Exploring the use of social network analysis methods in process improvement within healthcare organizations: a scoping review

Abstract

Background

Communication breakdowns among healthcare providers have been identified as a significant cause of preventable adverse events, including harm to patients. A large proportion of studies investigating communication in healthcare organizations lack the necessary understanding of social networks to make meaningful improvements. Process Improvement in healthcare (systematic approach of identifying, analyzing, and enhancing workflows) is needed to improve quality and patient safety. This review aimed to characterize the use of SNA methods in Process Improvement within healthcare organizations.

Methods

Relevant studies were identified through a systematic search of seven databases from inception - October 2022. No limits were placed on study design or language. The reviewers independently charted data from eligible full-text studies using a standardized data abstraction form and resolved discrepancies by consensus. The abstracted information was synthesized quantitatively and narratively.

Results

Upon full-text review, 38 unique articles were included. Most studies were published between 2015 and 2021 (26, 68%). Studies focused primarily on physicians and nursing staff. The majority of identified studies were descriptive and cross-sectional, with 5 studies using longitudinal experimental study designs. SNA studies in healthcare focusing on process improvement spanned three themes: Organizational structure (e.g., hierarchical structures, professional boundaries, geographical dispersion, technology limitations that impact communication and collaboration), team performance (e.g., communication patterns and information flow among providers., and influential actors (e.g., key individuals or roles within healthcare teams who serve as central connectors or influencers in communication and decision-making processes).

Conclusions

SNA methods can characterize Process Improvement through mapping, quantifying, and visualizing social relations, revealing inefficiencies, which can then be targeted to develop interventions to enhance communication, foster collaboration, and improve patient safety.

Introduction

Adverse events, including medical errors, diagnostic errors, and preventable complications, continue to affect millions of patients globally, leading to severe morbidity, mortality, and substantial avoidable healthcare costs [1, 2]. Among the many factors contributing to avoidable adverse events, breakdowns in communication have been identified as a leading cause [3,4,5]. Lapses in communication during care coordination and patient handoffs can lead to inadequate patient follow-up, delayed care, increased healthcare costs, and provider burnout, leading to an increased risk of adverse events [4, 6].

Many studies have highlighted that investigating the underlying causes and consequences of poor communication is necessary to improve the delivery of high-quality care [3, 4, 6, 7]. However, a large proportion of studies investigating communication in healthcare organizations lack the necessary understanding of social structures (interconnected relationships of social groups e.g., who speaks to who, for what purpose, using what mechanism) and coordination structures (e.g., how information gets transferred or transitioned between people or services) to make meaningful improvements and reduce adverse events [8, 9]. For example, the surgical safety checklist (SSC) is a tool meant to enhance patient safety by coordinating care delivery and improving inter-professional communication [10]. Yet, many studies report conflicting results on the impact of the SSC due to a lack of mutual understanding of communication among team members (e.g., who is responsible for leading a specific checklist pause point) and coordination (e.g., what team members should be present during specific pause points) structures (11,12,13). Effective communication among healthcare providers is challenging due to the complex nature of tasks performed and the numerous healthcare providers embedded within hierarchical structures. While the effective use of Process Improvement or Quality Improvement (QI; framework to systematically improve processes and systems in healthcare) interventions rely on understanding the social interactions and relationships within organizations, little attention has been paid to how social networks can be used to improve the effectiveness of communication and coordination in healthcare.

A social network is a set of social entities, actors or nodes (individuals, groups, organizations) connected by similarities, social relations, interactions, or flows (information) [14]. Analyzing professional communication structures (e.g., observed formal advice-seeking or giving related to work situations) within healthcare organizations’ social networks is important in understanding how best to inform interventions by identifying which network structures promote or inhibit behavior change [15]. The use of social network analysis (SNA) can provide insight into the social relationships, interactions, and tasks involved within sociotechnical systems. SNA metrics are quantitative measures used to analyze the structure, relationships, and dynamics within social networks through quantifying network behavior [16]. Network metrics reflect centrality, which refers to a family of measures where each represent different conceptualizations of nodal importance within a network, and cohesion measures, which examine the extent to which nodes within a network are connected [14, 17]. These metrics provide an understanding of the structure of social networks through identifying influential nodes, information flow, communities, and cliques [18]. SNA has been shown to improve professional communication and interprofessional relationships by revealing gaps in communication and identifying influential social entities and communication channels [14, 15, 19]. By indicating which social entities are effective in the flow of communication, organizations can leverage their skills to disseminate important information effectively and foster positive inter-professional relationships [19, 20]. Additionally, through identifying gaps in communication between different teams or departments organizations can work to prevent misunderstandings, adverse events, and the duplication of efforts resulting in a more collaborative work environment with stronger interprofessional relationships [14, 21]. Through understanding social networks, SNA can be effective in designing, implementing, and evaluating interventions needed to improve professional communication and coordination in healthcare [15, 22].

