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Identifying influential individuals on intensive care units: using cluster analysis to explore culture.

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Citation: Journal of Nursing Management. 25(5):384-391, 2017 JulPMID: 28568480Institution: MedStar Institute for InnovationForm of publication: Journal ArticleMedline article type(s): Journal ArticleSubject headings: *Health Personnel/px [Psychology] | *Intensive Care Units/ma [Manpower] | *Organizational Culture | *Peer Influence | *Social Support | Academic Medical Centers/og [Organization & Administration] | Cluster Analysis | Health Personnel/sn [Statistics & Numerical Data] | Health Personnel/st [Standards] | Humans | Intensive Care Units/og [Organization & Administration] | Intensive Care Units/sn [Statistics & Numerical Data] | Safety Management/sn [Statistics & Numerical Data] | Safety Management/st [Standards] | Surveys and QuestionnairesYear: 2017 ISSN:

0966-0429

Name of journal: Journal of nursing managementAbstract: AIM: The objective of this paper is to identify attribute patterns of influential individuals in intensive care units using unsupervised cluster analysis.BACKGROUND: Despite the acknowledgement that culture of an organisation is critical to improving patient safety, specific methods to shift culture have not been explicitly identified.CONCLUSIONS: Culture is created locally by individual influencers. Cluster analysis is an effective way to identify common characteristics among members of an intensive care unit team that are noted as highly influential by their peers. To change culture, identifying and then integrating the influencers in intervention development and dissemination may create more sustainable and effective culture change. Additional studies are ongoing to test the effectiveness of utilising these influencers to disseminate patient safety interventions.Copyright c 2017 John Wiley & Sons Ltd.IMPLICATIONS FOR NURSING MANAGEMENT: This study offers an approach that can be helpful in both identifying and understanding influential team members and may be an important aspect of developing methods to change organisational culture.METHODS: A social network analysis survey was conducted and an unsupervised cluster analysis was used.RESULTS: A total of 100 surveys were gathered. Unsupervised cluster analysis was used to group individuals with similar dimensions highlighting three general genres of influencers: well-rounded, knowledge and relational.All authors: Cheng T, Clark L, Fernandez N, Fong A, Franklin E, Parker SH, Ratwani RFiscal year: FY2018Digital Object Identifier:

https://dx.doi.org/10.1111/jonm.12476

Date added to catalog: 2017-07-10

Holdings ( 1 )
Title notes ( 8 )

Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Journal Article	MedStar Authors Catalog	Article	28568480	Available		28568480

AIM: The objective of this paper is to identify attribute patterns of influential individuals in intensive care units using unsupervised cluster analysis.

BACKGROUND: Despite the acknowledgement that culture of an organisation is critical to improving patient safety, specific methods to shift culture have not been explicitly identified.

CONCLUSIONS: Culture is created locally by individual influencers. Cluster analysis is an effective way to identify common characteristics among members of an intensive care unit team that are noted as highly influential by their peers. To change culture, identifying and then integrating the influencers in intervention development and dissemination may create more sustainable and effective culture change. Additional studies are ongoing to test the effectiveness of utilising these influencers to disseminate patient safety interventions.

IMPLICATIONS FOR NURSING MANAGEMENT: This study offers an approach that can be helpful in both identifying and understanding influential team members and may be an important aspect of developing methods to change organisational culture.

METHODS: A social network analysis survey was conducted and an unsupervised cluster analysis was used.

RESULTS: A total of 100 surveys were gathered. Unsupervised cluster analysis was used to group individuals with similar dimensions highlighting three general genres of influencers: well-rounded, knowledge and relational.

English