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Nudging Consent and the New Opt-Out System to the Processing of Health Data in England

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Legal Tech and the New Sharing Economy

Abstract

This chapter examines the challenges of the revised opt-out system and the secondary use of health data in England. The analysis of this data could be valuable for science and medical treatment as well as for the discovery of new drugs. For this reason, the UK government established the “care.data program” in 2013. The aim of the project was to build a central nationwide database for research and policy planning. However, the processing of personal data was planned without proper public engagement. Research has suggested that IT companies—such as in the Google DeepMind deal case—had access to sensitive data and failed to comply with data protection law. Since May 2018, the government has launched the “national data opt-out” (ND opt-out) system with the hope of regaining public trust. Nevertheless, there is no evidence of significant changes in the ND opt-out, compared to the previous opt-out system. Neither in the use of secondary data, nor in the choices that patients can make. The only notorious difference seems to be in the way that these options are communicated and framed to the patients. Most importantly, according to the new ND opt-out, the type-1 opt-out option—which is the only choice that truly stops data from being shared outside direct care—will be removed in 2020. According to the Behavioral Law and Economics literature (Nudge Theory), default rules—such as the revised opt-out system in England—are very powerful, because people tend to stick to the default choices made readily available to them. The crucial question analyzed in this chapter is whether it is desirable for the UK government to stop promoting the type-1 opt-outs, and whether this could be seen as a kind of “hard paternalism.”

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Notes

  1. 1.

    Deloitte (2016), p. 3.

  2. 2.

    Institute of Medicine (2013); Hanney and González-Block (2015), pp. 1–4.

  3. 3.

    See Digital NHS UK. Available at: https://digital.nhs.uk/national-data-opt-out. Accessed 10 June 2019.

  4. 4.

    Institute of Medicine (2013); Safran et al. (2007), pp. 1–9.

  5. 5.

    Safran et al. (2007), pp. 1–9.

  6. 6.

    Given the importance of this new field of law, the Government of the United Kingdom Cabinet Office established the “Behavioral Insight Team” (BIT)—unofficially known as the “Nudge Unit.” The BIT was originally a governmental organization set up to apply insights from behavioral economics to improve public policy and services. Recently, the BIT became a limited company and it is co-owned by the government. Since the BIT was spun off as a social purpose company, it has given birth to a global movement that now spans 153 countries. See, The Behavioral Insight Team. Available at: https://www.bi.team. Accessed 10 June 2019.

  7. 7.

    Angner and Loewenstein (2016) pp. 1–56; see generally, Zeiler and Teitelbaum (2015); Minton and Kahle (2013).

  8. 8.

    See English Collins Dictionary (Nudge).

  9. 9.

    Corrales and Kousiouris (2017), p. 161.

  10. 10.

    Corrales and Jurčys (2016), p. 533.

  11. 11.

    Corrales and Jurčys (2016), p. 533.

  12. 12.

    Thaler and Sunstein (2009), p. 6.

  13. 13.

    Coggon et al. (2017), p. 177.

  14. 14.

    See Nudging in the Cafeteria (2008). Available at: https://nudges.wordpress.com/2008/04/17/nudging-in-the-cafeteria/. Accessed 10 June 2019.

  15. 15.

    Willis (2015).

  16. 16.

    Thaler and Sunstein (2009) p. 6.

  17. 17.

    Businessballs.com. Nudge theory. Available at: https://www.businessballs.com/improving-workplace-performance/nudge-theory/. Accessed 10 June 2019.

  18. 18.

    Corrales and Kousiouris (2017), pp. 165–166.

  19. 19.

    See, e.g., generally, Jamson (2013), p. 298.

  20. 20.

    Thaler and Sunstein (2009), p. 3.

  21. 21.

    Sunstein (2014), pp. 1–30, 179.

  22. 22.

    Stoknes (2015), p. 25; see also Sunstein (2016).

  23. 23.

    Ben-Porath (2010), p. 11.

  24. 24.

    Heshmat (2015), p. 243.

