Skip to main content

Advertisement

SpringerLink
Log in

A Linked Democracy Approach for Regulating Public Health Data

  • Original Paper
  • Published:
Health and Technology Aims and scope Submit manuscript

Abstract

This article addresses the problem of constructing a public space to build sustainable data ecosystems for the biomedical field. It outlines three models of democracy —deliberative, epistemic, and linked— where privacy and data protection can be explored in connection with the existing ethical frameworks for Public Health Data, and the Theory of Justice. For the construction of a sustainable public space, it suggests exploring the analytical dimension of Linked Democracy, and the need for building new tools to regulate ‘Linked Open Data’, based on rule of law and the analytical dimension of the meta-rule of law. The construction of ‘intermediate’ or ‘anchoring’ institutions would help in embedding the protections of the rule of law into specific ecosystems (including direct, indirect and tactic modelling of privacy by design).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. Our notion of public space should not be understood according to the strong divide between public and private law. We are referring here to the digital, social and political space shared by citizens and involving state laws, government and corporate policies, and technical standards.

  2. Linked Data refers to a set of methods and standards for publishing data on the Web. The term was famously proposed by Tim Berners Lee in 2006 as a framework to connect data across websites and databases.

  3. E.g. Based on the available online data, social media and local news reports, an algorithm developed by Health Map indicated early signs of the Ebola disease spread in West Africa nine days before they were identified as ‘Ebola’ [22]. The Healthmap did not predict that the “mysterious disease” would spread.

  4. ‘Interoperability’ means ‘semantic interoperability’ here: the creation of a common meaning or information exchange ‘reference,’ across computational systems. We will be returning to this concept, later on (sections 7.1 and 7.3).

  5. Precision medicine can be defined as the “prevention and treatment strategies that take individual variability into account” [49].

  6. A brief reminder: 1. Proactive not Reactive; Preventative not Remedial; 2. Privacy as the Default Setting; 3. Privacy Embedded into Design; 4. Full Functionality—Positive-Sum, not Zero-Sum; 5. End-to-End Security—Full Lifecycle Protection; 6. Visibility and Transparency—Keep it Open; 7. Respect for User Privacy—Keep it User-Centric.

  7. As stated by Woods [62]: “Up to 80% of rare diseases are genetic diseases and strategies that seek to combine “omics” data with whole genome sequencing data, data from medical records, natural history data and data on family members of the proband (the affected individual) are now regarded as essential research tools. This combination of data sources opens a potential for the exploitable re-purposing of research data and presents the research participant with the challenge of consenting to a complex context of biomedical “Big Data”.

  8. See http://governingalgorithms.org/

  9. See the Guidelines for the Protection of Privacy and Transborder Flows of Personal Data set by the Organisation for Economic Co-operation and Development (OECD), https://www.oecd.org/sti/ieconomy/oecdguidelinesontheprotectionofprivacyandtransborderflowsofpersonaldata.htm

  10. For example national EHR systems have been set up in Australia, Denmark, Jordan, Saudi Arabia, Austria, and the Netherlands, while Canada Spain have adopted regional (“autonomic”) models.

  11. See https://myhealthrecord.gov.au/internet/mhr/publishing.nsf/Content/news-002 .

  12. The My Health Records Act 2012 (Cth) Sch 1 cl 2(1) provides for introduction of an opt-out option.

  13. My Health Records Act 2012 (Cth) Sch 1 cl 8(1); My Health Records Regulation 2012 (Cth) reg 4.1.2.

  14. See BBC News [94]: “Three US healthcare organisations are reportedly being held to ransom by a hacker who stole data on hundreds of thousands of patients. The hacker has also put the 650,000 records up for sale on dark web markets where stolen data is traded. Prices for the different databases range from $100,000 (£75,000) to $411,000”.

  15. These are the capabilities that should be widely shared: (i) (EXtract): enabling any health care organization to create a new secondary-use database (e.g., for population health management or clinical research); (ii) (Transmit): enables a clinician to send a copy of a patient’s record to another physician as part of a referral or to a patient’s personal health record; (iii) (Exchange): enables a health care organization to participate in a community-wide health-information exchange; (iv) (Move) enables a health care organization to switch HER developers without incurring extraordinary data extraction and conversion costs; (v) (Embed) enables an organization to develop new EHR features or functionality and incorporate this new software into clinicians’ workflow within their existing EHR.

  16. In November 2015, the Federal Parliament through Health Legislation Amendment (eHealth) Act 2015 (Cth) substantially amended the Personally Controlled Electronic Health Records Act 2012 (Cth) and renamed it My Health Records Act 2012 (Cth).

