers: a systematic review
Sabot et al. Systematic Reviews
Use of social network analysis methods to study professional advice and performance among healthcare providers: a systematic review
Kate Sabot 0 1
Deepthi Wickremasinghe 0 1
Karl Blanchet 2
Bilal Avan 0 1
Joanna Schellenberg 0 1
0 Department of Disease Control, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine , Keppel Street, London WC1E 7HT , UK
1 The Centre for Maternal, Adolescent, Reproductive and Child Health (MARCH), London School of Hygiene & Tropical Medicine , Keppel Street, London WC1E 7HT , UK
2 Department of Global Health, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine , London , UK
Background: Social network analysis quantifies and visualizes relationships between and among individuals or organizations. Applications in the health sector remain underutilized. This systematic review seeks to analyze what social network methods have been used to study professional communication and performance among healthcare providers. Methods: Ten databases were searched from 1990 through April 2016, yielding 5970 articles screened for inclusion by two independent reviewers who extracted data and critically appraised each study. Inclusion criteria were study of health care worker professional communication, network methods used, and patient outcomes measured. The search identified 10 systematic reviews. The final set of articles had their citations prospectively and retrospectively screened. We used narrative synthesis to summarize the findings. Results: The six articles meeting our inclusion criteria described unique health sectors: one at primary healthcare level and five at tertiary level; five conducted in the USA, one in Australia. Four studies looked at multidisciplinary healthcare workers, while two focused on nurses. Two studies used mixed methods, four quantitative methods only, and one involved an experimental design. Four administered network surveys, one coded observations, and one used an existing survey to extract network data. Density and centrality were the most common network metrics although one study did not calculate any network properties and only visualized the network. Four studies involved tests of significance, and two used modeling methods. Social network analysis software preferences were evenly split between ORA and UCINET. All articles meeting our criteria were published in the past 5 years, suggesting that this remains in clinical care a nascent but emergent research area. There was marked diversity across all six studies in terms of research questions, health sector area, patient outcomes, and network analysis methods. Conclusion: Network methods are underutilized for the purposes of understanding professional communication and performance among healthcare providers. The paucity of articles meeting our search criteria, lack of studies in middleand low-income contexts, limited number in non-tertiary settings, and few longitudinal, experimental designs, or network interventions present clear research gaps. Systematic review registration: PROSPERO CRD42015019328
Social network analysis; Health outcomes; Health system performance; Professional advice; Professional communication; Healthcare workers; Network analysis; Systematic review
In 2015, the Millennium Development Goals (MDGs)
expired after 15 years of galvanizing the global
development community around health targets related to women,
children, and HIV and AIDS. Their replacement, the
Sustainable Development Goals (SDGs), broaden global
focus beyond health [
]. As such, the health sector will
need to explore new ways to influence provider practice
and scale up best practices to meet the outstanding MDG
targets and improve health outcomes. Understanding and
harnessing the power of existing professional advice
networks among healthcare providers could assist in
influencing provider practice and improving health outcomes
in low- and middle-income countries (LMIC). Social
network analysis focuses on studying relationships
between and among individuals (or organizations) who are
connected by one or more ties of interdependency, such
as love, friendship, kinship, trust, collaboration, or
]. Social network analysis (SNA) can lend
insight into defining, measuring, and understanding these
professional communication networks and therefore
designing effective network interventions to improve
provider performance and ultimately, health outcomes [
SNA is defined as a means of mapping and exposing
channels of communication and information flow,
collaboration, and disconnection between people [
is both a theory and a methodology that has generated a
body of empirical research [
]. One of these theories
is diffusion of innovations defined by Rogers as the
“process by which an innovation is communicated
through certain channels over time among members of
social system” (p. 5) . Rogers proposed that
individuals go through several stages in deciding to “adopt” an
innovation; a process influenced by the characteristics of
innovations, specifically the complexity, triability,
observability, and the relative advantage conferred by the
]. Individual adoption of an innovation
can be expressed as a normal distribution, segmenting
individuals into categories of individual innovativeness:
innovators, early adopters, early majority, late majority,
and laggards .