  25. 25.

    Corrales et al. (2019), p. 197.

  26. 26.

    Detels and Gulliford (2015), p. 782.

  27. 27.

    See John et al. (2013), p. 104; Quigley and Stokes (2015), p. 64; Thaler (2009); Hamilton and Zufiaurre (2014), p. 18.

  28. 28.

    Leitzel (2015), p. 137.

  29. 29.

    Shafir (ed) (2013), p. 496.

  30. 30.

    Corrales et al. (2019), p. 197.

  31. 31.

    Zamir (2015), p. 103; see, also, generally, Davidai et al. (2012), pp. 15201–15205.

  32. 32.

    Darzi (2017).

  33. 33.

    Piel et al. (2018), pp. 594–600.

  34. 34.

    NHS factsheets for health and care staff, Factsheet 1B—Types of data used and legal protection in place (2018), p. 1. Available at: https://digital.nhs.uk/services/national-data-opt-out-programme/guidance-for-health-and-care-staff. Accessed 10 June 2019.

  35. 35.

    Mészáros and Ho (2019), p. 13.

  36. 36.

    Lee et al. (2012), pp. 38–44.

  37. 37.

    For more information behind the NHS plans for the secondary use of health data, before the creation of care.data program, see Department of Health (2006); see also Department of Health (2015).

  38. 38.

    Sterckx and Cockbain (2014), pp. 227–228, Mori (2016).

  39. 39.

    Vezyridis and Timmons (2017), p. 2.

  40. 40.

    The National Data Guardian (NDG) advises and challenges the health and care system in the UK to help ensure that citizens’ confidential information is safeguarded securely and used properly. Available at: https://www.gov.uk/government/organisations/national-data-guardian/about. Accessed 10 June 2019.

  41. 41.

    National Data Guardian for Health and Care (2016), pp. 1–56.

  42. 42.

    National Data Guardian for Health and Care (2016), pp. 6–9.

  43. 43.

    Department of Health and Social Care (2016).

  44. 44.

    Jones et al. (2017), pp. 43–50; Rothstein and Shoben (2013), p. 27.

  45. 45.

    The ICO has ruled the Royal Free NHS Foundation Trust failed to comply with the Data Protection Act when it provided patient details to Google DeepMind. Available at: https://ico.org.uk/about-the-ico/news-and-events/news-and-blogs/2017/07/royal-free-google-deepmind-trial-failed-to-comply-with-data-protection-law/. Accessed 10 June 2019.

  46. 46.

    Stockdale et al. (2018), pp. 1–25; Wyatt et al. (2018), pp. 1–8; Aitken et al. (2016), p. 73.

  47. 47.

    See Streams. Available at: https://deepmind.com/applied/deepmind-health/working-partners/how-were-helping-today/. Accessed 10 June 2019.

  48. 48.

    McGoogan (2017).

  49. 49.

    NHS (National Health Service) Digital is an executive non-departmental public body of the Department of Health in the UK. The NHS Digital is the national provider of information, data and IT systems for commissioners, analysts and clinicians in health and social care. Available at: https://www.gov.uk/government/organisations/nhs-digital/about. Accessed 10 June 2019.

  50. 50.

    NHS Digital: Opting out of sharing your confidential patient information. Available at: https://digital.nhs.uk/about-nhs-digital/our-work/keeping-patient-data-safe/how-we-look-after-your-health-and-care-information/your-information-choices/opting-out-of-sharing-your-confidential-patient-information. Accessed 15 June 2019.

    “Type 1 opt-out: medical records held at your GP practice: You can also tell your GP practice if you do not want your confidential patient information held in your GP medical record to be used for purposes other than your individual care. This is commonly called a type 1 opt-out. This opt-out request can only be recorded by your GP practice.”

  51. 51.

    NHS Digital, Opting out of sharing your confidential patient information. Available at:

    https://digital.nhs.uk/about-nhs-digital/our-work/keeping-patient-data-safe/how-we-look-after-your-health-and-care-information/your-information-choices/opting-out-of-sharing-your-confidential-patient-information. Accessed 10 June 2019.

