Application of Social Network Analysis to Health Care Sectors

Jang, Hae Lan; Lee, Young Sung; An, Ji-Young

doi:10.4258/hir.2012.18.1.44

Cited by 11 publications

(12 citation statements)

References 41 publications

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“…For example, they have also been used to represent the social relationships between people [26]. Typical applications of sociograms have been to ethnic relationships [27], physical education [28], personal relationships [21], skilled migration (Ryan, et al 2014 [22]), online communities [29], and medicine [30,31].…”

Section: Introductionmentioning

confidence: 99%

Interaction Diagrams: Development of a Method for Observing Group Interactions

Nestsiarovich

Pons

2018

Behavioral Sciences

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Recording of team meeting’s processes with electronic devices can be problematic because of the invasiveness of the process: issues with privacy; interpretation difficulty with noise or quiet speech; and distortion of participants’ behaviour. There is a need for less intrusive methods. We developed the interaction diagram method by extending the directed graph nature of sociograms to capture the time sequence of events, including the identification of the person, communication behaviour, and duration of interactions. The method was tested on engineering team meetings. Data processing by quantitative and qualitative analysis is shown to be feasible. Several team roles were observed in the engineering context: Initiator; Passive collector; Explorer; Information provider; Facilitator; Arbitrator; Representative; Gatekeeper; Connector; and Outsider. The work provides a graphical representation of the record of the interaction flow during meetings. It does this without needing video recording. It is also an efficient method, as it does not require subsequent transcription or coding. It provides a procedure to quickly analyse communication situations, identify group roles, and compare group activity at different meetings.

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Section: Introductionmentioning

confidence: 99%

Interaction Diagrams: Development of a Method for Observing Group Interactions

Nestsiarovich

Pons

2018

Behavioral Sciences

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“…Crucial to healthcare network analysis is selecting a network construction algorithm appropriate for the analytic goal, and understanding how the algorithm affects the results obtained from analysis. Despite the increasing use of network models to improve healthcare delivery and outcomes [ 1 , 4 , 5 , 11 , 12 , 34 – 36 ], rigorous published reports comparing the properties of networks constructed with different algorithms are lacking. There is also little guidance addressing the choice algorithms for different types of analyses.…”

Section: Introductionmentioning

confidence: 99%

“…One such method (the sliding frame algorithm is described in detail in the Methods section, has been used by the United States Center for Medicare Services to construct the annual United States Medicare Physician Referral Datasets [26][27][28][29], and to determine referral volumes from general practitioners to specialists [14,30,31].Crucial to healthcare network analysis is selecting a network construction algorithm appropriate for the analytic goal, and understanding how the algorithm affects the results obtained from analysis. Despite the increasing use of network models to improve healthcare delivery and outcomes [1,4,5,[32][33][34], rigorous published reports comparing the networks constructed with different algorithms are lacking. There is also little guidance addressing the choice algorithms for different types of analyses.…”

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confidence: 99%

Properties of healthcare teaming networks as a function of network construction algorithms

et al. 2017

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Network models of healthcare systems can be used to examine how providers collaborate, communicate, refer patients to each other, and to map how patients traverse the network of providers. Most healthcare service network models have been constructed from patient claims data, using billing claims to link a patient with a specific provider in time. The data sets can be quite large (106–108 individual claims per year), making standard methods for network construction computationally challenging and thus requiring the use of alternate construction algorithms. While these alternate methods have seen increasing use in generating healthcare networks, there is little to no literature comparing the differences in the structural properties of the generated networks, which as we demonstrate, can be dramatically different. To address this issue, we compared the properties of healthcare networks constructed using different algorithms from 2013 Medicare Part B outpatient claims data. Three different algorithms were compared: binning, sliding frame, and trace-route. Unipartite networks linking either providers or healthcare organizations by shared patients were built using each method. We find that each algorithm produced networks with substantially different topological properties, as reflected by numbers of edges, network density, assortativity, clustering coefficients and other structural measures. Provider networks adhered to a power law, while organization networks were best fit by a power law with exponential cutoff. Censoring networks to exclude edges with less than 11 shared patients, a common de-identification practice for healthcare network data, markedly reduced edge numbers and network density, and greatly altered measures of vertex prominence such as the betweenness centrality. Data analysis identified patterns in the distance patients travel between network providers, and a striking set of teaming relationships between providers in the Northeast United States and Florida, likely due to seasonal residence patterns of Medicare beneficiaries. We conclude that the choice of network construction algorithm is critical for healthcare network analysis, and discuss the implications of our findings for selecting the algorithm best suited to the type of analysis to be performed.

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“…SNA has also been suggested as a pragmatic methodology to delineate interaction models in healthcare settings. 4 - 8 Furthermore, we aim to present a proposal to integrate the health system groups through the SNA methodology as well as social media technology.…”

mentioning

confidence: 99%