Professional behavior change among healthcare
providers is often referred to as knowledge translation or
transfer. We hypothesize that certain network structures
and the presence of network roles within networks of
healthcare providers can facilitate diffusion of
innovations, or knowledge translation and transfer, particularly
where the issue is lack of provider information, and that
may in turn change practices and improve patient
outcomes. Admittedly, this is a simplification as
relationships among healthcare providers are multiplex and
friendship or trust networks rather than purely
professional communications may be more influential in
changing provider behavior when there is informational
]. As such, it is important to consider both
formal and informal professional communication in an
attempt to mitigate this concern.
While it is not possible for this paper to include a
comprehensive overview of social network analysis study
designs, data collection, and data analysis methods, the
key concepts are highlighted and further explanations
can be found elsewhere [
]. Social network analysis
studies are defined primarily as either whole network,
including all members of a group defined by a specified
boundary or ego network studies, capturing the
networks of select individuals within a network. Hybrid
models can combine elements of both approaches. All
networks are characterized by whether the network is
“directed,” indicating the orientation of the relationship,
for example if A influences B, then the tie would include
an arrowhead at B or “undirected” where the
relationship either exists or does not and none of the ties or
lines have arrowheads. They are also either “valued”
capturing the intensity of the relations on a scale or
“unvalued,” whereby these relations are dichotomous. Network
data can be captured through questionnaires, interviews,
observations, existing records, diaries, or other methods
]. Other data collection methods or ways of generating
networks of providers include journal publication
coauthor lists, identifying patient-sharing among providers,
attendance at conferences, and participation on social
media forums, to name a few.
Social network analysis data analysis method options
depend on how the data were collected and the research
questions of interest. In SNA, visualizing data is both a
means of presenting findings as well as a tool for
generating additional findings. Quantification of network
properties are subject to certain constraints as the unit
analysis is a relationship between actors (individuals or
organizations) rather than independent observations.
Thus, SNA requires analytical tools that do not rely on
independence of observations or relations [
can be at the actor, subgroup, or network level.
Common subgroup structures are dyads, triads, clusters,
cliques, components, and bridges [
]. Many network
metrics can be calculated including degree, density,
centrality, reachability, and distance. Some of these can be
calculated at the network or actor level or both. Gesell
et al. [
] recommend calculating isolates, degree, and
reciprocity at the actor level, and at the network level.
the presence of subgroups, density, centralization,
transitivity, and cohesion as the metrics most likely to have
effect on individual and group processes.
A 2012 systematic review of SNA applications in
healthcare settings concluded that SNA’s potential has been
unrealized in the health sector, particularly because
virtually all identified studies were simple network descriptions
rather than studies of network interventions [
review had a definition of a healthcare setting that
excluded community-based health workers and
interventions, a limitation particularly relevant in LMIC and
The present systematic review builds on the Chambers
et al.’s [
] review in the following ways: broadening the
definition of “healthcare settings” to be inclusive of
community-based settings, expanding the databases and
search terms, and updating the searches to include
articles from 2011 to 2016. The focus of the review
synthesis is substantively different looking specifically at SNA
methods used to understand healthcare provider
communication and performance. The primary research
question this review sought to address is what SNA
methods have been used to study professional
communication and performance among healthcare providers?
Secondary research questions included:
Does professional communication improve health
outcomes? What professional communication
network properties are associated with health
What methods have been used for which types of
What are the main limitations of the SNA methods?
What is the quality of these studies?
What is the quantity of SNA studies? What was the
evolution over time?
To what extent has this research taken place in
lowand middle-income countries?
To what extent has this research focused on
community-based health providers?
For any systematic review, it is critical to clarify our
meaning when using terms that define a search strategy.
For this review, we have operationally defined
“healthcare providers,” “professional communication,” and
“performance” as follows.
In this context, we defined “healthcare providers” as
physicians, clinical officers, nurses, midwives, counselors,
physician’s assistants, and others who provide health-related
services to patients in formal medical environments.
Additionally, community-based cadres such as community
health workers, village health workers, traditional birth
assistants, and others were also considered healthcare providers.
For our purposes, we defined “professional
communication” as formal or informal professional advice-seeking
or giving or discussion about hypothetical or actual work
situations or patients. For example, studies exploring
friendship networks of healthcare providers were not
considered eligible, unless they also captured communication
related to work situations or patient care and documented
patient health outcomes.
We defined “performance” as a study including a
patient health outcome. Studies that only considered
“patient satisfaction” or healthcare provider “perceptions
of performance” were not eligible for inclusion.
