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Network approaches and interventions in healthcare settings: A systematic scoping review

  • Ameneh Ghazal Saatchi ,

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Validation, Writing – original draft, Writing – review & editing

    Affiliation Imperial College London, London, United Kingdom

  • Francesca Pallotti,

    Roles Conceptualization, Formal analysis, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing

    Affiliation Department of Business, Operations and Strategy, University of Greenwich, London, United Kingdom

  • Paul Sullivan

    Roles Conceptualization, Formal analysis, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing

    Affiliations NIHR ARC Northwest London, Imperial College London, London, United Kingdom, University Sussex Hospitals NHS Foundation Trust, Sussex, United Kingdom



The growing interest in networks of interactions is sustained by the conviction that they can be leveraged to improve the quality and efficiency of healthcare delivery systems. Evidence in support of this conviction, however, is mostly based on descriptive studies. Systematic evaluation of the outcomes of network interventions in healthcare settings is still wanting. Despite the proliferation of studies based on Social Network Analysis (SNA) tools and techniques, we still know little about how intervention programs aimed at altering existing patterns of social interaction among healthcare providers affect the quality of service delivery. We update and extend prior reviews by providing a comprehensive assessment of available evidence.

Methods and findings

We searched eight databases to identify papers using SNA in healthcare settings published between 1st January 2010 and 1st May 2022. We followed Chambers et al.’s (2012) approach, using a Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. We distinguished between studies relying on SNA as part of an intervention program, and studies using SNA for descriptive purposes only. We further distinguished studies recommending a possible SNA-based intervention. We restricted our focus on SNA performed on networks among healthcare professionals (e.g., doctors, nurses, etc.) in any healthcare setting (e.g., hospitals, primary care, etc.). Our final review included 102 papers. The majority of the papers used SNA for descriptive purposes only. Only four studies adopted SNA as an intervention tool, and measured outcome variables.


We found little evidence for SNA-based intervention programs in healthcare settings. We discuss the reasons and challenges, and identify the main component elements of a network intervention plan. Future research should seek to evaluate the long-term role of SNA in changing practices, policies and behaviors, and provide evidence of how these changes affect patients and the quality of service delivery.


It is widely recognized that there is a gap between best achievable healthcare outcomes and those that are actually delivered, even in the best funded systems, suggesting that more is required than simply increasing available resources [1, 2]. Improving healthcare outcomes requires changes in frontline clinical practice, which in turn involves the ability to disseminate information across diverse teams, and to engender alignment of multiple groups.

The diffusion of practices and behaviors within any healthcare setting may be usefully framed as a network problem involving multiple individuals and the way they relate and interact with one another. Leaders aiming to improve healthcare outcomes would benefit from understanding how team members interact, and how interactions may be leveraged to optimize the adoption and diffusion of new practices. Information about patterns of interaction can be obtained using Social Network Analysis (SNA). SNA provides a set of tools and techniques used to investigate structural characteristics of networks [3], and understand how a broad range of behaviors may be triggered by social interaction [4]. SNA generates three main types of outputs. The first is a visual representation of networks structures, or network graphs. The second is a set of metrics providing quantitative information on properties of networks, such as density, or properties of individuals, such as centrality. The third type of output is produced by statistical models for network data, such as models for the analysis of longitudinal networks [5].

SNA outputs can be used to inform the design, implementation and monitoring of behavioral change programs, policies and practices [4, 6]. A network intervention can be defined as a structured process using social networks to accelerate behavior change or improve organizational performance [7]. Social networks are channels for information diffusion and interpersonal influence. Hence, changing the wiring of an existing social network may determine changes in how behaviors, ideas and practices spread in a social group.

Valente [7] proposed a taxonomy of four types of network intervention strategies: i) ‘Individuals’, based upon the identification of individuals with certain network characteristics who are recruited to act as change proponents; ii) ‘Segmentation’, involving the identification of subgroups in a network on which to focus behavioral change; iii) ‘Alteration’, whereby an existing network is changed by adding or removing ties or nodes in order to alter patterns of interaction and diffusion, and finally iv) ‘Induction’, whereby peer-to-peer interactions are encouraged through, for example, the use of meetings or training events bringing previously unconnected people together.