  17. The idea of a meta-system identity layer for the Internet was first coined by Microsoft architect Kim Cameron ten years ago [113]. For the sake of simplicity, and thinking of implementation into specific domains in a public space, we use the expression “identity ecosystem layer”.

  18. See e.g. http://constitutionlab.org/

References

  1. Stevens L. Big read: what does Google DeepMind want with the NHS? DigitalHealth 20 March 2017, available at: https://www.digitalhealth.net/2017/03/deepmind-mustafa-suleyman-interview/. Accessed 25 March 2017.

  2. Wakefield J. Google Deepmind: should patients trust the company with their data? BBC news, 23 September 2016, available at: http://www.bbc.com/news/technology-37439221. Accessed 5 March 2017.

  3. Mathews R. On protecting and preserving personal privacy in interoperable global healthcare venues. Heal Technol. 2016;6:53–73.

  4. Casanovas P, de Koker L, Mendelson, D, Watts D. Regulation of Big Data: Perspectives on Strategy, Policy, Law and Privacy, Heath & Technology, Special Issue on Privacy, this volume.

  5. Luo J, Wu M, Gopukumar D, Zhao Y. Big data application in biomedical research and Health Care: a literature review. Biomedical informatics insights. 2016;8:1–10. doi:10.4137/BIIII.S31559.

  6. Bender E. Big Data in Biomedicine. Nature. 2015;527(7576):S1–1.

  7. Raghupathi W. Data Mining in Healthcare. Healthcare informatics: improving efficiency through technology, analytics, and management. London: CRC Press; 2013.

    Google Scholar 

  8. Ye SQ, editor. Big Data analysis for bioinformatics and biomedical discoveries. Boca Ratón (USA): Chapman and Hall/CRC Press; 2016.

  9. Goodman KW. Ethics, medicine, and information technology: Intelligent machines and the transformation of Health Care. Cambridge: Cambridge University Press; 2016.

    Google Scholar 

  10. Mittelstadt BD, Floridi L, editors. The Ethics of Biomedical data. Dordrecht: Springer; 2016.

    Google Scholar 

  11. Gutwirth S, Leenes R, de Hert P, editors. Reforming European Data Protection Law. Dordrecht: Springer; 2016.

  12. Gutwirth R, Leenes PDH. Data Protection on the Move. Current Developments in ICT and Privacy/Data Protection. Dordrecht: Springer; 2016.

    Book  Google Scholar 

  13. Gonzalez-Fuster G. The Emergence of Personal Data Protection as a Fundamental Right of the EU. Dordrecht: Springer; 2014.

  14. Hijmans H. The European Union as Guardian of Internet Privacy. The story of art. 16 TFEU. Dordrecht: Springer; 2016.

  15. Egaña-Aranguren M, Fernández-Breis JT, Dumontier M. Special issue on Linked Data for Health Care and the Life Sciences. Semantic Web Journal. 2014;5(2):99–100.

  16. Casanovas P, Palmirani M, Peroni, S, Vitali F, van Engers, T. Guest Editors’ Editorial: The Next Step. Special issue on the semantic web for the legal domain. Semantic Web Journal. 2016;7(3):213–27. doi:10.3233/SW-160224. Available at SSRN. https://ssrn.com/abstract=2765912. Accessed 25 March 2017

  17. Heath T., Bizer C. Linked Data: Evolving the Web into a Global Data Space. Synthesis Lectures on the Semantic Web: Theory and Technology. Morgan & Claypool, 2011.

  18. Berners-Lee T. Giant Global Graph. 2007-11-2. Available at: http://dig.csail.mit.edu/breadcrumbs/node/215. Accessed 12 October 2016.

  19. Lupton D. The commodification of patient opinion: the digital patient experience economy in the age of Big Data. Sociology of Health & Illness. 2014;36(6):856–69.

  20. Badawi, et al. Making Big Data Useful for Health Care: A Summary of the Inaugural MIT Critical Data Conference. MIR Med Inform. 2014 Jul-Dec. 2(2): e22. Published online 2014 Aug 22. doi: 10.2196/medinform.3447. Available at: http://medinform.jmir.org/2014/2/e22/?utm_source=TrendMD&utm_medium=cpc&utm_campaign=JMIR_TrendMD_0. Accessed 25 March 2017.