The search strategy focuses on the intersection of SNA
and diffusion of innovations, the term used in the SNA
community most relevant for professional
communication related to knowledge sharing and transfer. Since
health policy and health systems research often use
“knowledge translation or transfer” language, the search
strategy also includes the intersection between those
terms and SNA. As a methodologically focused review,
this review will highlight the range of SNA methods
To address the research questions, the systematic
review focused on three concepts that are integral to the
primary research question: (1) SNA, (2) diffusion of
innovations, and (3) knowledge translation and transfer.
The key terms for these concepts are shown in
Additional file 1 and truncation search terms will be
used to make the search inclusive.
Concept 1: social network analysis
The search strategy for the SNA concept was adapted
from the Chambers et al.’s scoping systematic review
of Social Network Analysis and healthcare settings
]. This was particularly helpful guidance as a more
recent SNA review; Cunningham et al. noted the
challenge of “social network” yielding irrelevant social
media or social support articles [
]. One of the
changes from the Chambers et al. review was an
expansion of the list of SNA software listed (from
four: UCINET, NetDraw, Pajek, and KrackPlot to 56),
which was guided by a chapter in the SAGE
Handbook of Social Network Analysis [
]. Depending on
the database, specific software packages (Blanche,
InFlow, Jung, ORA, ORS, Pnet, Puck UNISoN, SNAP,
and STRUCTURE) were excluded as they yielded
thousands of off-topic articles. See Additional file 2
for a list of exclusions by database. None of these
exclusions were the SNA packages included in the
previous review and for the most part are not the
most commonly used software packages for SNA. The
one exception is ORA, a SNA software package that,
for 6 of 10 databases, returned thousands of articles
that used odd ratios in their analysis. However, as this
review still yielded two studies that used ORA, we do
not feel that this negatively impacted the search.
Concept 2: diffusion of innovations
The search strategy for the diffusion of innovations concept
was influenced by the original search strategy used as a
starting point for a meta-narrative on Diffusion of
Innovations in Health Service Organizations [
]. However, the
focus on health service organizations was seen as
potentially too limiting. Therefore, terms related to health service
organizations were not included to let the review capture a
broader range of studies. “Diffusion of innovations” is a
phrase that is relatively new to health systems research.
Consequently, the review used a third concept to ensure all
relevant studies were captured, which corresponds to
diffusion of information: knowledge translation and transfer.
Concept 3: knowledge translation and transfer
Knowledge translation and transfer (KT) are terms
describing a relatively new discipline, which does not
have an agreed upon lexicon. A systematic study of KT
terms used in 12 journals found inconsistent use of KT
terms such that less than half of what the authors
classified as “KT articles” used the presumed “KT terms”
leading the authors to refer to the situation as a “tower of
]. The search strategy for this concept was
developed by determining the common terms across six
sources including four systematic reviews [
two articles on knowledge translation “KT” or “K*”
]. A comprehensive listing of all 253 K*
terms can be found in Additional file 3. An initial search
conducted using all the terms yielded over 6000 articles
in MEDLINE, which led to a revision of the approach
for this concept. Priority terms for inclusion in the
search strategy were those that appeared in more than
The search strategies were then developed looking at
the intersection of concept 1 with concept 2 and the
intersection of concept 1 with concept 3. They were
then adapted to each of the databases included in the
review, including mapping the above terms to MeSH
terms. Detailed search strategies for each of the 10
databases are available upon request—an example is
included in Additional file 4.
MEDLINE, EMBASE, PsychINFO, CINAHL, Global
Health, Social Policy and Practice, Health Management
Information Consortium, and Web of Science were
searched. Gray literature was searched via Popline. The
Cochrane Library was searched to identify other
systematic reviews and relevant studies. Several websites were
searched including International Network for Social
Network Analysis, American Evaluation Association
Social Network Analysis Technical Interest Group, and
in the International Sunbelt Social Networks Conference
Articles were downloaded into Endnote X5.0.01, a
bibliographic software package and duplicates within
and across databases were removed. All 5970 articles
were then assessed for meeting study inclusion criteria
through a three-stage review process. Two independent
reviewers (KS and DW) screened titles, abstracts, and
full-text articles; after each step, discrepancies were
discussed and reconciled.
The 10 systematic reviews identified through the
search strategies that addressed SNA had the articles
they included screened for inclusion in this review
The search strategies were executed originally from
1990 to January–March 2015 and then updated in April
2016, capturing articles published since the original
search. All systematic reviews identified had the articles
they included screened. The final set of articles had their
reference lists screened and SCOPUS was used to
conduct a prospective citation search. All articles were
subjected to our two independent reviewer, 3-stage
screening process. The PRISMA flow chart (Fig.1)
reflects the combination of the searches and screenings
conducted in 2015 and updated in 2016.