While a large body of research is available that relies on SNA to examine networks of health professionals in healthcare settings, much of this research has been descriptive, with limited reporting of the relationship between network interventions and clinical or organizational outcomes. This is confirmed by recent systematic reviews. For example, Chambers et al.’s [8] systematic scoping review of SNA-based studies in healthcare settings found very little evidence of the use of SNA as part of an intervention. Cunningham et al.’s [9] review (1995–2009) included 40 eligible studies. Only one described an SNA-based intervention using survey data to identify opinion leaders, but did not measure its impact. Bae et al.’s [10] systematic review included 28 eligible studies (up to 2013), none of which reported on outcomes of SNA-based interventions. A recent umbrella review by Hu et al. [11] included 13 reviews between 2010 and 2019 and demonstrated a wide applicability of SNA to study health professional networks. Of the 330 papers included in the reviews, only one reported on a network intervention.

The aim of the present review is threefold. First, provide an update of prior reviews by searching for papers using SNA to investigate networks of healthcare professionals in healthcare settings. Second, identify research reporting about network-based interventions and their outcomes. Third, identify the component elements and discuss the main challenges of a network intervention strategy to call attention on its potential in healthcare settings. The primary research question that this review seeks to address is what evidence is available on the adoption of network interventions and evaluation of their effect on care processes and outcomes.



The literature review was undertaken in accordance with the protocol (S1 File) followed by Chambers’ et al. in their 2012 review [8]. We used the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) statement and guidelines (S2 File) [12].

Information sources and search strategy

The literature search focused on identifying studies performing SNA on networks of healthcare professionals in healthcare settings. We used the same search strategy, inclusion and exclusion criteria and keywords as those used by Chambers et al. [8]. We performed a systematic electronic database search of OVID MEDLINE (R) ALL first, using free text terms, synonyms and subject headings associated with social networks and the methods used to investigate them including ‘sociometrics’, ‘sociograms’ and ‘sociomaps’. We also used words associated with SNA software, such as NetDraw and UCINET. Finally, the search strategy included the subject headings inter-professional relations, inter-disciplinary communication and physician-nurse relationships. The search strategy was later adapted for other databases in our search. Specifically, for the period 1st January 2010 to 1st May 2022, we searched the following databases: OVID MEDLINE (R) ALL, EMBASE Classic+EMBASE, APA PsycINFO, Health Management Information Consortium (HMIC), the Cochrane Library (Cochrane Database of Systematic Reviews, Cochrane Protocols and Cochrane Central Register of Controlled Trials), CINAHL Plus, Business Source Ultimate, Social Science Citation Index (SSCI) and Conference Proceedings Citation Index—Social Science & Humanities (CPCI-SSH) databases. Reference lists of relevant reviews and studies were searched, as was the website of the International Network for Social Network analysis ( and its linked sites. The index of contents of the Social Networks journal was also searched. The online search was run on 5th January 2021 and later updated on 1st May 2022 to include papers published up to this date. The search strategy had no study design filters or restrictions to language as long as the paper could be found in English. Records were managed within a Mendeley library.

Eligibility criteria.

The review included studies undertaken in any healthcare setting that reported the results of an SNA performed on networks among healthcare professionals (e.g., doctors, nurses, etc.) and other individuals involved in their professional networks (e.g., management, administrative support etc.). Examples of these networks include discussion networks, advice and knowledge sharing, and working on projects together. The healthcare setting was not restricted to a single geographical or organizational location, and could include wider interpersonal networks, such as the Parkinson network [13]. Veterinary or dental professionals were not included. Studies of networks linking organizations, rather than individuals, were excluded. We excluded studies where network relations were defined solely by patient sharing, as this predicts person-to-person communication only in minority of instances [14].

We built upon Chambers et al.’s [8] classification method. We divided papers into three groups, which we termed level 1 to 3. Level 1 included studies reporting on the impact of an SNA-based intervention. Level 2 included studies describing existing social networks among healthcare professionals without reporting any follow-up action. Level 3 included descriptive studies that went on to suggest an SNA-based intervention intended to affect outcomes and behaviors. We added this additional category to shed light on the significant number of papers acknowledging the value of using SNA to inform the design of intervention plans, and the benefits associated with it.

Study selection and data extraction.

Two Authors independently screened studies by title and disregarded those that they agreed to exclude. Studies where there was agreement for inclusion were independently screened by abstract by three Authors. Studies that appeared to meet the review inclusion criteria were forwarded to full-text evaluation and data extraction. The Cochrane EPOC (Effective Practice and Organisation of Care) Group criteria were used to assess the risk of bias by two Authors. Disagreements were discussed with a third Author.