  52. 52.

    NHS Digital, About the national data opt-out. Available at: https://digital.nhs.uk/services/national-data-opt-out-programme. Accessed 10 June 2019.

  53. 53.

    Article 29 Working Party, Opinion 05/2014 on Anonymisation Techniques (2014), p. 3.

  54. 54.

    Regulation (EU) 2016/679 of the European Parliament of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) [hereinafter “GDPR”]. Recital 26: anonymous information, namely information which does not relate to an identified or identifiable natural person or to personal data rendered anonymous in such a manner that the data subject is not or no longer identifiable.

  55. 55.

    Article 4 (5) of the GDPR: “pseudonymisation” means the processing of personal data in such a manner that the personal data can no longer be attributed to a specific data subject without the use of additional information, provided that such additional information is kept separately and is subject to technical and organizational measures to ensure that the personal data are not attributed to an identified or identifiable natural person.

  56. 56.

    Article 29 Working Party, “Opinion 05/2014 on Anonymization Techniques” (WP216, 10 April 2014), p. 20.

  57. 57.

    National Data Guardian (2013), p. 128. “A clinical, social or public health activity concerned with the prevention, investigation and treatment of illness and the alleviation of suffering of individuals. It includes supporting individuals’ ability to function and improve their participation in life and society. It includes the assurance of safe and high-quality care and treatment through local audit, the management of untoward or adverse incidents, person satisfaction including measurement of outcomes undertaken by one or more registered and regulated health or social care professionals and their team with whom the individual has a legitimate relationship for their care.”

  58. 58.

    NHS (2018) Your Data Matters to the NHS, p. 1. Available at: https://digital.nhs.uk/services/national-data-opt-out-programme/supporting-patients-information-and-resources. Accessed 25 May 2019.

  59. 59.

    Powles and Hodson (2017), pp. 351–367.

  60. 60.

    The ICO has ruled the Royal Free NHS Foundation Trust failed to comply with the Data Protection Act when it provided patient details to Google DeepMind. Available at: https://ico.org.uk/about-the-ico/news-and-events/news-and-blogs/2017/07/royal-free-google-deepmind-trial-failed-to-comply-with-data-protection-law/. Accessed 10 June 2019.

  61. 61.

    NHS (2018) Your Data Matters to the NHS, p. 2. Available at: https://digital.nhs.uk/services/national-data-opt-out-programme/supporting-patients-information-and-resources. Accessed 25 May 2019.

  62. 62.

    NHS (2018) Your Data Matters to the NHS, p. 2. Available at: https://digital.nhs.uk/services/national-data-opt-out-programme/supporting-patients-information-and-resources. Accessed 25 May 2019.

  63. 63.

    Meskó et al. (2018), pp. 1–4.

  64. 64.

    NHS, When your choice does not apply. Available at: https://www.nhs.uk/your-nhs-data-matters/where-your-choice-does-not-apply/. Accessed 10 June 2019.

  65. 65.

    NHS (2018) Your Data Matters to the NHS, p. 2. Available at: https://digital.nhs.uk/services/national-data-opt-out-programme/supporting-patients-information-and-resources. Accessed 10 June 2019.

  66. 66.

    NHS (2018) Your Data Matters to the NHS, p. 2. Available at: https://digital.nhs.uk/services/national-data-opt-out-programme/supporting-patients-information-and-resources. Accessed 10 June 2019.

  67. 67.

    McDonald and Cranor (2009), pp. 543–568.

  68. 68.

    Solove (2013), pp. 1880–1903.

  69. 69.

    NHS (2018) A guide to the national data opt-out for carers. Available at: https://digital.nhs.uk/services/national-data-opt-out-programme/supporting-patients-information-and-resources. Accessed 11 May 2019.

  70. 70.

    Understanding Patient Data, Frequently Asked Questions. Available at: https://understandingpatientdata.org.uk/what-you-need-know. Accessed 23 June 2019.

