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 middle- and 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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5651641PMC
http://dx.doi.org/10.1186/s13643-017-0597-1DOI Listing

Publication Analysis

Top Keywords

social network
16
network analysis
16
performance healthcare
12
network
12
network methods
12
professional communication
12
articles meeting
12
methods study
8
study professional
8
healthcare providers
8

Similar Publications

Continued Implementation and Use of a Digital Informal Care Support Platform Before and After COVID-19: Multimethod Study.

JMIR Form Res

December 2024

Faculty of Behavioural, Management and Social Sciences, University of Twente, Drienerlolaan 5, Enschede, 7522 NB, Netherlands, 31 053 489 9111.

Background: With the growing need of support for informal caregivers (ICs) and care recipients (CRs) during COVID-19, the uptake of digital care collaboration platforms such as Caren increased. Caren is a platform designed to (1) improve communication and coordination between ICs and health care professionals, (2) provide a better overview of the care process, and (3) enhance safe information sharing within the care network. Insights on the impact of COVID-19 on the implementation and use of informal care platforms such as Caren are still lacking.

View Article and Find Full Text PDF

Despite the extensive use of network autocorrelation models in social network analysis, network autocorrelation models for binary dependent variables have received surprisingly scant attention. In this paper, we develop four network autocorrelation models for a binary random variable defined by whether the peer effect (also termed social influence or contagion) acts on latent continuous outcomes leading to an indirect effect under a normal or a logistic distribution or on the probability of the observed outcome itself under a probit or a logit link function defining a direct effect to account for interdependence between outcomes. For all models, we use a Bayesian approach for model estimation under a uniform prior on a transformed peer effect parameter ( ) designed to enhance model computation and compare results to those under the uniform prior for .

View Article and Find Full Text PDF

To prevent widespread epidemics such as influenza or measles, it is crucial to reach a broad acceptance of vaccinations while addressing vaccine hesitancy and refusal. To gain a deeper understanding of Japan's sharp increase in COVID-19 vaccination coverage, we performed an analysis on the posts of Twitter users to investigate the formation of users' stances toward COVID-19 vaccines and information-sharing actions through the formation. We constructed a dataset of all Japanese posts mentioning vaccines for five months since the beginning of the vaccination campaign in Japan and carried out a stance detection task for all the users who wrote the posts by training an original deep neural network.

View Article and Find Full Text PDF

World population is experiencing huge death toll due to Covid-19 pandemic with panic and uncertainty. Students used the electronic media called e-learning, became the budging word in these days. This study was aimed to determine the experiences of the medical students regarding e-learning during Covid-19 pandemic.

View Article and Find Full Text PDF

In Southern U.S. states with high HIV incidence and low HIV Pre-Exposure Prophylaxis (PrEP) uptake, enhanced efforts to increase interest in and willingness to use PrEP are needed.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!