The search returned 31,2867 unique papers, of which 102 met the eligibility criteria. Ten of these [1524] were also included in Chambers et al.’ s [8] review due to a crossover of search periods. We excluded these papers. The PRISMA diagram in Fig 1 below outlines the study selection process, and S1 Table outlines the number of records identified by database with a comparison to Chambers et al.’s [8] review. The comparison seems to suggest an increased use of social network approaches in healthcare studies over the past few years.

Four included studies met the level-1 [13, 2527], 74 the level-2 [1517, 1921, 23, 2894], and 24 the level-3 [18, 22, 24, 95115] criteria.

Of the 102 papers, one third (n = 33) was conducted in the USA, 22 in Europe (excluding UK), 16 in low- and middle-income countries (LMIC), 11 in Australia, eight in the UK, seven in Canada, two in Japan, two in China and one in Malaysia. The Netherlands and Italy produced the largest number of papers in Europe. Compared to previous reviews mentioned earlier, we found an increased number of studies conducted in LMIC. The largest number of studies (n = 59) had participants from multidisciplinary teams, and were conducted in secondary care settings (n = 64). The number of participants ranged from 10 [71] to 16,171 [66]. The largest number of studies used surveys/questionnaires (n = 57), followed by direct observations (n = 7), mixed methods (n = 13), process logs or other administrative data (n = 9), interviews (n = 7), online platforms or forums (n = 5), and interaction data collected through sensors (n = 4).

We summarized the types of ties examined in the included papers into 10 categories to standardize the language (see S2 Table). We also grouped network measures into 36 categories (see S3 Table). These measures were used across studies to describe or analyze networks at the individual, dyadic, group, and whole network levels. We also created a distinct category for those papers performing only statistical analysis of network data, such as Exponential Random Graph Models (ERGMs), Multiple Quadratic Assignment Procedure (MQAP), and Stochastic Actor Oriented Models (SAOMs). Network visualization was included as a distinct category when it was the only social network method used.

Level-1 studies

Table 1 below includes the level-1 studies, followed by a descriptive summary.

The four level-1 studies report on the results of SNA as part of an intervention, which we classified according to Valente [7]. Benton et al. [26] employed ‘alteration’ and ‘induction’ strategies by using shared project work to form new connections and increase interactions among network members. van der Eijk et al. [13] employed ‘induction’ through training events. The remaining two studies [25, 27] focused on ‘individuals’, by using social network methods to identify individuals who would act as champions. The impacts reported in the papers included structural network changes as well as changes in working practices and, in one study, staff safety outcomes. None of the studies reported on the impact on patient outcomes. The overall aim of the reported interventions was to improve organizational performance [26], patient care across the Parkinson’s network [13], safe patient manual handling [25], and hand hygiene [27]. All four papers used the information from SNA to improve connectedness within the networks. A summary of the level-1 studies is provided in turn below.

Benton et al.’s [26] research was set in the National Health Service, Scotland. This was a quasi-experimental, pre-post intervention design. Analysis of the communication network of a group of nurse leaders was performed. Forty-six nurse participants from the acute and community setting participated to a baseline survey, which identified 18 participants for the intervention. Participants were selected because SNA data showed they were relatively weakly connected within the network. They were placed into one of three working groups based on their area of expressed expertise or interest. The aim was to influence the existing communication network by encouraging less connected participants to work together. To facilitate this, SNA data from the initial survey was fed back to all participants. The communication network was measured six months after the first data collection. Following involvement in the working groups, the selected 18 individuals showed substantial increase in number of ties. This was evidenced by a rise in connectedness score, which improved from 15.72 to 33.9, and closeness centrality which improved from 8.76 to 13.17. There were also improvement in global network efficiency and density, while the average path length reduced from 1.58 to 1.48. Network visualization showed more connections between professional groups. The Authors suggested that the wider network effects may have been affected by the feedback of the results of the first survey, which made people aware of their own position, and prompted curiosity about how they could change it. It also made people aware of the expertise available in peers. One weakness of the paper is that increase in connectedness among the 18 project participants was based on a survey done six months after the completion of the project groups. Hence, it is unclear whether the impact on network topology would be continued long term.

