Governance and IT Architecture



Personalized medicine relies on the integration and analysis of diverse sets of health data. Many patients and healthy individuals are willing to play an active role in supporting research, provided there is a trust-promoting governance structure for data sharing as well as a return of information and knowledge. provides an IT platform that manages personal data under such a governance structure. As a not-for-profit citizen-owned cooperative, its vision is to allow citizens to collect, store, visualize, and share specific sets of their health-related data with friends and health professionals, and to make anonymized parts of these data accessible to medical research projects in areas that appeal to them. The value generated by this secondary use of personal data is managed collectively to operate and extend the platform and support further research projects. In this chapter, we describe central features of and insights gained since the operation of the platform. As an example for a novel patient engagement effort, has led to new forms of participation in research besides formal enrolment in clinical trials or epidemiological studies.


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  1. Appelboom, G., LoPresti, M., Reginster, J.-Y., Sander Connolly, E., & Dumont, E. P. L. (2014). The quantified patient: A patient participatory culture. Current Medical Research and Opinion, 30(12), 2585–2587. Scholar
  2. Bonney, R., Cooper, C. B., Dickinson, J., Kelling, S., Phillips, T., Rosenberg, K. V., et al. (2009). Citizen science: A developing tool for expanding science knowledge and scientific literacy. Bioscience, 59(11), 977–984. Scholar
  3. Datenschutzbeauftragte, daschug G., externe. (2018). Inhalte und Hinweise zur DSGVO/EU-Datenschutz-Grundverordnung. Retrieved February 26, 2018, from
  4. Derder Fathi. (2015). Recht auf Nutzung der persönlichen Daten. Recht auf Kopie. Retrieved February 26, 2018, from
  5. Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64–73. Scholar
  6. FHIR v3.0.1. (2018). Retrieved February 26, 2018, from
  7. Hafen, E., Kossmann, D., & Brand, A. (2014). Health data cooperatives - citizen empowerment. Methods of Information in Medicine, 53(2), 82–86. Scholar
  8. Health Record Banking Alliance. (2018). HRBA overview. Retrieved February 26, 2018, from
  9. Hürlimann, D., & Zech, H. (2016). Rechte an Daten. sui generis. Retrieved from
  10. Irwin, A. (2001). Constructing the scientific citizen: Science and democracy in the biosciences. Public Understanding of Science, 10(1), 1–18. Scholar
  11. Jeff Leek. (2013). The key word in “Data Science” is not data, it is science. Retrieved February 26, 2018, from
  12. Kaye, J., Heeney, C., Hawkins, N., de Vries, J., & Boddington, P. (2009). Data sharing in genomics – Re-shaping scientific practice. Nature Reviews. Genetics, 10(5), 331–335. Scholar
  13. LOINC. (2018). The freely available standard for identifying health measurements, observations, and documents. Retrieved February 26, 2018, from
  14. Mellström, C., & Johannesson, M. (2008). Crowding out in blood donation: Was Titmuss right? Journal of the European Economic Association, 6(4), 845–863. Scholar
  15. National Research Council (US) Committee on A Framework for Developing a New Taxonomy of Disease. (2011). Toward precision medicine: Building a knowledge network for biomedical research and a new taxonomy of disease. Washington, DC: National Academies Press. Retrieved from
  16. Olsson, T., Barcellos, L. F., & Alfredsson, L. (2017). Interactions between genetic, lifestyle and environmental risk factors for multiple sclerosis. Nature Reviews. Neurology, 13(1), 25–36. Scholar
  17. Roehrs, A., da Costa, C. A., Righi, R. d. R., & de Oliveira, K. S. F. (2017). Personal health records: A systematic literature review. Journal of Medical Internet Research, 19(1), e13. Scholar
  18. Savova, G. K., Masanz, J. J., Ogren, P. V., Zheng, J., Sohn, S., Kipper-Schuler, K. C., et al. (2010). Mayo clinical text analysis and knowledge extraction system (cTAKES): Architecture, component evaluation and applications. Journal of the American Medical Informatics Association: JAMIA, 17(5), 507–513. Scholar
  19. SNOMED International. (2018). Retrieved February 26, 2018, from
  20. Sospedra, M., & Martin, R. (2005). Immunology of multiple sclerosis. Annual Review of Immunology, 23, 683–747. Scholar
  21. Swan, M. (2012). Health 2050: The realization of personalized medicine through crowdsourcing, the quantified self, and the participatory biocitizen. Journal of Personalized Medicine, 2(3), 93–118. Scholar
  22. Takacs, J., Pollock, C. L., Guenther, J. R., Bahar, M., Napier, C., & Hunt, M. A. (2014). Validation of the Fitbit one activity monitor device during treadmill walking. Journal of Science and Medicine in Sport, 17(5), 496–500. Scholar
  23. Töpel, T., Kormeier, B., Klassen, A., & Hofestädt, R. (2008). BioDWH: A data warehouse kit for life science data integration. Journal of Integrative Bioinformatics, 5(2), 49–57. Scholar
  24. Uzuner, O., Solti, I., & Cadag, E. (2010). Extracting medication information from clinical text. Journal of the American Medical Informatics Association, 17(5), 514–518. Scholar
  25. Woolley, J. P., McGowan, M. L., Teare, H. J. A., Coathup, V., Fishman, J. R., Settersten, R. A., & Juengst, E. T. (2016). Citizen science or scientific citizenship? Disentangling the uses of public engagement rhetoric in national research initiatives. BMC Medical Ethics, 17, 33. Scholar
  26. Yasnoff, W. A., & Shortliffe, E. H. (2014). Lessons learned from a health record bank start-up. Methods of Information in Medicine, 53(2), 66–72. Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute for Medical Informatics, University of Applied Sciences BernBernSwitzerland
  2. 2.Institute of Molecular Systems Biology, ETH ZürichZürichSwitzerland

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