The aim of this review was to characterize the existing literature to assess SNA methods ability to identify, analyze, and improve processes (Process Improvement) related to patient care within healthcare organizations.

Methods

The scoping review was conducted using Arksey and O’Malley’s modified six-step framework [23, 24]. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) standards were used to guide the reporting of this review [25]. The PRISMA-ScR checklist is shown in the Appendix.

Information sources and search strategy

In collaboration with a research librarian (JB), relevant studies were identified through a systematic search of the MEDLINE (Ovid), Embase, Psychinfo, AMED (Allied and Complementary Medicine), CINAHL, Cochrane Library and Web of Science databases from inception – 16 October 2022. The database search was supplemented with hand searching of reference lists of included reviews. Grey literature was searched using Google Custom Search Engine strategies to narrow search results and allow for more targeted results [26, 27]. Searched websites included the International Network for Social Network Analysis, American Evaluation Association Social Network Analysis Technical Interest Group, and the International Sunbelt Social Networks Conference proceedings archives. The search strategy for the social network analysis concept was adapted from Sabot et al.’s systematic review of Social Network Analysis and healthcare settings [22]. Truncation search terms were used to search inclusive and key terms for these concepts can be found in the supplemental appendix.

Eligibility criteria

A screening checklist developed by Sabot et al., 2017 was modified to guide the review of this study [22, 28]. A “no” response to any of the study inclusion criteria (Appendix) was a reason for exclusion from the scoping review. “Healthcare providers” were classified as physicians, physician’s assistants, nurses, midwives, pharmacists, pharmacy technicians, clinical officers, counselors, allied health professionals, and other individuals involved in professional networks (e.g., administrative support staff, management). “Professional communication” was defined as observed formal professional advice-seeking or giving related to hypothetical or actual work situations or patients [22]. Healthcare organizations were defined as a building or mobile enclosure in which human medical, dental, psychiatric, nursing, obstetrical, or surgical care is provided. Healthcare organizations can include but are not limited to, hospitals, nursing homes, limited care facilities, medical and dental offices, and ambulatory care centers [29]. Studies had to report the use of SNA in the design of the study (e.g., social network mapping, evaluation of network properties or structure, or analysis of network actors) [22]. Additionally, to be included studies were required to use systematic data-guided activities (e.g., aims and measures) to achieve improvement or use an iterative development and testing process (i.e., Lean Management, Six Sigma, Plan-Do-Study-Act (PDSA) cycles, or Root Cause Analysis) [30, 31]. Studies where network relations were defined solely by patient sharing were excluded, as this only predicts person-to-person communication in a minority of instances [32]. Abstracts and conference proceedings were considered if details of their methodology and results were published. No limits were placed on study design, language, or publication period.

Study selection and screening process

Study selection and screening employed an iterative process involving searching the literature, refining the search strategy, and reviewing articles for study inclusion. The titles and abstracts of all identified references were independently examined for inclusion by three reviewers (T.F, M.D, and L.S) using the Covidence software platform for systematic reviews [33]. Full texts of potentially eligible studies were retrieved by the reviewers (T.F, M.D, and L.S), who determined study eligibility using a standardized inclusion screening checklist. Inter-rater reliability was assessed at each phase of the scoping review between reviewers and disagreements were resolved by consensus with input from a fourth author (L.J).

Charting the data

Data from eligible full-text studies was charted by the reviewers (T.F, M.D, L.S) independently using a standardized data abstraction form in Covidence to obtain key items of information from the primary research reports. Discrepancies among reviewers were resolved by consensus. The data abstraction form captured information on key study characteristics (e.g., author, year of publication, location of study, study design, aim of study, type of healthcare facility/provider), SNA-related information (e.g., SNA purpose, data collection methodology, software, SNA metrics) and reported on the implications of using SNA (e.g., social network mapping, assessment of network members or structures).