van der Eijk et al. [13] conducted a parallel group, mixed-methods study in the Netherlands. The study aimed to evaluate the Parkinson network, a nationwide organization with regional networks of health professionals. The study involved 101 multidisciplinary healthcare workers involved with Parkinson’s care. Participants, who were based in hospital, nursing home or primary care settings, were selected to take part in a program on the basis of their location and ‘motivation’ (the latter term is not explicitly defined in the paper). They underwent a training course on multidisciplinary aspects of Parkinson’s disease, and were given access to a database of expert therapists in their geographical location. There were also semi-annual meetings and an annual conference. Participants completed a survey on network connections and perceived team performance at baseline. One year later, a subsample was interviewed. There was a substantial increase in the number of ‘knowing each other’ connections from 1,431 to 2,175 (p < 0.001) and in ‘professional contact’ connections from 664 to 891 (p < 0.001). Neurologists and nurse specialists had a central position and were very well connected one year after the program implementation. Overall team performance did not change, but satisfaction with multidisciplinary collaboration increased significantly. There were no data on the impact of network characteristics on either patient outcome measures–such as symptom control or patient satisfaction, or process measures–such as rate of provision of evidence-based elements of care.

Hurtado et al. [25] used social network survey data to identify highly influential co-workers who were recruited as local champions in a safe patient handling education program. The Authors reported that previous studies in this context showed variable short- and long-term impact and that this may be due to a lack of proper methods for selecting workers best suited to exert influence. The study was carried out in critical care areas in one US hospital, and used a survey to collect data on advice seeking for safe patient handling. Individuals showing high centrality in the network were chosen as champions and were trained in safe handling. They were identified to other staff through announcements and wearing of ribbons. The results showed an increase in safety incident reporting, correct equipment use and safety compliance, as well as reduction in staff injuries. Individual injury profile was significantly different from that of the two control hospitals in the same system.

Lee et al. [27] performed a parallel group study comparing two strategies to influence a behavior, hand hygiene compliance, through the use of local champions. The strategies were deployed on two similar medical wards. SNA showed there were few ties between the wards, suggesting that cross contamination was unlikely to occur. Staff on both wards were asked to nominate and rank peers in terms of their suitability to be hand hygiene champions. In one study arm, champions were selected on this basis. In the other study arm, managers selected champions without reference to the peer ranking. The champions themselves did not know how they had been selected. Trained observers used a validated approach to measure hand hygiene compliance during the study. Compliance increased substantially, from 48% to 66% in the peer selected champion arm, and from 50% to 65% in the manager selected champion arm. There was no statistical difference between the groups.

Level-2 studies

Table 2 below includes the level-2 studies, followed by a descriptive summary.

Seventy-four studies were classified as Level-2. These are studies using SNA solely for descriptive or analytic purposes, without discussing about possible interventions aimed at changing or improving the structure or functioning of the networks. Twenty-three studies were from the USA, 14 from Europe (excluding UK), 14 from LMIC, seven from UK, nine from Australia, five from Canada and two from Japan. Forty-five studies used teams or mixed groups of healthcare professions as participants, 15 papers featured doctors only, 10 papers involved nurses, one study radiologists, one study psychologists, one also involved patients, and one had other types of healthcare professionals.

The majority of the studies (n = 46) were set in secondary care settings, followed by community (n = 9) and primary care settings (n = 5). Eight studies were conducted in mixed secondary and community, and primary and secondary settings. Finally, three studies were set in virtual settings, one in a university hospital, one in a cross sector and one in a nursing home. Twenty-six papers relied on surveys to collect network data, 17 used questionnaires, 10 used logs or administrative data, seven were based on mixed methods, six on observation, four on interviews, two on online platforms or forums, and two on interaction data from sensors.

Ten different types of ties were examined, the commonest being information and knowledge exchange. Nine papers described more than one tie [15, 34, 36, 44, 60, 64, 72, 75, 92]. Twenty-nine different network measures were used to describe the networks at the individual, dyadic, group and whole network levels. Statistical analysis was performed as the only analytical method in 10 studies. Burt et al. [42] is a theoretical paper suggesting different types of questions for name generators. Forty papers (60%) were published between 2010 and 2015, and thirty-four (40%) between 2016 to 1st May 2022.

Level-3 studies

Table 3 below includes the level-3 studies, followed by a descriptive summary.

Twenty-four studies were classified as Level-3. Nine were conducted in the USA, seven in Europe (excluding UK), two in Australia, two in LMIC, two in Canada and two in China. Twelve used teams or mixed groups of healthcare professionals as participants, nine studies used doctors, two had other health professionals and one used healthcare providers and patients. The majority of the studies (n = 17) collected data in a secondary care setting, four in the community, one in primary care, one in primary and secondary care, and one in public health.

