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

Fong, Allan; Clark, Lindsey; Cheng, Tianyi; Franklin, Ella; Fernandez, Nicole; Ratwani, Raj M.; Parker, Sarah

doi:10.1111/jonm.12476

Cited by 9 publications

(7 citation statements)

References 22 publications

(20 reference statements)

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“…The presence of high influencers in healthcare teams is established. In keeping with our own results, the personality characteristics associated with this network influencing roles have been shown to include contentiousness and agreeableness 21. The importance of perception of the utility of a practice, which we found to be key for adoption of ways of organising work, is also described elsewhere 22…”

Section: Discussionsupporting

confidence: 76%

Diffusion of knowledge and behaviours among trainee doctors in an acute medical unit and implications for quality improvement work: a mixed methods social network analysis

et al. 2019

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ObjectivesTo describe the social networks that diffuse knowledge, attitudes and behaviours relating to different domains of practice within teams of trainee doctors in an acute hospital medical setting. The domains examined were ‘clinical-technical’, ‘patient centredness’ and ‘organisation of work’.DesignSequential mixed methods: (i) sociocentric survey of trainee consisting of questions about which colleagues are emulated or looked to for advice, with construction of social network maps, followed by (ii) semi-structured interviews regarding peer-to-peer influence, analysed using a grounded theory approach. The study took place over 24 months.SettingAn acute medical admissions unit, which receives admissions from the emergency department and primary care, in a National Health Service England teaching hospital.ParticipantsTrainee medical doctors working in five consecutive rotational teams. Surveys were done by 39 trainee doctors; then 15 different participants from a maximal diversity sample were interviewed.ResultsClinical-technical behaviours spread in a dense network with rich horizontal peer-to-peer connections. Patient-centred behaviours spread in a sparse network. Approaches to non-patient facing work are seldom copied from colleagues. Highly influential individuals for clinical technical memes were identified; high influencers were not identified for the other domains.ConclusionInformation and influence relating to different aspects of practice have different patterns of spread within teams of trainee doctors; highly influential individuals were important only for spread of clinical-technical practice. Influencers have particular characteristics, and this knowledge could guide leaders and teachers.

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Section: Discussionsupporting

confidence: 76%

Diffusion of knowledge and behaviours among trainee doctors in an acute medical unit and implications for quality improvement work: a mixed methods social network analysis

et al. 2019

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“…Unsupervised clustering was performed to analyze the usual patterns among staff [42]. Results have concluded that we can recognize and group the influencers in developing intervention to alter the culture.…”

Section: Un-supervised Learningmentioning

confidence: 99%

“…Three kinds of optimization algorithms, evolutionary [8][9][10][11][12][13][14][15][16][17], stochastic [18][19][20][21][22][23][24][25][26][27][28][29] and combinatorial optimization [30][31][32][33][34][35][36][37][38] will be addressed. For machine learning algorithms, the discussion is based on un-supervised learning [39][40][41][42][43][44][45][46][47][48][49], supervised learning and semi-supervised learning [71][72][73][74][75][76][77][78]…”

Section: Introductionmentioning

confidence: 99%

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

et al. 2017

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Abstract:To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer's disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed.

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“…Another study on small ICU medical teams examined the formation of multidisciplinary communication networks, in which a doctor was at the center of one team network, while a nurse was at the center of another [21]. Yet another study on ICUs demonstrated that nurses represent an important group of influential individuals who create the central workplace culture [22]. The majority of previous social network analysis studies [16,20,21] used manual methods, such as surveys and observations, to collect connectivity data.…”

Section: Discussionmentioning

confidence: 99%

Social Network Analysis of Intensive Care Unit Health Care Professionals Measured by Wearable Sociometric Badges: Longitudinal Observational Study (Preprint)

Kawamoto¹,

Ito-Masui²,

Esumi³

et al. 2020

Preprint

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BACKGROUND Use of wearable sensor technology for studying human teamwork behavior is expected to generate a better understanding of the interprofessional interactions between health care professionals. OBJECTIVE We used wearable sociometric sensor badges to study how intensive care unit (ICU) health care professionals interact and are socially connected. METHODS We studied the face-to-face interaction data of 76 healthcare professionals in the ICU at Mie University Hospital collected over 4 weeks via wearable sensors. RESULTS We detail the spatiotemporal distributions of staff members’ inter- and intraprofessional active face-to-face interactions, thereby generating a comprehensive visualization of who met whom, when, where, and for how long in the ICU. Social network analysis of these active interactions, concomitant with centrality measurements, revealed that nurses constitute the core members of the network, while doctors remain in the periphery. CONCLUSIONS Our social network analysis using the comprehensive ICU interaction data obtained by wearable sensors has revealed the leading roles played by nurses within the professional communication network.

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

Abstract: 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.

Cited by 9 publications

References 22 publications

Diffusion of knowledge and behaviours among trainee doctors in an acute medical unit and implications for quality improvement work: a mixed methods social network analysis

Diffusion of knowledge and behaviours among trainee doctors in an acute medical unit and implications for quality improvement work: a mixed methods social network analysis

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Social Network Analysis of Intensive Care Unit Health Care Professionals Measured by Wearable Sociometric Badges: Longitudinal Observational Study (Preprint)

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