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ISDS 2018 Conference Abstracts

Social Network Analysis across Healthcare Entities, 
Orange County, FL, 2016
Danielle Rankin*
Epidemiology, Florida Department of Health in Orange County, Orlando, FL, USA

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.

Keywords
sna; social network analysis; patient movement

Acknowledgments
The Florida Department of Health in Orange County would like to thank 
Sarah Matthews for assistance with obtaining the data to conduct the social 
network analysis. Additonally, we would like to express our gratitude 
to The Centers for Disease Control and Prevention (CDC) Division 
of Healthcare Quality Promotion for providing the dataset used in the 
analysis of this study. I would like to express my gratitude to Dr. German 
Gonzalez, Alvina Chu, and Karen Elliott from the Florida Department of 
Health for mentoring and guiding me through my fellowship.

References
1. Luke DA, Harris JK. Network Analysis in Public Health:

History, Methods, and Applications. Annual Review of
Public Health. 2007;28(1):69-93. doi:10.1146/annurev.
publhealth.28.021406.144132.

*Danielle Rankin
E-mail: danielle.rankin@flhealth.gov

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 10(1):e170, 2018