  21. Hockings EA. Critical Examination of Policy-Developments in Information Governance. and the Biosciences. In B. D. Mittelstadt, L. Floridi, editors. The Ethics of Biomedical Data. Dordrecht: Springer, 2016, pp. 95–115

  22. Asokan GV, Asokan V. Leveraging “Big Data” to enhance the effectiveness of “One health” in an era of Health Informatics. Journal of Epidemiology and Global Health. 2015;5(4):311–4. doi:10.1016/j.jegh.2015.02.001.

  23. Bourne PE, Lorsch JR, Green ED. Perspective: Sustaining the Big-Data ecosystem. Nature. 2015;527(7576):S16–7.

  24. Bohman J. Epistemic Value and Deliberative Democracy. The Good Society. 2009;18(2):s 28–34.

    Article  MathSciNet  Google Scholar 

  25. Dryzek JS., Niemeyer N. Deliberative Turns. In J. Dryzek, editor, Foundations and Frontiers of Deliberative Governance. Oxford Scholarship Online; 2009, pp. 3–17.

  26. Kuyper J. Democratic deliberation in the modern world: The systemic turn. Critical Review. 2015;27(1):49–63.

  27. Grönlund K, Bächtiger A, Setäläs M, editors. Deliberative mini-publics: Involving citizens in the democratic process. Colchester: ECPR Press; 2014.

  28. Fishkin JS. When the people speak: Deliberative democracy and public consultation. Oxford: Oxford University Press; 2009.

  29. Schwartzberg M. Epistemic democracy and its challenges. Annual Review of Political Science. 2015;18:187–203.

  30. Waldron J. The wisdom of the multitude: some reflections on book 3, chapter 11 of Aristotle's politics. Political Theory. 1995;23(4):563–84.

    Article  Google Scholar 

  31. Estlund D. Epistemic approaches to democracy. Episteme: A Journal of Social Epistemology. 2008;5(1):1–4.

    Article  Google Scholar 

  32. Landemore H. Democratic reason: Politics, collective intelligence, and the rule of the many. New Jersey: Princeton University Press; 2013.

  33. Ober J. Democracy and knowledge: Innovation and learning in classical Athens. Princeton University Press: Princeton; 2008.

  34. Hong L, Page S. Groups of diverse problem solvers can outperform groups of high-ability problem solvers. Proc Natl Acad Sci. 2004;101(46):16385–9.

    Article  Google Scholar 

  35. Chun SA, MacKellar B. Social Health Data Integration using Semantic Web, SAC’12, March 25-29, 2012, Riva del Garda, Italy, ACM, 2012, pp. 392–397.

  36. Staab S, Studer R, editors. Handbook on Ontologies. Dordrecht: Springer Science Business Media; 2003.

  37. Horridge M, Parsia B, Sattler U: The state of bio-medical ontologies. Bio-Ontologies SIG, 2011, http://bio-ontologies.knowledgeblog.org/135. Accessed 5 October 2016.

  38. Hoehndorf R., Haendel M, Stevens R, Rebholz-Schuhmanns D. Thematic series on biomedical ontologies in JBMS: challenges and new directions. Journal of biomedical semantics. 2014; 5 (1): 1. doi: 10.1186/2041-1480-5-15, Available at: https://jbiomedsem.biomedcentral.com/articles/10.1186/2041-1480-5-15. Accessed 25 March 2017.

  39. Luciano JS, Andersson B, Batchelor C, Bodenreider O, Clark T, Denney CK, Domarew C, Gambet T, Harland L, Jentzsch A, Kashyap V. The Translational Medicine ontology and Knowledge Base: Driving personalized medicine by bridging the gap between bench and bedside. Journal of Biomedical Semantics 2011 2(Suppl 2):S1. Available at: http://www.Jbiomedsem.Com/content/2/S2/S1. Accessed 5 October 2016.S

  40. Machado CM, Rebholz-Schuhmann D, Freitas AT, Couto FM. The Semantic Web in Translational Medicine: Current applications and future directions. Brief Bioinform. 2015;16(1):89–103.

  41. Sarkar I. N. Biomedical Informatics and Translational Medicine. Journal of Translational Medicine. 2010;8(1):1. doi: 10.1186/1479-5876-8-22

  42. Olla P, Shimskey C. mHealth taxonomy: A literature survey of mobile health applications. Heal Technol. 2015;4(4):299–308.

  43. Mohammed S, Fiaidhi J. Identifying the Emerging e-Health Technologies To Ubiquity 2.0 and Beyond. Ubiquitous Health and Medical Informatics. IGI Global, 2010.

  44. Blobel B, Lopez DM, Gonzalez C. Patient privacy and security concerns on big data for personalized medicine Health and Technololy. 2016;6:75–81. doi:10.1007/s12553-016-0127-5.

