Employing Personal Health Records for Population Health Management

  • Ana KostadinovskaEmail author
  • Gert-Jan de Vries
  • Gijs Geleijnse
  • Katerina Zdravkova
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 311)


Linking various sources of medical data provides a wealth of data to researchers. Trends in society, however, have raised privacy concerns, leading to an increasing awareness of the value of data and data ownership. Personal Health Records address this concern by explicitly giving ownership of data to the patient and enabling the patient to choose whom to provide access to their data. We explored whether this paradigm still allows for population health management, including data analysis of large samples of patients, and built a working prototype to demonstrate this functionality. The creation and application of a readmission risk model for cardiac patients was used as carrier application to illustrate the functionality of our prototype platform.


Personal Health Records Population Health Management 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Flemmix, P.: Panopticon (2013), (last accessed: June 01, 2014)
  2. 2.
    MarketingCharts: Privacy A Growing Concern For Almost 2 in 3 Internet Users (2013), (last accessed: June 01, 2014)
  3. 3.
    Meingast, M., Roosta, T., Sastry, S.: Security and Privacy Issues with Health Care Information Technology. In: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2006, pp. 5453–5458 (2006)Google Scholar
  4. 4.
    Tang, P.C., Ash, J.S., Bates, D.W., Overhage, J.M., Sands, D.Z.: Personal Health Records: Definitions, Benefits, and Strategies for Overcoming Barriers to Adoption. J. Am. Med. Inform. Assoc. 13(2), 121–126 (2006)Google Scholar
  5. 5.
    Microsoft HealthVault, (last accessed: June 1, 2014)
  6. 6.
    Lee, M., Delaney, C., Moorhead, S.: Building a personal health record from a nursing perspective. International Journal of Medical Informatics 76 (2007)Google Scholar
  7. 7.
    Schneider, K.M., O’Donnell, B.E., Dean, D.: Prevalence of multiple chronic conditions in the United States’ Medicare population. Health Qual. Life Outcomes 7, 82 (2009)CrossRefGoogle Scholar
  8. 8.
    Stranges, E., Barrett, M., Wier, L.M., Andrews, R.M.: Readmissions for Heart Attack. Healthcare Cost Utilization Project, Statistical Brief, 140 (2009)Google Scholar
  9. 9.
    Jennings, D.L., Petricca, J.C., Yageman, L.A., O’Dell, K., Kalus, J.S.: Predictors of RehospitalizationAfter Acute Coronary Syndromes. American Journal of Health System Pharmacy 63(4) (2006)Google Scholar
  10. 10.
    Kociol, R.D., Lopes, R.D., Clare, R., Thomas, L., Mehta, R.H., Kaul, P., Pieper, K.S., Hochman, J.S., Weaver, W.D., Armstrong, P.W., Granger, C.B., Patel, M.R.: International Variation in and Factors Associated With Hospital Readmission After Myocardial Infarction. American Medical Association 307(1) (2012)Google Scholar
  11. 11.
    Andres, E., Cordero, A., Magan, P., Alegria, E., Leon, M., Luenqo, E., Botaya, R.M., Garcia Ortiz, L., Casasnovas, J.A.: Long-Term Mortality and Hospital Readmission after Acute Myocardial Infarction: an eight-year follow up study. Revista Espanola de Cardiologia 65(5) (2012)Google Scholar
  12. 12.
    Ross, J.S., Mulvey, G.K., Stauffer, B., Patlolla, V., Bernheim, S.M., Keenan, P.S., Krumholz, H.M.: Statistical Models and Patient Predictors of Readmission for Heart Failure: A Systematic Review. Arch. Intern. Med. 168(13), 1371–1386 (2008)CrossRefGoogle Scholar
  13. 13.
    Kansagara, D., Englander, H., Salanitro, A., Kagen, D., Theobald, C., Freeman, M., Kripalani, S.: Risk Prediction Models for Hospital Readmission A Systematic Review. JAMA: The Journal of the American Medical Association 306(15), 1688–1698 (2010)CrossRefGoogle Scholar
  14. 14.
    De Vries, J.J.G., Geleijnse, G., Tesanovic, A., Van de Ven, A.R.T.: Heart Failure Risk Models and Their Readiness for Clinical Practice. In: 2013 IEEE International Conference on Healthcare Informatics (ICHI), pp. 239–47 (2013)Google Scholar
  15. 15.
    Desai, M.M., Stauffer, B.D., Feringa, H.H., Schreiner, G.C.: Statistical Models and Patient Predictors of Readmission for Acute Myocardial Infarction: a systematic review. Circulation. Cardiovascular Quality and Outcomes 2(5) (2009)Google Scholar
  16. 16.
    McDonald, J.: Student’s t-test, Handbook of Biological Statistics, pp. 118–122. Sparky House Publishing, Baltimore (2009)Google Scholar
  17. 17.
    Witoelar, A.W., Ghosh, A., de Vries, J.J.G., Hammer, B., Biehl, M.: Window-Based Example Selection in Learning Vector Quantization. Neural Computation 22(11), 2924–2961 (2011)CrossRefGoogle Scholar
  18. 18.
    Auble, T.E., Hsieh, M., McCausland, J.B., Yealy, D.M.: Comparison of Four Clinical Prediction Rules for Estimating Risk in Heart Failure. Annals of Emergency Medicine 50(2), 127–135 (2007)CrossRefGoogle Scholar
  19. 19.
    Morrow, D.A., Antman, E.M., Charlesworth, A., Carins, R., Murphy, S.A., de Lemons, J.A., Guigliano, R.P., McCabe, C.H., Braunwald, E.: TIMI risk score for ST-elevation myocardial infarction: A convenient, bedside, clinical score for risk assessment at presentation: An intravenous nPA for treatment of infracting myocardium early II trial substudy. Circulation 102 (2000)Google Scholar
  20. 20.
    Resource Description Framework (RDF), (last accessed: June 1, 2014)

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ana Kostadinovska
    • 1
    Email author
  • Gert-Jan de Vries
    • 2
  • Gijs Geleijnse
    • 2
  • Katerina Zdravkova
    • 1
  1. 1.Faculty of Computer Science and EngineeringSs. Cyril and Methodius UniversitySkopjeMacedonia
  2. 2.Philips Research – HealthcareEindhovenThe Netherlands

Personalised recommendations