Ten studies used questionnaires to collect data, three relied on mixed methods, four used surveys, three interviews, two collected interaction data from sensors, one used direct observation, and one an online platform or forum. Seven different types of ties were analyzed across studies. Two studies analyzed more than one tie [108, 113, 114]. Twelve different network measures were used to describe or analyze networks at the individual, dyadic, group and whole network levels. Statistical analysis relying on ERGMs and MRQAP were used five times.

The four types of network interventions were mentioned as recommended strategies to be designed and implemented in order to improve the overall structure and functioning of the networks. Nine studies recommended to use ’individuals’ [18, 22, 100, 102, 103, 106, 109, 114, 115], eight studies recommended ‘induction’ [24, 95, 97, 100, 106108, 111], seven studies discussed possible ’alteration’ strategies [96, 98, 104, 105, 110, 113, 114], and four recommended ’segmentation’ [97, 99, 101, 112]. Four studies recommended more than one strategy [97, 100, 106, 114]. Thirteen papers were published between 2010 to 2015, and 11 between 2016 to 1st May 2022.


We updated previous reviews by including papers published since 2010 that have used SNA to investigate networks among healthcare professionals. Our search strategy included a wide range of databases and placed no restrictions on professional groups, healthcare setting, country, or study design. We found 102 papers that used SNA to examine networks of healthcare professionals. We confirmed the findings of prior systematic reviews: The majority of published studies were descriptive, with only four papers discussing the outcomes of an SNA-based intervention. We defined network intervention as a set of actions aimed at modifying the main elements of a network system (i.e., nodes and relations) so as to generate behavior change and improve system performance. The main idea behind network intervention is that if networks affect outcomes of interest, change in network structure could lead to change in relevant outcomes.

A possible explanation for the limited number of studies on network interventions concerns the practical difficulties in designing and implementing network-based interventions in general, and in healthcare contexts more specifically. Valente et al. [4] discuss the main challenges associated with network interventions in the domains of public health and medicine. In what follows, we will briefly describe the main challenges that we believe arise when an intervention is designed and implemented within an organizational context, such as a hospital or other healthcare organizations. Healthcare organizations present additional challenges over and above those identified by Valente et al. [4] for the public health domain. We organize our discussion by using the four-stage model of program implementation suggested by Valente et al. [4].


The first stage involves the assessment of a community in terms of needs, vision and opportunity for change [4]. In practice, this implies identifying: (i) a well-defined network (i.e., community boundaries); (ii) the relations among community members (i.e., social capital); (iii) the specific interests of various stakeholders, and (iv) the behavior under investigation. A number of specific challenges may arise at this stage when social network research is conducted within organizations [116]. First, network identification. This may be facilitated by the natural boundaries that organizations provide for the network of interest. Problems typically arise in collecting the non-anonymous data needed for network research. The management of the organization (which is often also the commissioner of the research) may provide partial commitment or discontinued support to the research, or even restricted access to data. Access to network and other types of data may also be problematic due to the specific nature of the population under investigation. Intervention programs within healthcare organizations are likely to involve multiple professional groups (e.g., hospital administrators, medical doctors, nurses, etc.) whose interdependencies may be difficult to manage or predict thoroughly ex ante. The actual use of output data from hospital administrators, participants’ protection of ethical rights, as well as the existence of ethical codes for professionals are all factors that may make data collection within healthcare organizations particularly challenging [117]. A solution to this problem may be a clear identification and communication of the goals and objectives of the research. The four studies that we identified as reporting the results of a network intervention (level-1), or those recommending a follow-up intervention in their conclusion section (level-2), mainly focused on improving specific structural features of the networks. Of the four level-1 studies, only two measured the impact of network intervention on health-related outcomes [25, 27]. The reason for this may lie in the difficulty of envisioning clear-cut causal links between behaviors at one level (e.g., health professionals) and outcomes at another level (e.g., patients). More direct evidence of measurable outcomes of network interventions at the patient or organizational level is needed. Finally, ethical challenges should also be considered at this stage. Cronin et al. [118] and Borgatti and Molina [119] offer explicit guidance on how to deal with specific ethical issues such as protecting anonymity, presenting output data in aggregated form, and offering participants multiple opportunities for opting-out.