Collating, summarizing, and reporting the results

A narrative synthesis was performed to describe the study characteristics, SNA methodology, and SNA metrics. The stages of the narrative synthesis included: (1) developing the preliminary synthesis, (2) comparing themes within and between studies, and (3) thematic classification [34]. Detailed text data on SNA characteristics and implications were reviewed, re-categorized, and analyzed thematically. In line with our objectives, the thematic analysis focused on identifying SNA methods used to improve communication and coordination in healthcare organizations. To categorize the approaches, we conducted further distillation of overarching approaches. We took notes throughout the review and analysis stages, documenting emerging trends and ideas to facilitate further review and discussion among the review team. The extracted data was tabulated in descriptive formats and narrative summaries were provided.

Results

The literature search generated 5084 potentially eligible studies after deduplication, of which 4936 were excluded based on title and abstract, leaving 148 full-text articles to be reviewed. The PRISMA-ScR flow diagram outlining the breakdown of studies can be found in Fig. 1. Upon full-text review, 44 reports of 38 studies were included for data abstraction. Six studies [4, 35,36,37,38,39] had multiple records and were truncated into single studies.

Fig. 1

PRISMA-ScR flow diagram

Study characteristics

The characteristics of the included studies are shown in Table 1. Many studies were recently published between 2015 and 2021 (26, 68%) and were primarily located in the United States (26, 68%). 67% of studies occurred within a hospital (25, 66%) and most studies (15, 39%) were set in Internal medicine (gastroenterology, oncology, cardiology, nephrology, respirology, telemetry, or acute care). Studies employed multidisciplinary healthcare providers, however many studies focused on physicians (endocrinologists, oncologists, plastic surgeons, neurologists, anesthesiologists, intensivists, generalists; 27, 71%) and nursing staff (registered nurse, nurse practitioner, practical nurse; nursing assistants; 27, 71%). Most studies employed an observational study design, with 5 studies utilizing longitudinal quasi-experimental design [40,41,42,43,44]. Five studies used mixed-methods designs [35, 36, 45,46,47] with integrated qualitative and quantitative data, and a further 6 studies used multi-method designs [48,49,50,51,52,53] using a combination of independent qualitative and quantitative data. Twenty-four studies reported using quantitative data only [3, 4, 6, 40,41,42,43, 54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70] and the remaining 2 studies used qualitative methods [71, 72].

Table 1 Characteristics of included studies

Table 2 provides an overview of the aims and findings of the included studies and Table 3 outlines the use of SNA methodology and reflects the data collection methods, software, and SNA metrics included in each study. A wide range of network visualization software was used with studies giving preferences towards UCINET [36, 40, 48, 54, 57,58,59, 66,67,68, 70, 72, 73], Organization Risk Analyzer (ORA) [4, 55, 74, 75], and Open-Sourced R Software [42, 49, 53, 63, 65, 76]. Five out of the 38 studies did not visualize their networks through social network mapping and only provided a descriptive assessment of network structures or analysis of network members [3, 40, 57, 68, 76]. Two studies did not explicitly report SNA metrics [47, 61]. Table 4 provides a comprehensive breakdown of the SNA metrics selected in each study and their application to healthcare networks. There were many network metrics used throughout the studies, however, most studies primarily employed Degree Centrality, Betweenness Centrality, and Density. Twenty-six studies used Degree Centrality as a measure of reach and importance [3, 4, 6, 35, 36, 41, 43,44,45,46, 48, 49, 51, 54,55,56,57,58,59, 62,63,64,65, 67, 69, 70], 20 studies used Density to measure network cohesion [6, 35, 36, 41, 43,44,45, 48, 53,54,55, 57, 58, 62, 63, 69,70,71,72, 77], and 19 studies used Betweenness Centrality as a measure of influence and brokerage [3, 4, 36, 44,45,46, 49, 51, 52, 55,56,57, 59, 60, 62, 63, 65, 66, 69].

Table 2 Summary of included studies

Table 3 Summary of included study methodology

Table 4 Summary of studies’ utilizing social network analysis (SNA) metrics

^*Some articles were assigned to more than one category.

Listed in descending frequency, however “Other” is always at the bottom.

Application and findings of SNA

SNA has been used in healthcare to measure the number of connections (i.e., interactions, tasks), the centrality of providers (i.e., degree, betweenness, and closeness), and network cohesion (i.e., density, clustering). It has helped us to understand essential themes like organizational structure, team performance, and influential actors in healthcare.

a) Organizational Structure.