The study protocol was registered with PROSPERO DOI:
Study inclusion and exclusion criteria
A checklist was developed to guide each reviewer. A
single “no” response to any of the questions below was a
cause of exclusion of the study from the systematic
Does the study use SNA methods?
Are the study subjects healthcare providers?
Is the communication/relationship of interest
between healthcare providers?
Does the research focus on professional
Is there some metric used for performance, defined
as assessing patient outcomes?
Only English search terms were used, and studies
included were limited to those published in English since
1990. This date was selected in part because in the
previous review, Chambers et al. 2012 had 49 of 52
included articles published after 1990. Furthermore,
modern SNA studies rely on software that has primarily
existed after 1990.
This review excluded studies conducting SNA of
patient-to-patient communication or patient-to-provider
communication. Direct-to-consumer advertising and
marketing studies, such as pharmaceutical companies
marketing to potential patients, were also excluded.
Publication or research networks, provider
patientsharing networks, provider friendship networks, and
non-empiric research were excluded. Studies whose only
measures of performance were “provider perceptions” or
“patient satisfaction” were excluded as not being an
objectively measurable health outcome.
These exclusions were made on that basis that they
were not thought to lend insight into methods used to
assess professional communications among healthcare
workers and their association with patient outcomes.
Study quality assessment
Two tools were developed for critically appraising study
quality—one for qualitative studies and the other one for
quantitative study designs. These tools were informed by
STROBE, EPOC, CASP, SIGN, ENTREQ, COREQ, RATS,
QARI, and NICE Process and Methods guidelines and
checklists and seminal articles on the subject [
Systematic reviews of SNAs identified through our search
strategy were consulted as there is not a standard tool for
assessing the quality of SNA, and some of the content of
existing checklists for other study methods do not apply
for network studies [
5, 13, 22–29
]. However the existing
tools were useful starting points for assessing the quality
of studies. See Additional files 5 and 6 for the tools
developed to assess qualitative and quantitative studies and
Table 2 for the summary of study quality. As per Cochrane
and SIGN, guidance studies were assessed as being high,
medium, and low quality with no summary score
produced or a quality threshold for inclusion in the review
]. Mixed-methods studies had both tools applied
and an overall study quality assessment provided drawing
on both tools’ assessments.
Selected studies were independently critically
appraised using these tools by two individuals (KS and
DW). Discrepancies were discussed until reconciled.
Data extraction strategy
A data extraction matrix was developed after reviewing
data extraction tools used in relevant systematic reviews
and consulting with a SNA and health expert [
5, 13, 22–
]. The tool was pilot-tested and revised for greater clarity
and specificity with the final version covering 35 data
points. Data were extracted independently by two
individuals (KS and DW), results compared and discrepancies
discussed and resolved by consensus. See Additional file 7
for the tool and Tables 2, 3, 4, 5, and 6 for a subset of the
Data synthesis and presentation
Narrative synthesis was used to describe studies included in the
review, focusing on the SNA methods and metrics used [
Our searches returned 5970 articles, which after double
screening yielded six articles meeting our inclusion
]. Figure 1 documents the review process
using a PRISMA flow chart.
Studies’ characteristics are described in Table 1. They
were primarily recently published (all since 2010),
looking at multidisciplinary healthcare providers (4 of 6) and
conducted in the US (5 of 6) in tertiary care facilities or
their equivalent (5 of 6).
Below, we review findings based on each of our
Primary research question
What SNA methods have been used to study professional
communication and performance among healthcare
Tables 2, 3, 4, 5, and 6 contain extractions from the six
studies. Table 2 provides an overview of the studies,
Table 3 focuses on their SNA methods, Table 4 lists the
SNA metrics used by each study, Table 5 looks at the
association between the SNA metrics and patient
outcomes, and Table 6 explores the relationship between
research questions and SNA methods used. Key patterns
are summarized below.