  45. UNESCO. Universal Declaration on Bioethics and Human Rights. 19 October 2005. 1260. doi:10.1016/S0140-6736(16)30061-7 Accessed 5 October 2016.

  46. UNESCO. Universal Declaration on Bioethics and Human Rights. 19 October 20105. http://unesdoc.unesco.org/images/0014/001461/146180E.pdf. Accessed 5 October 2016.

  47. Casanovas P. Conceptualisation of Rights and Meta-rule of Law for the Web of Data, Democracia Digital e Governo Eletrônico (Santa Caterina, Brazil). 2015;12:18–41; repr. Journal of Governance and Regulation. 2015;4(4):118–129.

  48. Barrett MA, Humblet O, Hiatt RA, Adler NE. Big Data and disease prevention: from qualified self to quantified communities. Big Data. September 2013;1(3):168–75. doi:10.1089/big.2013.0027. http://online.liebertpub.com/doi/abs/10.1089/big.2013.0027. Accessed 5 October 2016

  49. Collins FS, Varmus H. A new initiative on Precision Medicine. N Engl J Med. 2015;372(9):793–5.

  50. Cavoukian A, Chibba M. Cognitive Cities, Big Data and Citizen Participation: The essentials of Privacy and Security. In: Portmann E, Finger M, editors. Towards cognitive cities. Switzerland: Springer International Publishing; 2016. p. 61–82.

  51. Jonas J, Cavoukian A. Privacy by Design in the age of Big Data. Canada: Information and Privacy Commissioner, Toronto; 2012.

  52. Duhigg C. How Companies Learn Your Secrets. The New York Times Magazine. 16 February 2012. Available at: http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?_r=0 Accessed 10 October 2016.

  53. Vayena E., Gasser U. Strictly Biomedical? Sketching the Ethics of the Big Data Ecosystem in Biomedicine. In: B.D. Mittelstadt, l. Floridi, editors. The Ethics of Biomedical Big Data. Dordrecht: Springer; 2016, pp. 17–39.

  54. Middleton K. Millions of Australians caught in Health records breach. The Saturday Paper. 8 October 2016. https://www.thesaturdaypaper.com.au/news/politics/2016/10/08/millions-australians-caught-health-records-breach/14758452003833. Accessed 8 October 2016.

  55. Richterich A. Using Transactional Big Data for Epidemiological Surveillance: Google Flu Trends and Ethical Implications of ‘Infodemiology’. In: B.D. Mittelstadt, l. Floridi, editors. The Ethics of Biomedical Big Data. Dordrecht: Springer; 2016, pp. 41–72.

  56. Brüggemann T., Hansen J., Dehling T., Sunyaev A. An Information Privacy Risk Index for mHealth Apps. In: S. Schiffner et al, editors. Annual Privacy Forum, APF 2016, Privacy technologies and policy, Frankfurt/main, Germany, September 7, LNAI 9857, Dordrecht, Heidelberg: Springer;2016, pp. 190–201.

  57. van de Ven J., Dylla F. Qualitative Privacy Description Language. An Information Privacy Risk Index for mHealth Apps. In: S. Schiffner et al, editors. Annual Privacy Forum, APF 2016, Privacy technologies and policy, Frankfurt/main, Germany, September 7, LNAI 9857, Dordrecht, Heidelberg: Springer; 2016, pp. 171–189.

  58. Tsormpatzoudi P., Berendt B., & Coudert F. Privacy by Design: From Research and Policy to Practice–the Challenge of Multi-disciplinarity. In: Annual Privacy Forum. Springer International Publishing. B. Berendt et al., editors, Third Annual Privacy Forum, APF-2015, Luxembourg, Luxembourg, October 7–8, 2015, Privacy technologies and policy, revised selected papers, LNAI 9484, Dordrecht, Heidelberg: Springer; 2015, pp. 199–212.

  59. Hallinan D, De Hert P. Many Have It. Wrong–samples Do Contain Personal Data: the Data Protection Regulation as a Superior Framework to Protect Donor Interests in Biobanking and Genomic Research. In: The Ethics of Biomedical Big Data. Dordrecht: Springer; 2016. p. 119–37.

  60. Woolley JP. How Data Are Transforming the Landscape of Biomedical Ethics: The Need for ELSI Metadata on Consent. In: Mittelstadt BD, Floridi L, editors, The Ethics of Biomedical Big Data. Dordrecht: Springer; 2016, pp. 171–197.

