Social-network analysis in healthcare: analysing the effect of weighted influence in physician networks

Abstract

Understanding how relationships are structured in physician networks provides insights into how these networks influence physicians’ beliefs and behaviors. This understanding would help improve strategies for disseminating medical information and guidelines. But most physician social networks mainly focus on a binary relationship where either one physician is connected or not connected to another physician without any description about the strength of the relationship. This binary relationship can lead to misinformation in the network (as acquaintances and close friends may be treated equally). In this paper, we overcome the limitation of the binary relationship by proposing a weighted influence approach among a network of physicians. A physician network is a social graph comprising of nodes (physicians) and edges between nodes (social relationships). Specifically, we attach weights to the edges to quantify the strength of the relationship between two connected physicians. In one network, we assume an un-weighted (binary) link between two connected physicians; whereas, in a second network, we assume a weighted link between two physicians. In both networks, edges are created between physicians who are affiliated with the same organization-group and affiliated or working in the same hospital within the same specialty or specialty-group. We compare both the weighted and un-weighted approaches in the network by considering the diffusion of four highly prescribed pain medications in the US. Results reveal that the weighted approach is superior compared to the un-weighted approach network in explaining the diffusion of pain medications inside the social network. Additionally, our results help us identify that affiliation to the same organization-group and affiliation to the same hospital are important attributes to the diffusion process. Additionally, weights with high values do not necessarily lead to large diffusions inside the social network. We highlight the implication of our results for the diffusion of innovations in physician networks.

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Notes

  1. 1.

    The name of medications and physicians have been anonymized under a non-discloser agreement.

  2. 2.

    The 32-bit kdb + version is free and available at https://kx.com. An academic license for the 64-bit version is available for free to researchers upon request.

  3. 3.

    Both the cosine similarity and Jaccard similarity predict equal proportion of edges.

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Acknowledgements

The project was supported from grants (awards: #IITM/CONS/PPLP/VD/03 and IITM/CONS/RxDSI/VD/16) to Varun Dutt. We are thankful to Dr. Baskaran Sankaran, Mr. Nataraj Dasgupta, Mr. Sayee Natarajan, and Mr. Larry A. Pickett Jr. for their valuable comments and suggestions in this research.

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Correspondence to Abhinav Choudhury.

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Choudhury, A., Kaushik, S. & Dutt, V. Social-network analysis in healthcare: analysing the effect of weighted influence in physician networks. Netw Model Anal Health Inform Bioinforma 7, 17 (2018). https://doi.org/10.1007/s13721-018-0176-y

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Keywords

  • Social network analysis
  • Similarity measure
  • Pain medications
  • Big-data