All studies included in this review were exploratory in
nature. All but one study [
] employed a
crosssectional study design looking at whole networks. Four
studies could only look at one or two whole networks,
listing this as a limitation to their study’s generalizability
]. Data collection tools were typically network
surveys, designed specifically for that study. However,
one study coded observations  and another study
extracted data on healthcare worker communications
from surveys of patients that attended emergency
]. All but one study [
] visualized their
networks, which is not surprising as one of the unique
aspects of SNA methods and software is the ability to
visualize networks. Software preferences leaned towards
UCINET and ORA [
] with Microsoft Excel and
SPSS mentioned as supplementary tools. A wide range
of network metrics were calculated, although density
and centrality were the most commonly calculated. See
Table 4 below for an overview of which studies
calculated specific network metrics. There was a range in
how these data were analyzed with some integrating
them into models and others using tests of significance.
Secondary research questions
What is the quantity of SNA studies? What was the
evolution over time?
Six studies were identified, with all studies published in the
past 5 years and none before 2011. One study was
published annually from 2011 to 2013, and then, three were
published in 2015. The evolution over time suggests there
is an increasing interest in this type of study; however, with
only six studies, it may be a premature assessment.
To what extent has this research taken place in low- and
Not a single study that met our search criteria was
conducted in a low- or middle-income country. All studies
took place in either the USA [
] or Australia [
What is the quality of these studies?
The quality of the studies meeting our selection criteria
was assessed using the tools found in Additional files 5
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and 6 and summarized in Table 2. None of the studies
were found to be of low quality, two were found to be of
acceptable quality and four of high quality applying the
SIGN guidelines for assigning these categories.
What methods were used for which types of research
There were only two studies using mixed methods, the
other four only used quantitative methods.
Interestingly, Alexander et al. [
] only visualized the
communication networks and did not calculate SNA
metrics. Their authors cited this as a limitation of their
design, which did not involve coding the observations in a
way that would allow for healthcare workers to be
individual nodes. Yet another study, Lindberg et al. [
], did not
include a visualization of the communication network.
Modeling and tests of significance were used when
studies intended to measure the association of network
properties with other factors. Table 2 lists the study objectives,
research questions, study design, and data collection
methods whereas Table 3 goes into detail regarding each
study’s SNA methods. Table 4 summarizes the SNA
metrics used by each study, and Table 6 looks at the link
between research questions and study design.
What are the main limitations of the SNA methods?
Table 2 lists the limitations, identified by our reviewers
(although some were mentioned by the authors as well)
of each of the six studies. While some of these
limitations do not relate to the SNA methods, they provide
insight into some of the challenges faced by studies
using such methods. A general challenge generated by
SNA methods is the need to clearly define the study
boundary, which can limit sample size and therefore
affect the broader generalizability. This came up in
several studies noting areas for further research including
broadening to other settings and repeating the study
elsewhere given the limited sample size. The sample size
limitation related less to the number of nodes, but more
to the number and type of whole networks included.
The lack of longitudinal and experimental designs
speaks to a broader challenge in the field as these are new
areas for application of SNA methods, and the analytical
tools and software are still in development. This limited
the ability of SNA studies to address causal pathways.
Similarly, the limited qualitative methods being integrated
into the studies constrain the contextual understanding of
the network properties quantified and visualized through
applying the quantitative SNA methods.
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One study, Alexander et al. [
] reported that their
coding method limited the type of analyses that can be
conducted, and therefore, they did not analyze their
SNA data beyond visualizing patterns.
To what extent has this research focused on
communitybased health providers?
None of the studies took place in a community-based
setting. Only one study took place in a primary
healthcare context [
]. The other five studies took place in
tertiary level facilities including hospital units and
specialist care facilities like hemodialysis centers or
nursing homes [
What are the key findings of these SNA studies?
While this is a methodologically focused review, therefore
inherently less focused on any associations observed
between network properties and the health outcomes
measured, the next question naturally arises, what did
these studies find? Their overall study findings are
discussed in Table 2, but to better understand any
relationship between specific network metrics and patient health
outcomes, we looked at the metrics captured in more than
one of the studies and their reported association with
patient outcomes in Table 5. There were only two metrics,
density, and in-degree centrality reported in more than
one study. For this analysis, all centrality metrics were
collapsed into one category, although the actual centrality
metrics used in the study are specified in Table 4.
Patient outcomes generally improved when healthcare
worker communication was denser and more centralized
as measured by various centrality metrics. However, for
both metric studies reported no significant association
with some patient outcomes, as such more studies are
needed to clarify patterns.
The Effken study had one exception to the relationship
proposed between centrality and patient outcomes.
