Analyzing Collective Knowledge Towards Public Health Policy Making

Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 628)


Nowadays there exists a plethora of diverse data sources producing tons of healthcare data, augmenting the size of data that finally is stored both in Electronic Health Records (EHRs) and in Personal Health Records (PHRs). Thus, the great challenge that emerges is not only to gather all this data in an efficient and effective manner, but also to extract knowledge out of it. The latter is the key factor that enables healthcare professionals to take serious clinical decisions both on individual and on collective level, finally forming representative public health policies. Towards this direction, the current paper proposes a system that supports a new paradigm of EHRs, the eXtended Health Records (XHRs), which include the majority of the health determinants. XHRs are then transformed into XHRs Networks that capture the clinical, social and human context of diverse population segmentations, producing the corresponding collective knowledge. By exploiting this knowledge, the proposed system is finally able to create multi-modal policies, addressing various facts and evolving risks that arise from diverse population segmentations.


EHRs PHRs Collective knowledge Data analysis Public health policies 



The research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: BeHEALTHIER - T2EDK-04207).


  1. 1.
    Top 10 Challenges Healthcare Companies Face Today. Accessed April 2021
  2. 2.
    The State of Healthcare Industry – Statistics for 2021. Accessed April 2021
  3. 3.
    WHO: Analysis of third global survey on eHealth based on the reported data by countries (2016). April 2021
  4. 4.
    Menachemi, N., Collum, T.H.: Benefits and drawbacks of electronic health record systems. Risk Manag. Healthc. Policy 4, 47 (2011)CrossRefGoogle Scholar
  5. 5.
    Bate, L., Hutchinson, A., Underhill, J., Maskrey, N.: How clinical decisions are made. Br. J. Clin. Pharmacol. 74(4), 614–620 (2012)CrossRefGoogle Scholar
  6. 6.
    Kahn, J.S., Aulakh, V., Bosworth, A.: What it takes: characteristics of the ideal personal health record. Health Aff. 28(2), 369–376 (2012)CrossRefGoogle Scholar
  7. 7.
    Cyganek, B., et al.: A survey of big data issues in electronic health record analysis. Appl. Artif. Intell. 30(6), 497–520 (2016)CrossRefGoogle Scholar
  8. 8.
    Boccia, S.: Why is personalized medicine relevant to public health? Eur. J. Public Health 24, 349–350 (2014)CrossRefGoogle Scholar
  9. 9.
    Swati, B.B., Jayashree, R.P.: Machine learning approach to revolutionize use of holistic health records for personalized healthcare. Int. J. Adv. Sci. Technol. 29(05), 313–321 (2020)Google Scholar
  10. 10.
    Benson, T.: Principles of Health Interoperability HL7 and SNOMED. Springer, London (2012). Scholar
  11. 11.
    Kruse, C., Stein, A., Thomas, H., Kaur, H.: The use of electronic health records to support population health: a systematic review of the literature. J. Med. Syst. 42 (2018).
  12. 12.
    Asimakopoulos, S., Asimakopoulos, G., Spillers, F.: Motivation and user engagement in fitness tracking: Heuristics for mobile healthcare wearables. Informatics 4(1), 5 (2017). Multidisciplinary Digital Publishing InstituteCrossRefGoogle Scholar
  13. 13.
    Runtastic. Runtasitc: Running, Cycling and Fitness GPS Tracker. Accessed April 2021
  14. 14.
    Runkeeper. Runkeeper - Track your runs, walks and more with your iPhone or Android phone. Accessed April 2021
  15. 15.
    FitBit. Fitbit Official Site for Activity Trackers & More. Accessed April 2021
  16. 16.
    Garmin. Garmin Connect. Accessed April 2021
  17. 17.
    Strava. Strava| Run and Cycling Tracking on the Social Network for Athletes. Accessed April 2021
  18. 18.
