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The influence of strong and weak ties in physician peer networks on new drug adoption

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Listed:
  • Yong Cai

    (IQVIA)

  • Mohamed Abouzahra

    (California State University at Monterey Bay)

Abstract

Physicians interact and exchange information through various social networks. Understanding peer effects through different networks can help accelerate new medical technology and innovative treatment adoption. In this research, we measure the influence of strong-tie and weak-tie connections on new drug adoption and study the overlap between advice-discussion and patient-sharing network. We construct two physician networks with strong and weak ties from peer nomination surveys and commercial medical claims data. We design a dynamic system to define peer adoption status and build patient-level hierarchical logistic models to measure the peer influence on new product adoption for treating new-to-therapy patients. Our results show that A strong-tie early adoption peer has six times more influence on new drug adoption than a weak-tie peer. Weak tie peers collectively exert as much or higher influence than strong-tie peers because of the larger network size. In the case of inaccessibility to strong-tie data, researchers can still reliably use the influence of the weak tie data only even though they will lose the effect of the omitted strong ties.

Suggested Citation

  • Yong Cai & Mohamed Abouzahra, 2023. "The influence of strong and weak ties in physician peer networks on new drug adoption," International Journal of Health Economics and Management, Springer, vol. 23(1), pages 133-147, March.
  • Handle: RePEc:kap:ijhcfe:v:23:y:2023:i:1:d:10.1007_s10754-022-09335-8
    DOI: 10.1007/s10754-022-09335-8
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    1. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
    2. Raghuram Iyengar & Christophe Van den Bulte & Thomas W. Valente, 2011. "Opinion Leadership and Social Contagion in New Product Diffusion," Marketing Science, INFORMS, vol. 30(2), pages 195-212, 03-04.
    3. Shaw, Norman, 2014. "The role of the professional association: A grounded theory study of Electronic Medical Records usage in Ontario, Canada," International Journal of Information Management, Elsevier, vol. 34(2), pages 200-209.
    4. Julie M Donohue & Hasan Guclu & Walid F Gellad & Chung-Chou H Chang & Haiden A Huskamp & Niteesh K Choudhry & Ruoxin Zhang & Wei-Hsuan Lo-Ciganic & Stefanie P Junker & Timothy Anderson & Seth Richards, 2018. "Influence of peer networks on physician adoption of new drugs," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-18, October.
    5. Kimberley H Geissler & Benjamin Lubin & Keith M Marzilli Ericson, 2020. "The association between patient sharing network structure and healthcare costs," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-13, June.
    6. Muller, Eitan & Peres, Renana, 2019. "The effect of social networks structure on innovation performance: A review and directions for research," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 3-19.
    7. Hossain, Akram & Quaresma, Rui & Rahman, Habibur, 2019. "Investigating factors influencing the physicians’ adoption of electronic health record (EHR) in healthcare system of Bangladesh: An empirical study," International Journal of Information Management, Elsevier, vol. 44(C), pages 76-87.
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