Adverse drug events increased with betweenness
centrality, possibly due to the presence of gatekeepers the
authors hypothesized. Another patient outcome, symptom
management on the surface appears to have conflicting
associations with centrality metrics; however, the authors
suggest that taken together the correlation of this patient
outcome metric with eigenvector centrality and patient
symptom management capacity with simmelian ties
(strong ties within cliques) which could point to the
importance of small group communication [
broader pattern of performance being linked to more
centralized networks is generally supported by the SNA
literature, although the debate continues [
Furthermore, patient outcomes may not necessarily be expected
to be associated with healthcare provider centrality as
they could be central for reasons other than the quality
of care or professional advice they provide. Network
density can provide more pathways for communication,
however, in its extreme, can reinforce insularity and
limit external sources of information [
]. As such a
network diagnostic tool proposed ideal network density to
be .15–.50 [
There are definite limitations to this specific analysis.
Through this process, it became clear that not every
SNA metric calculated and its association with all
outcomes captured in a study are published. Another
complication is that not all of the metrics and results were
truly comparable given the different data sources and
analytical approaches. For example, one study used
GLMM models collapsing all data collected across teams
that had different professionals with either strong or
weak ties for two types of communication networks
(electronic and face to face) rather than looking at the
in-degree centrality of a network and its association with
the patient outcome of interest.
The discussion will focus on the two main research
questions of the review, the primary research question
“What SNA methods have been used to study
professional communication and performance among
healthcare providers?” and “What methods were used for
which types of research questions?”
What SNA methods have been used to study professional
communication and performance among healthcare
The majority (5 of 6) of the studies that met our
selection criteria used a cross-sectional, observational study
design. This posed challenges in addressing the research
questions looking at the association between provider
communication networks and patient outcomes, as the
patient outcome data timeframe and the networks being
captured, were not always temporally aligned.
As other systematic reviews suggested, there remain
few network intervention studies, a frontier opportunity
for researchers [
5, 26, 27
]. See Additional file 8 for an
overview of the other SNA and health systematic reviews
identified through our search strategy and their
recommendations for further research. The lone experimental
study included in this review, Lindberg et al., did not use
network data to design the intervention, so it does not
qualify as a “network intervention” [
interventions and experimental, longitudinal study designs
will allow for SNA methods to address causal pathways,
a current limitation on how the methods are being
One of the challenges facing researchers wanting to use
SNA methods is the lack of validated SNA survey tools
for use in the health sector, as highlighted by Creswick
and Westbrook and Perkins et al. [
]. While this only
is relevant for those interested in using sociometric survey
methods, as more studies use SNA methods, we can
anticipate that a set of tools or best practices for applying
a range of SNA methods will emerge. The Perkins
systematic review aimed to address one aspect of that gap by
gathering all the name generating tools they found across
the studies they reviewed . However, this is only one
step in the process of having more systematically validated
tools and best practices available.
The most obvious pattern in study methods was that
studies looking to establish associations used more
advanced statistical methods to test their hypotheses
whereas the studies that were looking to answer
questions about processes used more qualitative methods.
However, this observation is largely less about SNA
methods and more about the relative strengths of
qualitative vs quantitative research methods. The diversity of
SNA analytical methods could also speak to the
expertise of the individual researchers and which methods they
were more comfortable using rather than necessarily a
clear advantage posed by using one method over another
to answer a given research question. That said there are
SNA methods such as exponential random graph models
which are appropriate to answer specific SNA questions
that other SNA methods would not be able to address.
These methods were not used in the studies meeting our
It is important to note that while this review did not
identify any studies conducted in LMIC, this does not
mean SNA methods have never been used to study health
in these contexts. A systematic review looked specifically
at SNA applications in LMIC and found 17 articles from
10 health-related network studies; however, their focus
was broad and none of the studies met our criteria of
focusing on healthcare provider communication and
patient outcomes [
]. Instead, these studies set in 9
countries looked primarily at patients or their household as the
ego and used name generators to establish networks
related to contraception use and family planning, mercury
consumption (2 studies), HIV transmission (5 studies),
and diarrheal disease transmission (3 studies) [
One of the issues with the way SNA methods have
been applied in the health sector is the often artificial
boundaries imposed by limiting studies to specific
cadres, which did not reflect the actual care
environments. Notably four of six studies looked at
multidisciplinary teams and one of the studies limited to one cadre,
Effken, et al. [
], suggested that future studies look at
other providers in the care setting.