  61. Goodman B. What’s Wrong with the Right to Genetic Privacy: Beyond Exceptionalism, Parochialism and Adventitious Ethics. In: Mittelstadt BD, Floridi L, editors.The Ethics of Biomedical Big Data. Dordrecht: Springer; 2016, pp. 139–167.

  62. Woods S. Big Data governance: solidarity and the patient voice. In: The ethics of biomedical big data. Dordrecht: Springer; 2016. p. 221–38.

  63. Gangemi A., Presutti V. Ontology design patterns. In: Staab S, Studer R, editors. Handbook on ontologies. Berlin, Heidelberg: Springer; 2009, pp. 221–243.

  64. Hoehndorf R., Ngonga Ngomo A-C., Pyysalo S., Ohta T., Oellrich A., Rebholz-Schuhmann D. Ontology design patterns to disambiguate relations between genes and gene products in GENIA. Journal of Biomedical Semantics 2011; 2 (5): S1. Available at: https://jbiomedsem.biomedcentral.com/articles/10.1186/2041-1480-2-S5-S1. Accessed 25 March 2017.

  65. Egaña-Aranguren M, Antezana E, Kuiper M., Stevens R. Ontology design patterns for bio-ontologies: a case study on the cell cycle ontology. BMC bioinformatics 2008; 9 (5): S1. Available at: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-S5-S1. Accessed 25 March 2017.

  66. Seddig-Raufie D, Jansen L, Schober D, Boeker M, Grewe , Schulz S. Proposed actions are no actions: re-modeling an ontology design pattern with a realist top-level ontology. Journal of biomedical semantics 2012; 3 (2): S2.. Available at: https://jbiomedsem.biomedcentral.com/articles/10.1186/2041-1480-3-S2-S2. Accessed 25 March 2017.

  67. Gharib M, Giorgini P, Mylopoulos J. Ontologies for privacy requirements engineering: a systematic literature review. The Computer Research Repository, CORR, November 2016. Available at: https://arxiv.org/abs/1611.10097. Accessed 17 February 2017.

  68. Suárez-Figueroa MC, Gómez-Pérez A, Motta E, Gangemi A, editors. Ontology engineering in a networked world. Dordrecht: Springer; 2012. doi:10.1007/978-3-642-24794-1_1.

    Google Scholar 

  69. Villazón-Terrazas B, Vilches-Blázquez LM, Corcho O, Gómez-Pérez A. Methodological guidelines for publishing government linked data. In D. Wood, editor. Linking government data. New York: Springer; 2011, pp. 27–49. doi: 10.1007/978-1-4614-1767-5.

  70. Santos C, Casanovas P, Rodriguez-Doncel V, Van der Torre L. Non-ontological Legal Resources Reuse and Reengineering, EKAW-2016, Workshop on the Semantic Web and Legal Knowledge, Bologna, November 2016.

  71. Casanovas, P. Semantic Web Regulatory Models: Why Ethics Matter. Philosophy & Technology. 2015; 28(1):33–55

  72. Casanovas P, Pagallo U, Palmirani M, Sartor G, editors. AI Approaches to the Complexity of Legal Systems IV. Social Intelligence, Models and Applications for Law and Justice Systems in the Semantic Web and Legal Reasoning, LNAI 8929, Heidelberg: Springer; 2014. doi:10.1007/978-3-662-45960-7

  73. Boddington P. Big data, small talk: Lessons from the ethical practices of interpersonal communication for the Management of Biomedical big Data. In: Mittelstadt BD, Floridi L, editors. The ethics of biomedical data. Dordrecht: Springer; 2016. p. 277–305.

    Chapter  Google Scholar 

  74. Walzer M. Liberalism and the art of separation. Political Theory. 1984;12(3):315–30.

    Article  Google Scholar 

  75. Walzer M. Spheres of justice: a defense of pluralism. New York: Basic Books; 1983.

    Google Scholar 

  76. Nissenbaum, H. Privacy as contextual integrity. Wash. L. Rev.; 2004; 79:119–154.

  77. Nissenbaum H. Privacy in context: technology, policy, and the integrity of social life: Stanford University Press; 2010.

  78. Nissenbaum H. A contextual approach to privacy online. Daedalus. 2011;140(4):32–48.

    Article  Google Scholar 

  79. Barth A., Datta A., Mitchell JC., Nissenbaum H. Privacy and contextual integrity: Framework and applications. In: 2006 I.E. Symposium on Security and Privacy, 21–24 May. Proceedings IEEE 2006. p. 15 pp.-198. doi:10.1109/SP.2006.32.

