Applying Data Mining to Healthcare: A Study of Social Network of Physicians and Patient Journeys

  • Shruti Kaushik
  • Abhinav Choudhury
  • Kaustubh Mallik
  • Anzer Moid
  • Varun Dutt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9729)


In 2004, the US President launched an initiative to make healthcare medical records available electronically [27]. This initiative gives researchers an opportunity to study and mine healthcare data across hospitals, pharmacies, and physicians in order to improve the quality of care. Physicians can make better informed decisions regarding care of patients if physicians have proper understanding of patient journeys. In addition, physician healthcare decisions are influenced by their social networks. In this paper, we find patterns among patient journeys for pain medications from sickness to recovery or death. Next, we combine social network analysis and diffusion of innovation theory to analyze the diffusion patterns among physicians prescribing pain medications. Finally, we suggest an interactive visualization interface for visualizing demographic distribution of patients. The main implication of this research is a better understanding of patient journeys via data-mining and visualizations; and, improved decision-making by physicians in treating patients.


Diffusion of innovation Patient journey Social network analysis Physicians Visualization Pain medications 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Curry, J., McGregor, C., Tracy, S.: A communication tool to improve the patient journey modeling process. In: Conference Proceedings of the International Conference of IEEE Engineering in Medicine and Biology Society, pp. 4726–4730 (2006)Google Scholar
  2. 2.
    Ben-Tovim, D.I., et al.: Patient journeys: the process of clinical redesign. Medical Journal of Australia 188(6), S14 (2008)Google Scholar
  3. 3.
    Trebble, T.M., et al.: Process mapping the patient journey through health care: an introduction. BMJ 341(7769), 394–397 (2010)Google Scholar
  4. 4.
    NHS Modernisation Agency. Process mapping, analysis and redesign. Department of Health, London, pp. 1–40 (2005)Google Scholar
  5. 5.
    Valente, T.W.: Social network thresholds in the diffusion of innovations. Social networks 18(1), 69–89 (1996)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Ryan, B., Gross, N.C.: The diffusion of hybrid seed corn in two Iowa communities. Rural Sociology 8, 15–24 (1943)Google Scholar
  7. 7.
    Rogers, E.M.: Diffusion of Innovation, 3rd edn. Free Press, New York (1983)Google Scholar
  8. 8.
    Valente, T.W.: Diffusion of Innovation and policy decision-making. Journal of Communications 43, 30–45 (1993)CrossRefGoogle Scholar
  9. 9.
    Knoke, D., Kuklinski, J.H.: Network analysis. Sage, Newbury Park (1982)Google Scholar
  10. 10.
    Burt, R.S., Minor, M.J.: Applied Network Analysis. Sage, Newbury Park (1983)Google Scholar
  11. 11.
    Wellman, B.: Structural analysis: From method and metaphor to theory and substance. Contemporary Studies in Sociology 15, 19–61 (1997)Google Scholar
  12. 12.
    Scott, J.: Network Analysis: A Handbook. Sage, Newbury Park (1991)Google Scholar
  13. 13.
    Rogers, E.M.: Categorizing the adopters of agricultural practices. Rural Sociology 23, 345–354 (1958)Google Scholar
  14. 14.
    Beal, G.M., Bohlen, J.M.: How Farm People Accept New Ideas. Cooperative Extension Service Report 15, Ames, IA (1955)Google Scholar
  15. 15.
    Valente, T.W., Rogers, E.M.: The origins and development of the diffusion of innovations paradigm as an example of scientific growth. Science communication 16(3), 242–273 (1995)CrossRefGoogle Scholar
  16. 16.
    Anderson, J.G., Jay, S.J.: The diffusion of medical technology: Social network analysis and policy research. The Sociological Quarterly 26(1), 49–64 (1985)CrossRefGoogle Scholar
  17. 17.
    Mahajan, V., et al.: Determination of adopter categories by using innovation diffusion models. Journal of Marketing Research, 37–50 (1990)Google Scholar
  18. 18.
    Big Data Analytics, Time-Series Database, Kx Systems.
  19. 19.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, vol. 1215 (1994)Google Scholar
  20. 20.
    Don Josef de Mendoza y Rios, F.R.S.: Recherches sur les principaux problemes de l’astronomie nautique. In: Proceedings of the Royal Society (1796)Google Scholar
  21. 21.
    Johansson, J.: On the Usability of 3D Display in Parallel Coordinates: Evaluating the Efficiency of Identifying 2D Relationships (2013)Google Scholar
  22. 22.
    Bostock, M.: Parallel Coordinates, November 5, 2011Google Scholar
  23. 23.
    Parallel coordinates (0.7.0) A visual toolkit for multidimensional detectivesGoogle Scholar
  24. 24.
    Nutrient Contents - Parallel Coordinates.
  25. 25.
    U.S. Population By Region (1990–2010).
  26. 26.
    Heidari, B.: Knee osteoarthritis prevalence, risk factors, pathogenesis and features: Part I. Caspian Journal of Internal Medicine 2(2), 205–212 (2011)Google Scholar
  27. 27.
    The White House, authors. Transforming Health Care: The President’s Health Information Technology Plan, December 16, 2004.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Shruti Kaushik
    • 1
  • Abhinav Choudhury
    • 1
  • Kaustubh Mallik
    • 1
  • Anzer Moid
    • 1
  • Varun Dutt
    • 1
  1. 1.Applied Cognitive Science LaboratoryIndian Institute of Technology MandiMandiIndia

Personalised recommendations