There was a surprising variability across the studies
with respect to the network metrics calculated and used.
Two—centrality (in-degree) and density––were included
in 4 and 3 of 6 studies. The range of network metrics
calculated per study included in our review ranged from
0 to 15 with most calculating 3 or 4. See Table 4 for a
breakdown of which studies calculated which metrics.
What methods were used for which types of research
With only six studies meeting our criteria, there are
limits to identifying clear patterns in methods used to
address types of research questions and study objectives.
Table 6 focuses on the link between types of research
questions and study methods. Research questions were
classified as either descriptive, relational, or causal in
]. Half of the studies included more than one
type of research question. Those studies that included
causal or relational research questions typically involved
more robust quantitative analyses. Mixed methods were
used in two studies: one only had causal research
questions while the other had descriptive and relational
research questions. Most study designs were observational
and cross-sectional and had descriptive and relational
research questions. As more studies are conducted over
the coming years, these patterns will likely evolve and
become more consistent.
While the focus of our review has been on these two
research questions as applied to the six articles that met
our search criteria, there are a range of SNA methods
and metrics beyond what is discussed here which could
have applicability in answering research questions
related to healthcare professional advice networks and
performance including, but not limited to block modeling,
core-periphery, presence of structural holes and bridges,
cohesion, proximity, and prestige/prominence analyses.
Limitations of the review
This review looked at a very specific question and found
that few, albeit in recent years a growing number of
researchers, have designed studies meeting these criteria.
Our definition of performance as being assessed by
patient outcomes rather than through proxy interim
measures, such as use of evidence-based tools and practices,
restricted the studies that met our search criteria. This
may have been particularly limiting for studies of
community-based healthcare, which often takes the form
of counseling whereby certain outcomes like patient
satisfaction are more likely to be appropriate study
outcomes than patient outcomes. Our definition of
professional communication networks excluded studies of
provider friendship networks or other types of ties
between healthcare workers unless they explicitly
captured professional communication. In theory, those
networks may have embedded professional advice exchange
not captured, analyzed, or presented in the paper.
Another limitation is that we only looked at English
language publications. However, looking at other systematic
reviews of SNA studies, that is a common limitation [
22, 25, 27–29
]. For those that included studies in other
languages, like Benton et al. which included Spanish and
Portuguese language studies, they found 2 of 43 included
studies were non-English and excluded a further 2 for
language reasons . Chambers et al. and Flodgren et
] did not impose any language restrictions in
their searches but did not identify studies published
outside of English language journals, so this is unlikely to
be a major source of bias. We limited our searches to
those studies published from 1990, although given the
emphasis on software packages in current SNA studies,
it was believed that few studies would have been using
relevant methods before 1990 as those software packages
did not exist for use on widely accessible platforms.
Another limitation speaks to broader limitations of
systematic reviews. The language used for social network
analysis is vague and inconsistent, and search strategies
were challenging to devise that returned a manageable
number of articles to screen yet were broad enough to
capture all the ways in which researchers may have
described an SNA study.
Five years after the Chambers et al.’s [
] review, searched
for articles, social network analysis methods continue to
be underutilized in the health sector, particularly when
looking at healthcare provider communication and
performance. There are few studies that do more than
describe professional communication networks among
healthcare providers, for those that do, only a small
subset, six measure performance using patient outcomes.
This may be a broader reflection of the challenge in
accurately capturing patient outcome data as many
studies were excluded for using proxy measures such as
patient satisfaction or use of an evidence-based practice.
While a diverse set of methods were used across the six
studies, as more studies are conducted clearer patterns
in methods may emerge. The quality of these studies
was either acceptable or high; however, the level of
sophistication of these studies was relatively low with an
emphasis on cross-sectional study designs. This is not
an unsurprising finding as the network methods
themselves and software tools capable of dynamic and
longitudinal network analyses are still developing. As
longitudinal SNA analysis methods mature, other study
designs and network interventions should become more
common. All articles meeting the review criteria were
published in the past 5 years, suggesting that this is a
developing area of research.