  80. Floridi L. The Ethics of Information. Oxford: Oxford University Press.

  81. Mittelstadt BD, Floridi L. The Ethics of Big Data: Current and foreseeable issues in biomedical contexts. In: Mittelstadt BD, Floridi L, editors. The Ethics of Biomedical Data. Dordrecht: Springer; 2016. p. 445–80.

  82. Dwork C, Roth A. The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science. 2014;9(3–4):211–407.

    MathSciNet  MATH  Google Scholar 

  83. Simonite T. Apple’s New Privacy Technology May Pressure Competitors to Better Protect Our Data, MIT Technology Review, August 3, 2016. Available at: https://www.technologyreview.com/s/602046/apples-new-privacy-technology-may-pressure-competitors-to-better-protect-our-data/?utm_campaign=content-distribution&utm_source=dlvr.it&utm_medium=twitter. Accesed 5 October 2016.

  84. Gillespie T. The relevance of algorithms. Media technologies: Essays on communication, materiality, and society. T. Gillespie, P. Boczkowski, K. Foot, editors. Cambridge, MA: MIT Press Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.692.3942&rep=rep1&type=pdf. Accessed 18 October 2016.

  85. Domingo-Ferrer J, Soria-Comas J, Ciobotaru O. Co-utility: Self-enforcing protocols without coordination mechanisms. In: Industrial Engineering and Operations Management (IEOM), 2015 International Conference, IEEE, 1–7. doi:10.1109/IEOM.2015.7093833

  86. Koops B-J, Hoepman JH, Leenes R. Open-source intelligence and privacy by design. Computer Law & Security Review. 2013;29(6):676–88.

  87. Casanovas P, Arraiza J, Melero F, González-Conejero J, Molcho G, Cuadros M. Fighting Organized Crime Through Open Source Intelligence: Regulatory Strategies of the CAPER Project. In: R. Hoekstra, editor, JURIX-2014, Legal Knowledge and Information Systems 271, Amsterdam: IOS Press; pp. 189–198. doi:10.3233/978-1-61499-468-8-189

  88. Colesky M., Hoepman JH., Hillan C. A Critical Analysis of Privacy Design Strategies, Security and Privacy Workshops (SPW). 2016; IEEE. doi:10.1109/SPW.2016.23.s

  89. Koops B-J, Leenes,R. Privacy regulation cannot be hardcoded. A critical comment on the ‘Privacy by design’provision in Data-protection law. International Review of Law, Computers & Technology; 2014,28(2):159–171. doi:10.1080/13600869.2013.801589

  90. Casanovas P. Open Source Intelligence, Open Social Intelligence and Privacy by Design. In: A. Herzig, E. Lorini, editors. Proceedings of European Conference on Social Intelligence (ECSI), Barcelona, November 3–5,2014; CEUR; 2014, vol. 1283, pp. 174–185. http://ceur-ws.org/Vol-1283/paper_24.pdf. Accessed 5 October 2016.

  91. Klitou D. A solution, but not a panacea for defending privacy: the challenges, criticism and limitations of privacy by design. In: Preneel B, Ikonomou D, editors. Privacy technologies and policy, first Annual privacy forum, APF 2012, LNCS 8319. Berlin: Springer; 2012. pp. 86–110. doi:10.1007/978-3-642-54069-1_6.

  92. Mendelson D, Wolf G. My electronic Health Record’Cui Bono (For Whose Benefit)? 24 Journal of Law and Medicine, 283. Available at: http://www.zorgictzorgen.nl/wp-content/uploads/2016/12/SSRN-id2881787.pdf. Accessed 17 February 2017.

  93. Caldicott F. Review of data security, consent and opt-outs, National Data Guardian for Health and Care, 6 July 2016. Available at https://www.gov.uk/government/publications/review-of-data-security-consent-and-opt-outs. Accessed March 10 2017.s

  94. BBC News, BBC News, US Healthcare records offered for sale online, 27 Jun3 2016. available at http://www.bbc.com/news/technology-36639981. Accessed March 10th 2017.

  95. Fernández-Alemán JL, Carrión-Señor I, Oliver Lozoya PA, Toval A. Security and privacy in electronic health records: A systematic literature review. J Biomed Inform. 2013;46(3):541–62. doi:10.1016/j.jbi.2012.12.003.

    Article  Google Scholar 

  96. Mahfuth A, Dhillon JS, Drus SM. A systematic review on data security and patient privacy issues in electronic medical records. Journal of Theoretical and Applied Information Technology. 2016;90,(2):106–115. Available at: http://www.jatit.org/volumes/Vol90No2/12Vol90No2.pdf [accessed 25 March 2017].

