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
The name of medications and physicians have been anonymized under a non-discloser agreement.
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.
Both the cosine similarity and Jaccard similarity predict equal proportion of edges.
References
Borbas C, Morris N, McLaughlin B, Asinger R, Gobel F (2000) The role of clinical opinion leaders in guideline implementation and quality improvement. Chest 118(2):24S–32S
Brown JJ, Reingen PH (1987) Social ties and word-of-mouth referral behavior. J Consum Res 14(3):350–362
Burt RS (1992) Structural hole. Harvard Business School Press, Cambridge
Burt RS, Minor MJ (1983) Applied network analysis: a methodological introduction. Sage Publications, Inc, Thousand Oaks
Choudhury A, Kaushik S, Dutt V (2017) Social-network analysis for pain medications: influential physicians may not be high-volume prescribers. In: Proceedings of the 2017 IEEE/ACM international conference on advances in social networks analysis and mining 2017. ACM, pp 881–885
Coleman J, Katz E, Menzel H (1957) The diffusion of an innovation among physicians. Sociometry 20(4):253–270
Creswick N, Westbrook JI (2010) Social network analysis of medication advice-seeking interactions among staff in an Australian hospital. Int J Med Inform 79(6):e116–e125
Cullen R (1997) The medical specialist: information gateway or gatekeeper for the family practitioner. Bull Med Libr Assoc 85(4):348
Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 57–66
exxeleron (2015) QPython [software]. https://github.com/exxeleron/qPython. Accessed 12 Dec 2017
Gilbert E, Karahalios K (2009) Predicting tie strength with social media. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 211–220
Goyal A, Bonchi F, Lakshmanan LV (2010) Learning influence probabilities in social networks. In: Proceedings of the third ACM international conference on Web search and data mining. ACM, pp 241–250
Granovetter MS (1973) The strength of weak ties. Am J Soc 78(6):1360–1380
Granovetter MS (1977) The strength of weak ties. Am J Soc 78(6):1360–1380
Granovetter M (1983) The strength of weak ties: a network theory revisited. Sociol Theory 1:201–233
Gruppen LD, Wolf FM, Voorhees CV, Stross JK (1987) Information-seeking strategies and differences among primary care physicians. J Contin Educ Health Prof 7(3):18–26
Hangal S, MacLean D, Lam MS, Heer J (2010) All friends are not equal: using weights in social graphs to improve search. Workshop on Social Network Mining & Analysis, ACM KDD
Haug JD (1997) Physicians’ preferences for information sources: a meta-analytic study. Bull Med Libr Assoc 85(3):223
Hill S, Provost F, Volinsky C (2006) Network-based marketing: identifying likely adopters via consumer networks. Stat Sci 21(2):256–276
IM S Health (2012) Healthcare organization services: professional and organization affiliations maintenance process. Bedford, USA. http://us.imshealth.com/legal/ServicePlanDetails-HCOS.pdf. Accessed 15 Jan 2018
Keating NL, Ayanian JZ, Cleary PD, Marsden PV (2007) Factors affecting influential discussions among physicians: a social network analysis of a primary care practice. J Gen Intern Med 22(6):794–798
Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 137–146
Knoke D, Kuklinski JH (1982) Network analysis. Quantitative applications in the social sciences, vol 28. Sage Publications, Beverly Hills
Kossinets G, Watts DJ (2009) Origins of homophily in an evolving social network. Am J Sociol 115(2):405–450
Kx Systems (2003) Kdb+ [software]. https://kx.com/download/. Accessed 12 Oct 2017
Landis J (2013) Post-acute care cheat sheet: integrated delivery networks. Advisory Board, Washington, DC. https://www.advisory.com/research/post -acute-care- collaborative/members/resources/cheat-sheets/integrated-delivery- networks. Accessed 15 Jan 2018
Levandowsky M, Winter D (1971) Distance between Sets. Nature 234:34–35
Mahajan V, Muller E, Bass FM (1991) New product diffusion models in marketing: a review and directions for research. In: Nakićenović N, Grübler A (eds) Diffusion of technologies and social behavior. Springer, Berlin, Heidelberg, pp 125–177
McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Annu Rev Sociol 27(1):415–444
Pappalardo L, Rossetti G, Pedreschi D (2012) How well do we know each other? Detecting tie strength in multidimensional social networks. In: Proceedings of the 2012 international conference on advances in social networks analysis and mining (ASONAM 2012). IEEE Computer Society, pp 1040–1045
Rajaraman A, Ullman JD (2011) Mining of massive datasets. Cambridge University Press, Cambridge
Rogers EM (1995) Diffusion of innovations, 4th edn. Free Press, New York
Saito K, Nakano R, Kimura M (2008) Prediction of information diffusion probabilities for independent cascade model. In: International conference on knowledge-based and intelligent information and engineering systems. Springer, Berlin, pp 67–75
Salton G, Wong A, Yang CS (1975) A vector space model for automatic indexing. Commun ACM 18(11):613–620
Sasaki Y (2007) The truth of the F-measure. Teach Tutor Mater 1(5):1–5
Scott J (1991) Social network analysis: a handbook
Scott J, Tallia A, Crosson JC, Orzano AJ, Stroebel C, DiCicco-Bloom B, Crabtree B (2005) Social network analysis as an analytic tool for interaction patterns in primary care practices. Ann Fam Med 3(5):443–448
Singhal A (2001) Modern information retrieval: a brief overview. IEEE Data Eng Bull 24(4):35–43
Susarla A, Oh JH, Tan Y (2012) Social networks and the diffusion of user-generated content: Evidence from YouTube. Inform Syst Res 23(1):23–41
Valente TW, Davis RL (1999) Accelerating the diffusion of innovations using opinion leaders. Ann Am Acad Polit Soc Sci 566(1):55–67
Vanderveen KA, Paterniti DA, Kravitz RL, Bold RJ (2007) Diffusion of surgical techniques in early stage breast cancer: variables related to adoption and implementation of sentinel lymph node biopsy. Ann Surg Oncol 14(5):1662–1669
Wellman B (1997) Structural analysis: from method and metaphor to theory and substance. Contemp Stud Sociol 15:19–61
Williamson JW, German PS, Weiss R, Skinner EA, Bowes F (1989) Health science information management and continuing education of physicians: a survey of US primary care practitioners and their opinion leaders. Ann Intern Med 110(2):151–160
Zheng K, Padman R, Krackhardt D, Johnson MP, Diamond HS (2010) Social networks and physician adoption of electronic health records: insights from an empirical study. J Am Med Inform Assoc 17(3):328–336
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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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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DOI: https://doi.org/10.1007/s13721-018-0176-y