SNA has been used to better understand how organizational structures (e.g., management roles, groupings of tasks and employees) influence communication and coordination, thereby informing opportunities for improvement. Nine studies showed how SNA was used to redesign hospital organizational structures [35, 36, 41, 45, 46, 53, 66, 69, 72]. For example, Samarth et al. [69] applied SNA to improve the throughput of their surgical patients, which revealed a hierarchical network coordination structure in their post-anesthesia care unit (PACU) wherein the Charge Nurse channeled all communication downstream, thereby becoming a bottleneck resulting in patient delays. This led to a redesign of their organizational network to a more democratic structure where coordination was performed by an integrated information technology (IT) system which was available to all team members, reducing the dependence on the charge nurse [69]. Additionally, Alhaider et al. [52] demonstrated how SNA could be used to investigate system-wide communication in patient flow management and identify process improvement within the healthcare system. Applying SNA within the Distributed Situation Awareness (DSA) framework helped identify bottlenecks in patient flow and the roles that were most likely to experience communication or transaction overload while acquiring and disseminating situational awareness. The DSA model provided a characterization of patient flow and a blueprint for healthcare facilities to consider when modifying their organizational structure to improve communication and coordination. Spitzer-Shohat et al. [36] used SNA to understand how their organizational structure could help implement disparity reduction interventions to improve care. The SNA unveiled that their subregional management had a high degree of centrality (i.e., many connections), and as such, they were targeted to spread information about the interventions [36].

A specialized application of SNA involves identifying how IT can enhance or transform organizational communication and coordination. Three studies used SNA to understand how providers from different professions and units communicate across various modes (e.g., in-person, phone, electronic medical record) [4, 48, 69]. For example, SNA highlighted that IT could help improve communication efficiencies during in-person patient handoffs. More specifically, SNA showed that IT could support the redesign of the social network patterns by removing redundant communication exchanges and support emergent and non-linear information flow [4, 69]. Six studies used electronic health records (EHR) data to map the network structure of professionals involved in care to show that improving the design of IT can support communication leading to more frequent information sharing among professional groups [6, 47, 51, 56, 60, 63]. Nengliang et al. [56] demonstrated that EHR log data could be used within an SNA to map the network structure of all healthcare providers and examine the connectivity, centrality, and clustering of networks that emerged from interactions between providers who shared patients. In turn, this data revealed the dynamic nature of care teams and areas (inpatient and outpatient) for collaborative improvement [56]. Another study used SNA to help contrast low and high IT implementations; they found that the high IT sophistication care homes had more robust and integrated communication strategies requiring fewer face-to-face interactions between providers to verify orders or report patient status compared to the low IT sophistication nursing home [47].

b) Team Performance.

Sixteen studies used SNA to examine poor team communication and coordination by highlighting the inefficiencies in health networks [3, 36, 41, 43, 53,54,55, 57, 58, 61, 64, 65, 67, 68, 70, 71]. SNA identified that these inefficiencies stem from: teams being overburdened due to workload [54, 61], conflict between team roles [36], lack of leadership [43, 58], and fragmented interprofessional relationships [57, 65, 70]. For example, poor team performance in hospital emergency departments has resulted in congestion and increased length of stay with patients having prolonged discharges. SNA allowed for an exploration of the possible causes of inefficiencies resulting in access blocks and determined that the number of healthcare providers and interactions between them, and the centralization of providers within the network affected the performance and quality of emergency departments [54]. Grippa et al. [3] used SNA and determined that the most efficient and effective healthcare teams focused more inwardly (internal team operation) and were less connected to external members. Additionally, SNA highlighted that effective teams communicated using only one or two mediums (e.g., in-person, email, instant messaging media) instead of dispersing time on multiple media applications.

SNA has been used to diagnose possible reasons for team inefficiencies and to identify potential design solutions to improve team performance [3, 35, 42, 53, 64, 67, 68, 71]. A study used SNA to identify that some experienced staff (who frequently mentor other staff) may have too many connections (high degree of centrality), leading to interruptions or distractions and impacting performance and coordination [54]. However, a different study, identified that staff with a high degree of centrality have the benefit of improving team performance by leveraging their social networks to be change agents and lead others to replicate desired behaviors (e.g., when a provider may forget to implement a desired change but gets reminded by a team member) [62]. Lastly, analyzing network cohesion helped identify fragmentation and cliques in the network which may reflect a lack of collaboration and interprofessional relations. For instance, denser (more connections) communication networks with more clustering (groups of connections) are associated with more rapid diffusion of information. Additionally, the connections between providers in dense networks can provide social support (reinforcement) to team members that strengthen their commitment to follow desired behaviors and increase the likelihood that deviations from those actions will be noted by their peers [62].

c) Influential Actors.

