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Network analysis of team communication in a busy emergency department

BACKGROUND: The Emergency Department (ED) is consistently described as a high-risk environment for patients and clinicians that demands colleagues quickly work together as a cohesive group. Communication between nurses, physicians, and other ED clinicians is complex and difficult to track. A clear u...

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Autores principales: Patterson, P Daniel, Pfeiffer, Anthony J, Weaver, Matthew D, Krackhardt, David, Arnold, Robert M, Yealy, Donald M, Lave, Judith R
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3637459/
https://www.ncbi.nlm.nih.gov/pubmed/23521890
http://dx.doi.org/10.1186/1472-6963-13-109
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author Patterson, P Daniel
Pfeiffer, Anthony J
Weaver, Matthew D
Krackhardt, David
Arnold, Robert M
Yealy, Donald M
Lave, Judith R
author_facet Patterson, P Daniel
Pfeiffer, Anthony J
Weaver, Matthew D
Krackhardt, David
Arnold, Robert M
Yealy, Donald M
Lave, Judith R
author_sort Patterson, P Daniel
collection PubMed
description BACKGROUND: The Emergency Department (ED) is consistently described as a high-risk environment for patients and clinicians that demands colleagues quickly work together as a cohesive group. Communication between nurses, physicians, and other ED clinicians is complex and difficult to track. A clear understanding of communications in the ED is lacking, which has a potentially negative impact on the design and effectiveness of interventions to improve communications. We sought to use Social Network Analysis (SNA) to characterize communication between clinicians in the ED. METHODS: Over three-months, we surveyed to solicit the communication relationships between clinicians at one urban academic ED across all shifts. We abstracted survey responses into matrices, calculated three standard SNA measures (network density, network centralization, and in-degree centrality), and presented findings stratified by night/day shift and over time. RESULTS: We received surveys from 82% of eligible participants and identified wide variation in the magnitude of communication cohesion (density) and concentration of communication between clinicians (centralization) by day/night shift and over time. We also identified variation in in-degree centrality (a measure of power/influence) by day/night shift and over time. CONCLUSIONS: We show that SNA measurement techniques provide a comprehensive view of ED communication patterns. Our use of SNA revealed that frequency of communication as a measure of interdependencies between ED clinicians varies by day/night shift and over time.
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spelling pubmed-36374592013-04-27 Network analysis of team communication in a busy emergency department Patterson, P Daniel Pfeiffer, Anthony J Weaver, Matthew D Krackhardt, David Arnold, Robert M Yealy, Donald M Lave, Judith R BMC Health Serv Res Research Article BACKGROUND: The Emergency Department (ED) is consistently described as a high-risk environment for patients and clinicians that demands colleagues quickly work together as a cohesive group. Communication between nurses, physicians, and other ED clinicians is complex and difficult to track. A clear understanding of communications in the ED is lacking, which has a potentially negative impact on the design and effectiveness of interventions to improve communications. We sought to use Social Network Analysis (SNA) to characterize communication between clinicians in the ED. METHODS: Over three-months, we surveyed to solicit the communication relationships between clinicians at one urban academic ED across all shifts. We abstracted survey responses into matrices, calculated three standard SNA measures (network density, network centralization, and in-degree centrality), and presented findings stratified by night/day shift and over time. RESULTS: We received surveys from 82% of eligible participants and identified wide variation in the magnitude of communication cohesion (density) and concentration of communication between clinicians (centralization) by day/night shift and over time. We also identified variation in in-degree centrality (a measure of power/influence) by day/night shift and over time. CONCLUSIONS: We show that SNA measurement techniques provide a comprehensive view of ED communication patterns. Our use of SNA revealed that frequency of communication as a measure of interdependencies between ED clinicians varies by day/night shift and over time. BioMed Central 2013-03-22 /pmc/articles/PMC3637459/ /pubmed/23521890 http://dx.doi.org/10.1186/1472-6963-13-109 Text en Copyright © 2013 Patterson et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Patterson, P Daniel
Pfeiffer, Anthony J
Weaver, Matthew D
Krackhardt, David
Arnold, Robert M
Yealy, Donald M
Lave, Judith R
Network analysis of team communication in a busy emergency department
title Network analysis of team communication in a busy emergency department
title_full Network analysis of team communication in a busy emergency department
title_fullStr Network analysis of team communication in a busy emergency department
title_full_unstemmed Network analysis of team communication in a busy emergency department
title_short Network analysis of team communication in a busy emergency department
title_sort network analysis of team communication in a busy emergency department
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3637459/
https://www.ncbi.nlm.nih.gov/pubmed/23521890
http://dx.doi.org/10.1186/1472-6963-13-109
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