scholarly journals Social Network Analysis of Patient Movement Across Health Care Entities in Orange County, Florida

2020 ◽  
Vol 135 (4) ◽  
pp. 452-460
Author(s):  
Danielle A. Rankin ◽  
Sarah D. Matthews
Keyword(s):  
Health Care ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Acute Care ◽  
Orange County ◽  
Health Care Systems ◽  
Acute Care Hospitals ◽  
Local Public Health ◽  
Care Systems

Objective Multidrug-resistant organisms (MDROs) are continually emerging and threatening health care systems. Little attention has been paid to the effect of patient transfers on MDRO dissemination among health care entities in health care systems. In this study, the Florida Department of Health in Orange County (DOH-Orange) developed a baseline social network analysis of patient movement across health care entities in Orange County, Florida, and regionally, within 6 surrounding counties in Central Florida. Materials and Methods DOH-Orange constructed 2 directed network sociograms—graphic visualizations that show the direction of relationships (ie, county and regional)—by using 2016 health insurance data from the Centers for Medicare & Medicaid Services, which include metrics that could be useful for local public health interventions, such as MDRO outbreaks. Results We found that both our county and regional networks were sparse and centralized. The county-level network showed that acute-care hospitals had the highest influence on controlling the flow of patients between health care entities that would otherwise not be connected. The regional-level network showed that post–acute-care hospitals and other facilities (behavioral hospitals and mental health/substance abuse facilities) served as the primary controls for flow of patients between health care entities. The most prominent health care entities in both networks were the same 2 acute-care hospitals. Practice Implications Social network analysis can help local public health officials respond to MDRO outbreak investigations by determining which health care facilities are the main contributors of dissemination of MDROs or are at high risk of receiving patients with MDROs. This information can help epidemiologists prioritize prevention efforts and develop county- or regional-specific interventions to control and halt MDRO transmission across a health care network.

scholarly journals Social Network Analysis across Healthcare Entities, Orange County, FL, 2016

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Danielle Rankin
Keyword(s):  
Public Health ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Disease Control ◽  
Acute Care ◽  
Orange County ◽  
Acute Care Hospitals ◽  
Patient Movement

Objective: To create a baseline social network analysis to assess connectivity of healthcare entities through patient movement in Orange County, Florida.Introduction: In the realm of public health, there has been an increasing trend in exploration of social network analyses (SNAs). SNAs are methodological and theoretical tools that describe the connections of people, partnerships, disease transmission, the interorganizational structure of health systems, the role of social support, and social capital1. The Florida Department of Health in Orange County (DOH-Orange) developed a reproducible baseline social network analysis of patient movement across healthcare entities to gain a county-wide perspective of all actors and influences in our healthcare system. The recognition of the role each healthcare entity contributes to Orange County, Florida can assist DOH-Orange in developing facility-specific implementations such as increased usage of personal protective equipment, environmental assessments, and enhanced surveillance.Methods: DOH-Orange received Centers for Medicare and Medicaid Services data from the Centers for Disease Control and Prevention Division of Health Care Quality Promotion. The dataset contains the frequency of patients transferred across Medicare accepting healthcare entities during 2016. We constructed a directional sociogram using R package statnet version 2016.9, built under R version 3.3.3. Node colors are categorized by the type of healthcare entity represented (e.g., long-term care facilities, acute care hospitals, post-acute care hospitals, and other) and depict the frequency of patients transferred with weighted edges. Node sizes are proportional to the log reduction of the total degree of patients transferred, and are arranged with the Fruchterman-Reingold layout. We calculated standard network indices to assess the magnitude of connectedness across healthcare entities in Orange County, Florida. Additionally, we calculated node-level indices to gain a perspective of the strength of each individual entity.Results: A total of 48 healthcare entities were included in the sociogram, with 44% representing Orange County, Florida. Although the majority of the healthcare entities are located in nearby counties, 90% of patient movement occurred across Orange County entities. The range of patient movement was 1 to 5196 with a median of 15 patients transferred in 2016. The network in Orange County is sparse with a density of 0.05, but the movement of patients across the healthcare entities is predominately symmetric (reciprocity=97%). The sociogram is centralized (degree centrality= 0.70) and contains a vast amount of entities that serve as connectors (betweenness centrality=0.53). The node-level indices identified our acute care hospitals and long term acute care hospitals are the connectors of our county health system.Conclusions: The SNA of patient movement across healthcare entities in Orange County, Florida provides public health with knowledge of the influences entities contribute to the county healthcare system. This will contribute to identifying changes in the network in future research on the transmission risks of specific diseases/conditions, which will enhance prioritization of targeted interventions within healthcare entities. In addition, SNAs can assist in targeting disease control efforts during outbreak investigations and support health communication. A SNA toolkit will be distributed to other local county health departments for reproduction to determine baseline data and integrate county-specific SNAs.

scholarly journals Application of Social Network Analysis to Health Care Sectors

2012 ◽  
Vol 18 (1) ◽  
pp. 44 ◽  
Author(s):  
Hae Lan Jang ◽  
Young Sung Lee ◽  
Ji-Young An
Keyword(s):  
Health Care ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis

Examining the Opportunities of Social Networking Adoption in the Health Care Systems

2015 ◽  
pp. 173-189
Author(s):  
Peldon
Keyword(s):  
Health Care ◽  
Social Network ◽  
Literature Review ◽  
Social Networking ◽  
Health Care Systems ◽  
Social Network Sites ◽  
Healthcare Systems ◽  
The Third ◽  
Healthcare Profession ◽  
Care Systems

Social Network Sites (SNSs) are known for providing the opportunity to quickly spread information faster than any other mode because of its ease of accessibility and ability to reach wider populations. The purpose of this chapter is to examine the opportunities of adopting Social Networking (SN) in the healthcare systems. Based on the current literature review, using a social network will enhance communication, collaboration, connection, coordination, and knowledge sharing. The healthcare profession of Bhutan undertook the survey for this study. Three new factors were generated from this study, namely 4Cs; it was found that the use of social networking enhances communication, coordination, collaboration, and connection with patients and among healthcare professionals. The second factor, Green and Sustainability, social networking enables the reduction of the carbon footprint, and the third factor is Exchange Knowledge via use of social networking.

scholarly journals Nature-Based Interventions for Mental Health Care: Social Network Analysis as a Tool to Map Social Farms and their Response to Social Inclusion and Community Engagement

2019 ◽  
Vol 16 (18) ◽  
pp. 3501 ◽  
Author(s):  
Marta Borgi ◽  
Mario Marcolin ◽  
Paolo Tomasin ◽  
Cinzia Correale ◽  
Aldina Venerosi ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Mental Health Care ◽  
Rural Areas ◽  
Social Inclusion ◽  
Local Communities ◽  
Social Farming

Social farming represents a hybrid governance model in which public bodies, local communities, and economic actors act together to promote health and social inclusion in rural areas. Although relational variables are crucial to foster social farm performance, the relational system in which farms are embedded has still not been fully described. Using social network analysis, here we map the nature of the links of a selected sample of social farms operating in Northern Italy. We also explore possible network variations following specific actions taken to potentiate local social farming initiatives. The results show a certain degree of variability in terms of the extension and features of the examined networks. Overall, the actions taken appear to be significant to enlarge and diversify farms’ networks. Social farming has the potential to provide important benefits to society and the environment and to contrast vulnerability in rural areas. Being able to create social and economic networks of local communities, social farming may also represent an innovative way to respond to the cultural shift from institutional psychiatry to community-based mental health care. This study emphasizes the critical role played by network facilitation in diversifying actors, promoting heterogeneous relationships, and, in turn, system complexity.

Health care provider social network analysis: A systematic review

2015 ◽  
Vol 63 (5) ◽  
pp. 566-584 ◽  
Author(s):  
Sung-Heui Bae ◽  
Alexander Nikolaev ◽  
Jin Young Seo ◽  
Jessica Castner
Keyword(s):  
Systematic Review ◽  
Health Care ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Health Care Provider ◽  
Care Provider

Team Communication in an Acute Care Unit: A Social Network Analysis

2019 ◽  
Vol 229 (4) ◽  
pp. S167
Author(s):  
Sarah Benammi ◽  
Naoufel Madani ◽  
Jihane Belayachi ◽  
Redouane Abouqal
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Acute Care ◽  
Team Communication ◽  
Acute Care Unit

scholarly journals The supervisory relationships of community health workers in primary health care: social network analysis of ward-based outreach teams in Ngaka Modiri Molema District, South Africa

2019 ◽  
Vol 4 (6) ◽  
pp. e001839
Author(s):  
Tumelo Assegaai ◽  
Helen Schneider
Keyword(s):  
Health Care ◽  
Primary Health Care ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Supportive Supervision ◽  
Team Leaders ◽  
Supervisory Relationships ◽  
Primary Health ◽  
Facility Staff

IntroductionSupportive supervision remains a key challenge to the sustainability of community health worker (CHW) programmes globally. The aim of the study was to identify critical actors and patterns of relationships in the supervision of ward-based outreach teams (WBOT) in a rural South African district.MethodsA cross-sectional study of social and professional relationships of WBOTs with other primary health care (PHC) system actors was conducted using a social network analysis (SNA) approach. A structured questionnaire was distributed to CHWs (37), WBOT team leaders (3), PHC facility managers (5) and PHC local area managers (2) (total n=47) assessing interaction patterns of supportive supervision, namely management, development and support.ResultsThe supportive supervision system pivoted around team leaders, who were nurse cadres and who ensured internal cohesion and support among WBOT members. The network patterns also showed the extent of peer support between CHWs in WBOTs. PHC facility staff and middle managers in the subdistrict did not appear to play active roles in the supervision of CHWs and their team leaders. However, there were exceptions, with WBOTs drawing on sympathetic cadres identified among the PHC facility staff for support.ConclusionSupportive supervision of CHWs can be thought of as a system of horizontal and vertical relationships that go beyond just one supervisor–supervisee interaction. In this study, supervisory relationships within teams functioned better than those between teams and the rest of the PHC system. Understanding these relationships is key to designing effective supportive supervision in CHW programmes. SNA can be a valuable approach in identifying the relationships to be strengthened.

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