SNA was used to identify influential actors who could act as brokers (an individual who occupies a specific structural position in systems of exchange) [3, 49, 64] who could become opinion leaders (an individual who holds significant influence over others’ attitudes/beliefs) [62], champions (an individual who actively supports innovation and its promotion/implementation) [40] or a change agent (an early adopter of an intervention who supports the dissemination of its use) [44] based off measures of social influence within a network. Studies showed that influential actors in social networks can inform behavioral interventions needed to improve professional communication or coordination [3, 40, 49, 62, 64]. For example, Meltzer et al. [62] used SNA to identify influential physicians to join a QI team and highlighted that having members with connections external to the team is most important when disseminating information, while within team relationships matter most when coordination, knowledge sharing, and within-group communication are most important. When creating an interdisciplinary team, betweenness centrality (node that frequently lies on the shortest path in a network) may be a useful network metric for prospectively identifying team members that may help to facilitate coordination within and across units / professional groups. Providers with a high betweenness have been found to be leaders and active participants in task-related groups [68]. Hurtado et al. [40] used SNA to identify and recruit champions who were used to deploy a QI intervention (safe patient handling education program) to advance safety in critical access hospitals. The champion-centered approach resulted in improved safety outcomes (increase in safety participation/compliance and decrease in patient-assist injuries) after one year. Additionally, Lee et al. [44] used SNA to assess the use of peer-identified and management-selected change agents on improving hand hygiene behavior in acute healthcare. No significant differences were reported between the two groups; however providers expressed a preference for hierarchical leadership styles highlighting the need to understand organizational culture before designing changes to the system.

Discussion

This scoping review presents a comprehensive overview of the existing literature looking at the use and impact of SNA methodology on Process Improvement within healthcare organizations. Our search strategy included a wide range of databases and placed no restrictions on study design, language, or publication period. When examining the expanding body of literature represented in our identified 38 studies, SNA methods were used to detect essential work processes in organizations, reveal bottlenecks in workflow, offer insight into resource allocation, evaluate team performance, identify influential providers, and monitor the effectiveness of process improvements over time. By analyzing the communication and relationships between management roles, employee groupings, and task allocation, SNA provides insights that can help identify areas for improvement related to patient throughput, diffusion of information, and the uptake of technology (e.g., IT systems). Studies highlighted that healthcare team performance can be hampered by inefficiencies related to being overburdened due to workload, conflicts between team roles, lack of leadership, and fragmented interprofessional relationships. To address these inefficiencies, SNA can leverage network outcomes related to connectedness (e.g., degree, betweenness, closeness) and use knowledge of the network structure (e.g., density, clustering coefficient, fragmentation) to create targeted interventions to mitigate these problems. Additionally, inefficiencies in social networks can be mitigated by identifying influential actors who serve as change agents and can be utilized as opinion leaders or champions to improve the efficiency of information exchange and the uptake of behavioral interventions.

Comparison With Past Literature (Study Design and Data Collection).

Our review stands out from previous studies due to its unique focus on the application of SNA methods in Process Improvement within healthcare organizations. Our primary objective was to investigate how healthcare organizations utilize SNA techniques to improve system-level coordination and enhance the overall quality of care provided to patients. In their research study, Sabot et al. [22] aimed to investigate the various SNA methods employed to examine professional communication and performance among healthcare professionals. Their study delved into the diverse range of SNA techniques used to gain insights into the complex network dynamics and interactions among providers. In more recent studies, Saatchi et al. [78] focused on exploring the adoption and implementation of network interventions in healthcare settings. This study provided insights into the effectiveness of network interventions (in which contexts they are successful and for whom), their potential benefits (increased volume of communication), and the challenges associated with their adoption in practice. Additionally, Rostami et al. [79] focused on advancing quantitative SNA techniques and investigated the application of community detection algorithms in healthcare. This study offers a comprehensive categorization of SNA community detection algorithms and explores potential approaches to overcome gaps and challenges in their use. Previous reviews primarily included observational and cross-sectional study designs with no comparator arms, which made determining the value of using SNA methods difficult as there was no comparison of social networks over time and no comparable head-to-head data. Our review identified 5 quasi-experimental studies [40,41,42,43,44] which used longitudinal or pre-post study designs. In each of these studies SNA was used to review a system which delivered clinical care to identify sources of variation and areas for process improvement at an individual and organizational level. The quasi-experimental studies were published within the last 5 years, indicating that SNA methodology is still in development and opportunities for experimental and longitudinal study designs are forthcoming. Using experimental and longitudinal SNA methods would enable causal inference of healthcare interventions or policies leading to improved generalizability of results.

