Healthcare Analytics Applications

  • Christo El Morr
  • Hossam Ali-Hassan
Part of the SpringerBriefs in Health Care Management and Economics book series (BRIEFSHEALTHCARE)


This chapter provides an overview of many descriptive, predictive, and prescriptive analytics applications in healthcare. Specific algorithms are chosen to illustrate each type of analytic approach. In descriptive analytics, we cover simple descriptive statistics. In predictive analytics, we cover
  1. 1.

    A detailed logistic progression application to illustrate regression analysis

  2. 2.

    Three applications covering decision trees, Naïve Bayes, and natural language processing, to illustrate classification techniques

  3. 3.

    Two applications using K-means and hierarchical clustering to illustrate clustering techniques

  4. 4.

    One dimensionality reduction application to illustrate dimension reduction techniques


Finally, one application illustrates the nascent prescriptive analytics. At the end of the chapter, a set of statistical tools is provided.


Report Pivot table Hospital readmission LACE index Chart review Natural language processing (NLP) K-means Cluster analysis Rehabilitation service Principle component analysis (PCA) Chronic pain 


  1. 1.
    H. A. Simon, Administrative Behavior, 4th Edition. Free Press, 1997.Google Scholar
  2. 2.
    L. C. Jain and C. P. Lim, “Advances in Intelligent Decision Making,” in Handbook on Decision Making: Vol 1: Techniques and Applications, vol. 1, C. P. Lim, Ed.: Springer Berlin Heidelberg, 2010.Google Scholar
  3. 3.
    G. A. Forgionne, “Decision-Making Support System Effectiveness: the Process to Outcome Link %J Inf. Knowl. Syst. Manag,” vol. 2, no. 2, pp. 169-188, 2000.Google Scholar
  4. 4.
    Canada Health Infoway, “Year In Review 2016-2017,” Canada Health Infoway, Toronto Jul 28, 2017 2017, Available:
  5. 5.
    G. F. Anderson and E. P. Steinberg, “Hospital readmissions in the Medicare population,” (in eng), N Engl J Med, vol. 311, no. 21, pp. 1349–53, Nov 22 1984.CrossRefGoogle Scholar
  6. 6.
    C. van Walraven et al., “Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community,” (in eng), Cmaj, vol. 182, no. 6, pp. 551–7, Apr 6 2010.Google Scholar
  7. 7.
    Centers for Medicare & Medicaid Services. (2014, July 22). Readmission Reduction Program. Available: Accessed: 2017-01-31. Archived at
  8. 8.
    (2013). Health Indicators 2013: Definitions, Data Sources and Rationale. Available:
  9. 9.
    Canadian Institute for Health Information, “All-Cause Readmission to Acute Care and Return to the Emergency Department,” CIHI, Ottawa, ON2012, Available:
  10. 10.
    S. E. Frankl, J. L. Breeling, and L. Goldman, “Preventability of emergent hospital readmission,” (in eng), Am J Med, vol. 90, no. 6, pp. 667–74, Jun 1991.CrossRefGoogle Scholar
  11. 11.
    C. H. Yam et al., “Avoidable readmission in Hong Kong--system, clinician, patient or social factor?,” (in eng), BMC Health Serv Res, vol. 10, p. 311, Nov 17 2010.Google Scholar
  12. 12.
    The Canadian Medical Protective Association. (2014). Reducing unplanned hospital readmissions. Available: Accessed: 2017-01-31. Archived at
  13. 13.
    L. M. Sullivan, J. M. Massaro, and R. B. D’Agostino, Sr., “Presentation of multivariate data for clinical use: The Framingham Study risk score functions,” (in eng), Stat Med, vol. 23, no. 10, pp. 1631–60, May 30 2004.CrossRefGoogle Scholar
  14. 14.
    Health Systems Performance Research Network (HSPRN). (2016, Aug. 17). Online LACE index Tool. Available: Accessed: 2017-01-31. Archived at
  15. 15.
  16. 16.
    A. Gruneir et al., “Unplanned readmissions after hospital discharge among patients identified as being at high risk for readmission using a validated predictive algorithm,” Open Medicine, vol. 5, no. 2, pp. e104-e111, 2011.Google Scholar
  17. 17.
    A. S. Mixon et al., “Preparedness for hospital discharge and prediction of readmission,” (in Eng), J Hosp Med, Feb 29 2016.Google Scholar
  18. 18.
    J. Billings, T. Georghiou, I. Blunt, and M. Bardsley, “Choosing a model to predict hospital admission: an observational study of new variants of predictive models for case finding,” BMJ Open, vol. 3, no. 8, p. e003352, Aug 26 2013.CrossRefGoogle Scholar
  19. 19.
    IS Scotland. Scottish patients at risk of readmission (SPARRA). (2017). SPARRA Model. Available: Accessed: 2017-01-30. Archived at
  20. 20.
    A. Mahmoud, “Scottish patients at risk of readmission and admission (Sparra),” International Journal of Integrated Care, vol. 16, no. 6, p. A216, 2016.CrossRefGoogle Scholar
  21. 21.
    H. Kaur et al., “Automated chart review utilizing natural language processing algorithm for asthma predictive index,” (in eng), BMC Pulm Med, vol. 18, no. 1, p. 34, Feb 13 2018.Google Scholar
  22. 22.
    J. J. Armstrong, M. Zhu, J. P. Hirdes, and P. Stolee, “K-means cluster analysis of rehabilitation service users in the Home Health Care System of Ontario: examining the heterogeneity of a complex geriatric population,” (in eng), Arch Phys Med Rehabil, vol. 93, no. 12, pp. 2198–205, Dec 2012.CrossRefGoogle Scholar
  23. 23.
    D. Keszthelyi et al., “Delineation between different components of chronic pain using dimension reduction - an ASL fMRI study in hand osteoarthritis,” (in eng), Eur J Pain, Mar 9 2018.Google Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Christo El Morr
    • 1
  • Hossam Ali-Hassan
    • 2
  1. 1.School of Health Policy and ManagementYork UniversityTorontoCanada
  2. 2.Department of International StudiesGlendon College, York UniversityTorontoCanada

Personalised recommendations