One pattern that this review highlights is a trend
towards looking at multidisciplinary provider networks
rather than focusing on one cadre. Other SNA
methodological consistencies among these six studies included a
preference for calculating specific network metrics:
density and centrality. The limited number of articles
meeting our search criteria, the glaring lack of any
studies in LMIC, non-Western contexts, and in non-tertiary
settings or community-based settings present clear
research opportunities. Once there are more studies
published addressing healthcare provider communication
and performance, it may be useful to revisit this analysis
and draw conclusions on the SNA methods best placed
to answer specific research questions within this space.
Additional file 1: Search strategy concepts and associated terms. (XLSX 12 kb)
Additional file 2: SNA software search strategy for each database. (XLSX 10 kb)
Additional file 3: K* search strategy development. (DOCX 107 kb)
Additional file 4: MEDLINE search strategy. (DOCX 18 kb)
Additional file 5: Critical appraisal tool for qualitative studies. (XLSX 13 kb)
Additional file 6: Critical appraisal tool for quantitative studies (XLSX 15 kb)
Additional file 7: Data extraction tool. (XLSX 11 kb)
Additional file 8: Existing SNA systematic reviews. (XLSX 14 kb)
ADE: Adverse drug event; AR-BSI: Access-related bloodstream infection;
BSI: Bloodstream infection; CASP: Critical Appraisal Skills Programme;
CDC: Centers for Disease Control and Prevention; CNA: Certified Nursing
Assistant; COREQ: Consolidated criteria for reporting qualitative research;
DOI: Digital Objective Identifier; ED: Emergency department;
ENTREQ: Enhancing transparency in reporting the synthesis of qualitative
research; EPOC: Effective Practice and Organisation of Care; FGD: Focus
group discussion; GLMM: Generalized linear mixed model; HCW: Healthcare
worker; IT: Information technology; ITS: Information technology
sophistication; LMIC: Low- and middle-income countries; LPN: Licensed
practical nurse; MA: Medical assistant; NH: Nursing home; NHAMCS: National
Hospital Ambulatory Medical Care Survey; ORA: Organization Risk Analyzer;
PRISMA: Preferred Reporting Items for Systematic reviews and Meta-Analyses;
PROSPERO: An international database of prospectively registered systematic
reviews in health and social care, welfare, public health, education, crime,
justice, and international development; QARI: Qualitative assessment and
review instrument; RATS: Relevance of study question, appropriateness of
qualitative method, transparency of procedures, and soundness of
interpretive approach; RN: Registered nurse; SIGN: Scottish Intercollegiate
Guidelines Network; SNA: Social network analysis; STROBE: Strengthening the
Reporting of Observational studies in Epidemiology
Subject experts were consulted at various points during the development of
the protocol. We thank Dr. Tom Valente and Dr. Jim Dearing for sharing their
expertise in the areas of social network analysis and diffusion of innovations.
We thank Dr. Justin Parkhurst and Professor Sir Andy Haines for sharing their
expertise in health policy research and knowledge translation and transfer. We
thank Professor Mark Petticrew and Jane Falconer for sharing their expertise in
conducting systematic reviews. We thank Professor Val Curtis and Professor
James Hargreaves who provided input through their roles on an independent
advisory panel for the author’s (KS) DrPH Review.
The research was supported by the IDEAS––Informed Decisions for Actions
to improve maternal and newborn health (http://ideas.lshtm.ac.uk), which is
funded through a grant from the Bill & Melinda Gates Foundation to the
London School of Hygiene & Tropical Medicine (Gates Global Health grant
Availability of data and materials
Search strategies, tools, completed extraction and appraisal tools, and
endnote libraries are available upon request.
KS conceptualized research questions, wrote protocol, and registered
systematic review with PROSPERO. As primary reviewer, she screened titles,
abstracts, and full-text articles; developed extraction tools and critical
appraisal tools; and extracted data using those tools. KS is the primary writer of
the paper. As the secondary reviewer, DW screened titles, abstracts, and
fulltext articles; located articles as needed; supported development of data
extraction tool and critical appraisal tools; extracted data using those tools; and
discussed with KS until reconciled; and reviewed drafts of the protocol prior
to registration and drafts of the manuscript. KB reviewed the protocol, data
extraction tool, critical appraisal, and drafts of the manuscript. BA supported
the conceptualization of research questions and reviewed the drafts of the
study protocol and the drafts of the manuscript. JS guided the
conceptualization of research questions and reviewed the protocol, data
extraction tool, critical appraisal tool, and drafts of the manuscript. All authors
reviewed the final version of the review.
Ethics approval and consent to participate
Not applicable, conducting the systematic review did not involve human
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
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