When performing SNA there is a variety of qualitative (interviews, focus groups, observations) and quantitative (surveys, document artifacts, information systems) methods that researchers can use to map social networks, assess network structures, and analyze team actors. However, previous literature reviews have outlined an overreliance on descriptive SNA methods, which lack the contextual factors needed to interpret how a network reached a given structure. There has been a growing body of evidence advocating for the use of mixed-method social network data collection [80]. Our review has highlighted an increased uptake of mixed-method (integration of qualitative and quantitative methods and data) and multi-method (independent use of quantitative and qualitative methods) SNA study designs [81].

Knowledge Gaps and Future Research.

This scoping review highlights many practical uses of SNA; however, within most studies, little attention has been paid to leveraging SNA theory to help explain why networks have the structures they do [21]. For example, social boundaries between professional groups (e.g., Physicians, Nurses, Pharmacists) can inhibit the development of interprofessional networks though the creation of cliques leading to strong communication and coordination within groups, but fragmented communication across professional groups [21, 82, 83]. A potential explanation for the scarcity of studies assessing the reasons behind the structures of networks could be attributed to the primarily quantitative SNA methods used. Few studies used a qualitative or mixed-method design, indicating a limited understanding of the contextual factors associated with social networks. SNA can reveal the informal structures within organizations and underscores the importance of understanding that not all influential relationships between healthcare providers are found on formal organizational charts, and that informal networks can significantly influence communication and coordination [84]. The lack of robust study designs (mixed-method or multi-method) may also reflect the use of SNA by researchers more so as a technique than a methodology with theoretical underpinnings.

The value of using SNA to inform research and disseminate evidence-based interventions and policies has been discussed in the literature extensively. However, very few studies have used research on complex systems and network theory to examine how HCWs can act as change agents, interacting within and between hubs in organizations to disseminate knowledge [85]. Future research should apply complexity science to SNA to reconceptualize knowledge translation and think of the process as interdependent and relationship-centric to support sustainable translation [85]. Only a small group of included articles have highlighted how leveraging influential actors as change agents such as opinion leaders or champions can be advantageous in improving professional communication or coordination [3, 40, 44, 49, 62, 64]. This review identified two studies [40, 44] which utilized SNA and a champion-centered approach to support the successful implementation of a QI intervention resulting in improved safety outcomes. The use of champions is very prevalent in healthcare; however, success rates vary widely, likely due to the poor selection of champion candidates or organizational culture [40, 44]. In many cases healthcare workers selected to be champions are volunteered and do not hold enough social influence to change the behaviors of their colleagues. In the future SNA methods should be used to identify influential champions or opinion leaders embedded within their social networks who can influence knowledge transfer and facilitate coordination leading to process improvements.

Future research should identify how SNA methods can leverage health informatics and the large amounts of data stored within healthcare organizations. Even though past studies have used SNA to enhance organizational communication and coordination using IT [47, 56, 69], applying SNA to artificial intelligence and machine learning (ML) algorithms has not received much attention [86]. Integrating ML algorithms into community detection techniques has showcased the diverse ways SNA can be utilized in healthcare to monitor disease diagnosis, track outbreaks, and analyze HCW networks [79].

Limitations of the Review.

This review has some limitations that should be acknowledged. First, we excluded studies of provider friendship networks, which theoretically may have contained some professional communication. Secondly, we excluded studies where network relations were defined solely by patient sharing, as this has only been shown to predict person-to-person communication in a minority of instances. Lastly, studies were required to incorporate a Process Improvement component. Different terms were used to describe Process Improvement in the literature, making it challenging to devise a search strategy that would yield sufficient articles for review while also utilizing SNA methods. As a result, studies that utilized SNA methods but did not explicitly examine a process or system for delivering clinical care to identify sources of variation and areas for improvement were excluded.

Conclusion

SNA methods can be used to characterize Process Improvements through mapping, quantifying, and visualizing social relations revealing inefficiencies, which can then be targeted to develop interventions to enhance communication, foster collaboration, and improve patient safety. However, healthcare organizations still lack an understanding of the benefit of using SNA methods to reduce adverse events due to a lack of experimental studies. By emphasizing the importance of understanding professional communication and coordination within healthcare teams, units, and organizations, our review underscores the relationship between organizational structures and the potential of influential actors and emerging IT technologies to mitigate adverse events and improve patient safety.