  97. Rezaeibagha F, Win KT, Susilo W. A systematic literature review on security and privacy of electronic health record systems: technical perspectives. Health Information Management Journal. 2015;44(3):23–38. doi:10.12826/18333575.2015.0001.

    Article  Google Scholar 

  98. Kun L, Beuscart R, Coatrieux G, Quantin ., with consultations and contributions from Robert. Mathews. Improving outcomes with interoperable EHRs and secure global health information infrastructure. In: Medical and Care Compunethics 5, L. Bos et al. (Eds.), Amsterdam: IOS Press, 2008, pp. 68–79.

  99. Chalasani S, Jain P, Dhumal P, Moghimi H. Wickramasinghe. Content architecture applications in healthcare. Heal Technol. 2014;4(1):11–9. doi:10.1007/s12553-014-0075-x.

    Article  Google Scholar 

  100. Sittig DF, Wright A. What makes an EHR “open” or interoperable? J Am Med Inform Assoc. 2015;22(5):1099–101. doi:10.1093/jamia/ocv060.

    Article  Google Scholar 

  101. Mandel JC, Kreda DA, Mandl KD, Kohane IS, Ramoni RB. SMART on FHIR: A standards-based, interoperable apps platform for Electronic Health Records. Journal of the American Medical Informatics Association. Res Appl. doi:10.1093/jamia/ocv189.

  102. Kindrick JD, Sauter JA, Matthews RS. Improving conformance and interoperability testing. StandardView. 1996;4(1):61–8.

    Article  Google Scholar 

  103. Noyes H. Direct democracy as a legislative act. Chapman Law Review. 2016;19(1):199–218.

    Google Scholar 

  104. Schwrzschild M. Popular initiatives and American federalism, or Putting Direct Democracy in its Place. Journal of Contemporary Legal Issues. 2014;13:531.

    Google Scholar 

  105. DuVivier KK. The United States as a democratic ideal? International Lessons in Referendum Democracy Temple Law Review. 2006;79:821.

    Google Scholar 

  106. Kirby M. The Australian Republican Referendum 1999 - Ten Lessons, Law and Justice Foundation Available at: http://www.lawfoundation.net.au/ljf/app/&id=DF4206863AE3C52DCA2571A30082B3D5. Accessed 10 March 2017.

  107. Hoffmann C. The 8 categories of crowdsourcing in Healthcare, MedCityNews, January 3 2015. Available at: http://medcitynews.com/2015/01/8-categories-crowdsourcing-healthcare/?rf=1. Accessed 15 October 2016.

  108. Swan M. Health 2050: the realization of personalized medicine through crowdsourcing, the quantified self, and the participatory biocitizen. Journal of Personalized Medicine 2012; 2(3): 93–118. doi:10.3390/jpm2030093. Available at: http://www.mdpi.com/2075-4426/2/3/93/htm. Accessed 15 October 2016.

  109. Ranard BL, Ha YP, Meisel ZF, Asch DA, Hill SS, Becker LB, Merchant RM. Crowdsourcing—Harnessing the masses to advance Health and Medicine, a systematic review. J Gen Intern Med. 2014;29(1):187–203.

  110. Boulos MNK, Resch B, Crowley DN, Breslin JG, Sohn G, Burtner R, Chuang KYS. Crowdsourcing, citizen sensing and sensor web technologies for public and environmental Health surveillance and crisis management: Trends, OGC standards and application examples. Int J Health Geogr 2011;10(1): 1. Available at: http://ij-healthgeographics.biomedcentral.com/articles/10.1186/1476-072X-10-67. Accessed 15 October 2016.

  111. Poblet M. editor. Mobile technologies for Conflict management: Online Dispute Resolution, Governance, Participation. Dordrecht: Springer, 2011. doi: 10.1007/978-94-007-1384-0

  112. Poblet M, García-Cuesta E, Casanovas P. Crowdsourcing: Roles, Methods and Tools for Data-intensive Disaster Management. Information Systems Frontiers, published 12 January 2017. doi:10.1007/s10796-017-9734-6.

  113. Cameron K. The 7 Laws of Identity, May 2005. Available at: https://msdn.microsoft.com/en-us/library/ms996456.aspx. Accessed 10 March 2017.