  71. 71.

    NHS (2018) A guide to the national data opt-out for carers, p. 1. “If you think the person you care for is happy for their information to be shared you don’t need to do anything further.” “If you think that the person you care for would wish to register a national data opt-out, or you are satisfied that registering a national data opt-out is in that person’s best interest then follow step 3.”

  72. 72.

    NHS Digital, statistics on the volumes of national data opt-outs. Available at: https://digital.nhs.uk/data-and-information/publications/statistical/national-data-opt-out. Accessed 12 June 2019.

  73. 73.

    NHS Digital, statistics on the volumes of national data opt-outs, March 2019. Available at:

    https://digital.nhs.uk/data-and-information/publications/statistical/national-data-opt-out/march-2019/ndop-mar19. Accessed 12 June 2019.

  74. 74.

    Type 1 opt-outs have been reported as instances (i.e., number of times the opt-out code occurs within GP records, which may include the same patient recorded at more than one practice), therefore the NHS Digital could not de-duplicate this information.

  75. 75.

    NHS Digital, statistics on the volumes of national data opt-outs, March 2018. Available at:

    https://digital.nhs.uk/data-and-information/publications/statistical/care-information-choices/mi-care-information-choices-england-march-2018. Accessed 16 May 2019.

  76. 76.

    Mill (1859).

  77. 77.

    Corrales and Jurčys (2016), p. 534.

  78. 78.

    Corrales and Jurčys (2016), p. 534.

  79. 79.

    Corrales and Jurčys (2016), p. 534.

  80. 80.

    Bishop (2009), p. 296.

  81. 81.

    Tanner (2007), p. 200; Hartley (2012), p. 70; Angner (2016), p. 264.

  82. 82.

    See, e.g., generally, Jackson (2006), pp. 68–69.

  83. 83.

    Sunstein (2014), pp. 63–99.

  84. 84.

    Corrales and Jurčys (2016), pp. 534–535.

  85. 85.

    Corrales and Jurčys (2016), pp. 534–535.

References

  • Aitken et al (2016) Public responses to the sharing and linkage of health data for research purposes: a systematic review and thematic synthesis of qualitative studies. BMC Med Ethics 17(1):73

    Article  Google Scholar 

  • Angner E (2016) A course in behavioral economics, 2nd edn. MacMillan Education Palgrave, London

    Book  Google Scholar 

  • Angner E, Loewenstein G (2016) Behavioral economics. Elsevier’s handbook of the philosophy of science, vol 5. http://www.cmu.edu/dietrich/sds/docs/loewenstein/BehavioralEconomics.pdf. Accessed 10 Jun 2019

  • Ben-Porath S (2010) Tough choices: structured paternalism and the landscape of choice. Princeton University Press, Princeton

    Book  Google Scholar 

  • Bishop M (2009) Essential economics: an A to Z guide. The Economist, 2nd edn. Bloomberg Press, New York

    Google Scholar 

  • Coggon J, Syrett K, Vienns AM (2017) Public health law: ethics, governance and regulation. Routledge, London

    Google Scholar 

  • Corrales M, Jurčys P (2016) Sunstein, Cass, Why nudge: the politics of libertarian paternalism. New Haven/London: Yale University Press, 2014, 208 pp, pb £10.99. (Book Review). Mod Law Rev 79(3):533–536

    Google Scholar 

  • Corrales M, Kousiouris G (2017) Nudging cloud providers: Improving cloud architectures through intermediary services. In: Corrales M, Fenwick M, Forgó N (eds) New technology, big data and the law. Springer, Singapore

    Chapter  Google Scholar 

  • Corrales M, Jurčys P, Kousiouris G (2019) Smart contracts and smart disclosure: coding a GDPR compliance framework. In: Corrales M, Fenwick M, Haapio H (eds) Legal tech, smart contracts and Blockchain. Springer, Singapore