The second stage involves the creation and adoption of an intervention program to address a behavioral problem [4]. The use of network analysis is particularly helpful at this stage, as it provides valuable information that can be used to tailor an intervention to the specific needs of the population under investigation. High response rates and lack of missing data are crucial as they allow to design an intervention based on more complete information. The identification of opinion leaders within a network who may act as change agents has been used in a large number of studies. Also, network analysis may be useful at this stage to identify other roles or positions, cohesive subgroups, or important cleavages within a network structure. Within an organizational setting, the existence of a formal reporting structure is particularly relevant in that it provides additional information on power structures and formal roles that can also be leveraged in a network-based intervention.


The third stage involves implementing the program with adherence and competence [4]. Within healthcare organizations, pressures to improve outcomes (e.g., clinical, operational, financial and managerial) are frequently generated by policy changes that produce top-down initiatives proposed by senior management and implemented through the involvement of various organizational change agents such as medical doctors, hospital administrators and, occasionally, technical and support staff. Research has recognized that the success of change initiatives hinges on the ability of change agents to overcome potential resistance from other organizational members, and encourage them to adopt or develop new practices [120]. In professional organizations, such as healthcare organizations, the coexistence of many professional groups with strong identity and role boundaries may represent the biggest obstacle to organizational change. Furthermore, not all change initiatives are equivalent, and recent research has pointed to the need of establishing the extent to which a change initiative diverges from the institutional status quo in order to better identify factors enabling adoption [120]. Other than resistance to, and extent of, change, challenges that may arise at this stage include availability of resources needed to implement a change program, lack of evidence of successful research designs to use in non-experimental, organizational settings, and lack of clarity about outcome variables to be monitored during the implementation stage.


The fourth, and last stage involves checking that the program continues to be implemented as intended over time, and is continuing to exert the anticipated effects [4]. The main challenge at this stage concerns the slow-moving nature of network and organizational variables, compounded by the often-far too high turnover rates within organizational units. This could make particularly difficult predicting with a reasonable level of certainty how long a social structure would take to affect a behavior, or an outcome of interest. As this usually takes time, problems may arise that are related to changes in the composition of a network structure, which should ideally remain unchanged for the duration of an intervention program. In non-experimental, naturalistic settings this is unlikely to occur. Research has also shown that changes in the composition of a network structure led to changes in the attitudes and behaviors of those who remain in the organization [121].

We have not offered specific solutions to the various issues highlighted above. Rather, our aim was to shed light on the main challenges of implementing a change initiative within an organizational setting. A possible solution to some of the challenges associated with implementing an intervention and measuring its effects over time is the adoption of a simulation-based analytic approach. This approach involves data collected on an existing network to simulate a number of alternative scenarios resulting from altering specific characteristics of the nodes and ties within a network. An example of application of a simulation-based approach to a longitudinal network dataset can be found in Schaefer et al. [122]. The authors use the results of Stochastic Actor-Oriented Models to simulate the coevolution of friendship ties and smoking behavior under potential intervention scenarios. Currently available statistical models for network data have the advantage of being particularly well-suited for simulation analyses. This is an approach that we believe may provide realistic and interpretable evidence of the possible outcomes of a change initiative, and may justify the long-term resource commitment that network-based interventions usually require.

While a number of studies are available that describe network structure, it is important to consider that research informing on how to make positive changes in networks is likely to be closer to having practical impact. There is an urgent need for more research into which healthcare network interventions work in different contexts and how they can be best designed and employed. Similarly pressing is a need for further work to identify experimental design options that are more effective at identifying and maximizing control over relevant variables and outcomes, and that are more efficient in terms of time and resource needed. We may conclude that this is an important opportunity for the field to coalesce on terminology, measures, and applications, after establishing priority areas for researchers in how to do so to advance work on the application of SNA to the design, dissemination, implementation and sustainability of behavior change interventions.


We used a comprehensive broad approach to searching but may have missed some research results such as unpublished conference proceedings, papers not available in English language, negative findings or studies that did not complete and were not submitted, and grey literature.


Studies of network intervention remain scant and devoid of implications for the impact of intervention initiatives on patient care. There is a need for evidence on which kinds of network interventions work, in which contexts, and under what conditions—or for whom. It is possible to measure the effect of an intervention on network effectiveness, for example, by measuring the number of new links or increased volume of communication. However implicitly, this approach assumes a causal link between inter-professional communication and patient benefits. The complexity of healthcare, and the ubiquitous nature of barriers to best practice, implies that this is often a conjecture too far, and a more direct evidence of patient benefit should be preferred. The most important test of the effectiveness of network intervention would be assessing its impact on patient level outcomes, or, when this is difficult to determine, on the delivery of processes of care that are supported by good evidence.


Disclaimer. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.


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