  114. Garcia M, Grassi PA. NISTIR 8103. Advanced Identity Workshop on Applying Measurement Science in the Identity Ecosystem: Summary and Next Steps. Applied Cybersecurity Division Information Technology Laboratory, September 2016. Available at: http://nvlpubs.nist.gov/nistpubs/ir/2016/NIST.IR.8103.pdf. Accessed March 10 2017.

  115. Grassi PA, Nadeau EM, Galluzzo RJ, Dinh AT. NIST Internal Report 8112 (draft). Attribute Metadata, August, 2016.. Available at: https://pages.nist.gov/NISTIR-8112/. Accessed 10 March 2017.

  116. Lessig L. Code and Other Laws of the Cyberspace, v.2.0. 2006. http://codev2.cc/. Accessed 15 October 2016.

  117. Klitou D. Privacy-invading technologies and privacy by design. Berlin: Springer and TMC Asser Press; 2014. doi:10.1007/978-94-6265-026-8_2.

    Google Scholar 

  118. Hardy Q. The Web’s Creator Looks to Reinvent It. New York Times, June 7 2016. Available at: https://www.nytimes.com/2016/06/08/technology/the-webs-creator-looks-to-reinvent-it.html?_r=2. Accessed 10 March 2017.

  119. Manyika J, Lund S, Bughin J, Woetzel J, Stamenov K, Dhruv D. Digital globalization: The new era of global flows. Report. Mackinsey Global Institute. March 2016. Available at: http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/digital-globalization-the-new-era-of-global-flows. Accessed 5 October 2016.

  120. Kennedy B. Most Americans trust the military and scientists to act in the public’s interest, FactTank, October 18, 2016. Available at: http://www.pewresearch.org/fact-tank/2016/10/18/most-americans-trust-the-military-and-scientists-to-act-in-the-publics-interest /. Accessed 20 October 2016.

  121. C.L. Philip Chen, Zhang C-Y. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data, Information Sciences;s 2014, 275: 314–347. doi:10.1016/j.ins.2014.01.015

  122. Andrighetto G., Governatori G., Noriega P., van der Torre L., editors. Normative Multi-Agent Systems, Saarbrüucken/ /Wadern: Schloss-Dagstuhl Publishing, 2013. doi:10.4230/DFU.Vol4.12111.i. Available at: https://pdfs.semanticscholar.org/15be/bb5940263a609efef75df357bf33f82c1e26.pdf. Accessed 25 March 2017.

  123. Mendelson G and Mendelson D. Criteria of Evaluation of Personal Injury and Damage in Australia. In: SD Ferrara, R Boscolo-Berto, G Viel, editors. Personal Injury and Damage Ascertainment under Civil Law. Dordrecht: Springer; 2017, pp. 467–507.

Download references

Acknowledgements

Law and Policy Program of the Australian government funded Data to Decisions Cooperative Research Centre (http://www.d2dcrc.com.au/); Meta-Rule of Law DER2016-78108-P, Research of Excellence, Spain. In Figure 1, we used icons from the Noun Project: the group icon was created by Gregor Cresnar, the piled data icon by IcoDots, and the the mobile device icon by Vildana.

Author information

Authors and Affiliations

  1. Institute of Law and Technology (IDT), Faculty of Law, Autonomous University of Barcelona, Cerdanyola del Vallès, 08193, Barcelona, Spain

    Pompeu Casanovas

  2. Law and Policy Program: Data to Decisions Cooperative Research Centre and La Trobe Law School, La Trobe University, Melbourne, VIC, 3086, Australia

    Pompeu Casanovas

  3. Key Independent Researcher, formerly Chair in Law (Research), School of Law Deakin University, Melbourne, Australia, VIC, Australia

    Danuta Mendelson

  4. Graduate School of Business and Law, Royal Melbourne Institute of Technology, Melbourne, VIC, Australia

    Marta Poblet

Authors
  1. Pompeu Casanovas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Danuta Mendelson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marta Poblet
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pompeu Casanovas.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Funding

Data to Decisions Cooperative Research Centre (D2D CRC Ltd., ABN 45168769677; Project DC160051-Integrated Policing: End User Evaluation. DER2016-78108-P. While the support of the Data to Decisions Cooperative Research Centre is acknowledged, the views expressed in this article do not necessarily reflect the views of the Centre.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study (not applicable).

Additional information

This article is part of the Topical collection on Privacy and Security of Medical Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Casanovas, P., Mendelson, D. & Poblet, M. A Linked Democracy Approach for Regulating Public Health Data. Health Technol. 7, 519–537 (2017). https://doi.org/10.1007/s12553-017-0191-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12553-017-0191-5

Keywords

Search

Navigation