    Misfit. Smartwatches, Fitness Trackers & Wearable Technology – Misfit. Accessed April 2021
  19. 19.
    Nokia. Nokia Health (Withings)| Connected health devices for the whole family: scales, activity & HR monitors, thermometer, camera…. Accessed April 2021
  20. 20.
    iHealth. iHealth Labs Europe - Connected Health. Accessed April 2021
  21. 21.
    Noura, S.A.-H., Eimear, E.K.: Personalized medicine and the power of electronic health records. Cell 177(1), 58–69 (2019)CrossRefGoogle Scholar
  22. 22.
    Bennett, C.C.: Utilizing RxNorm to support practical computing applications: capturing medication history in live electronic health records. J. Biomed. Inform. 45(4), 634–641 (2012)CrossRefGoogle Scholar
  23. 23.
    Dorda, W., Duftschmid, G., Gerhold, L., Gall, W., Gambal, J.: Introducing the Electronic Health Record in Austria. Studies in Health Technology and Informatics, vol. 116. IOS Press, Amsterdam (2005)Google Scholar
  24. 24.
    Haveman, H., Flim, C.: eHealth Strategy and Implementation Activities in the Netherlands. eHalth ERA Project (2007)Google Scholar
  25. 25.
    Xu, J., Gao, X., Sorwar, G., Crol, I.P.: Implementation of e-health record systems in Australia. Int. Technol. Manag. Rev. 3(2), 92–104 (2013)CrossRefGoogle Scholar
  26. 26.
    Chiaravalloti, M.T., Ciampi, M., Pasceri, E., Sicuranza, M., De Pietro, G., Guarasci, R.: A model for realizing interoperable EHR systems in Italy. In: Proceedings of the 15th International HL7 Interoperability Conference (2015)Google Scholar
  27. 27.
    Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nat. Rev. Genet. 13(6), 395–405 (2012)CrossRefGoogle Scholar
  28. 28.
    Chousiadas, D., Menychtas, A., Tsanakas, P., Maglogiannis, I.: Advancing quantified-self applications utilizing visual data analytics and the Internet of Things. In: Iliadis, L., Maglogiannis, I., Plagianakos, V. (eds.) AIAI 2018. IAICT, vol. 520, pp. 263–274. Springer, Cham (2018). Scholar
  29. 29.
    Cohen, M.J., et al.: Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis. Crit. Care 14(1), 1–11 (2010)CrossRefGoogle Scholar
  30. 30.
    Huang, G.T., Cunningham, K.I., Benos, P.V., Chennubhotla, C.S.: Spectral clustering strategies for heterogeneous disease expression data. In: Pacific Symposium on Biocomputing. NIH Public Access, pp. 212 (2013)Google Scholar
  31. 31.
    Pew research Centre. Internet Project survey of US citizens, 2012. Accessed April 2021
  32. 32.
    Kho, A.N., et al.: Electronic medical records for genetic research: results of the eMERGE consortium. Sci. Transl. Med. 3, 79re1 (2011)CrossRefGoogle Scholar
  33. 33.
    Nelson, C.A., Butte, A.J., Baranzini, S.E.: Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings. Nat. Commun. 10, 3045 (2019)CrossRefGoogle Scholar
  34. 34.
    Winslow, C.E.A.: The Evolution and Significance of the Modern Public Health Campaign. Yale University Press, New York (1923)Google Scholar
  35. 35.
    Turnock, B.: Public Health. Jones & Bartlett Publishers, Burlington (2012)Google Scholar
  36. 36.
    Kremastinou-Kourea, J.: Public Health: Theory, Action, Policies. Publications Technogramma, Athens (2007)Google Scholar
  37. 37.
    National Action Plan for Public Health 2008 – 2012, Ministry of Health & Social Solidarity, Athens 2008. Accessed April 2021
  38. 38.
    National Action Plan for Public Health 2019 – 2022, Ministry of Health, National Council for Public Health, Athens 2019. Accessed April 2021
  39. 39.
    Kiourtis, A., Mavrogiorgou, A., Menychtas, A., Maglogiannis, I., Kyriazis, D.: Structurally mapping healthcare data to HL7 FHIR through ontology alignment. J. Med. Syst. 43(3), 62 (2019)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2021

Authors and Affiliations

  1. 1.University of PiraeusPiraeusGreece

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