    Chapter  Google Scholar 

  • Darzi A (2017) There is huge potential to apply behavioral economics in health. https://blogs.bmj.com/bmj/2017/10/16/ara-darzi-there-is-huge-potential-to-apply-behavioural-economics-in-health/. Accessed 10 Jun 2019

  • Davidai S, Gilovich T, Ross L (2012) The meaning of default options for potential organ donors. Proc Natl Acad Sci 109(38):15201–15205

    Article  Google Scholar 

  • Deloitte (2016) International review, Secondary use of health and social care data and applicable legislation. https://media.sitra.fi/julkaisut/Muut/International_review_secondary_use_health_data.pdf. Accessed 10 Jun 2019

  • Department of Health (2006) Best research for best health: a new national health strategy, London

    Google Scholar 

  • Department of Health and Social Care (2016) Written statement to Parliament: review of health and care data security and consent. https://www.gov.uk/government/speeches/review-of-health-and-care-data-security-and-consent. Accessed 10 Jun 2019

  • Detels R, Gulliford M (2015) Oxford textbook of global public health, vol 1, 6th edn. Oxford University Press, Oxford

    Google Scholar 

  • Hamilton D, Zufiaurre B (2014) Blackboards and bootstraps: revisioning education and schooling. Sense Publishers, Rotterdam

    Book  Google Scholar 

  • Hanney S, González-Block M (2015) Health research improves healthcare: now we have the evidence and the chance to help the WHO spread such benefits globally. Health Res Policy Syst 13(12):1–4

    Article  Google Scholar 

  • Hartley D (2012) Education and the culture of consumption: personalisation and the social order. Routledge, London

    Book  Google Scholar 

  • Heshmat S (2015) Addiction: a behavioral economic perspective. Routledge, New York

    Book  Google Scholar 

  • Institute of Medicine (2013) Best care at lower cost: the path to continuously learning health care in America. The Institute of Medicine, National Academy of Sciences, Washington DC

    Google Scholar 

  • Jackson J (2006) Ethics in medicine: virtue, vice and medicine. Polity Press, Cambridge

    Google Scholar 

  • Jamson S (2013) The Role of Acceptance in Behavioral Adaptation. In: Rudin-Brown C, Jamson S (eds) Behavioral adaptation and road safety: theory, evidence and action. CRC Press, Boca Ratón

    Google Scholar 

  • John P et al (2013) Nudge, nudge, think, think: experimenting with ways to change Civic behavior. Bloomsbury, London

    Google Scholar 

  • Jones K et al (2017) The other side of the coin: harm due to the non-use of health-related data. Int J Med Inform 97(2017):43–50

    Article  Google Scholar 

  • Lee LM, Heilig CM, White A (2012) Ethical justification for conducting public health surveillance without patient consent. Am J Public Health 102(1):38–44

    Article  Google Scholar 

  • Leitzel J (2015) Concepts in law and economics: a guide for the curious. Oxford University Press, Oxford

    Book  Google Scholar 

  • McDonald A, Cranor L (2009) The cost of reading privacy policies. I/S: J Law Policy Inf Soc 4(3):543–568

    Google Scholar 

  • McGoogan C (2017) NHS illegally handed Google firm 1.6 m patient records, UK data watchdog finds. https://www.telegraph.co.uk/technology/2017/07/03/googles-deepmind-nhs-misused-patient-data-trial-watchdog-says/. Accessed 10 Jun 2019

  • Meskó B, Hetényi G, Győrffy Z (2018) Will artificial intelligence solve the human resource crisis in healthcare? BMC Health Serv Res 18(545):1–4

    Google Scholar 

  • Mészáros J, Ho C (2019) Big data and scientific research: the secondary use of personal data under the research exemption in the GDPR. Acta Juridica Hungarica (in press)

    Google Scholar 

  • Mill J (1859) On liberty. John W. Parker and Son, London

    Google Scholar 

  • Minton E, Kahle L (2013) Belief systems, religion, and behavioral economics: marketing in multicultural environments. Business Expert Press, New York

    Google Scholar 

  • Mori I (2016) The one-way mirror: public attitudes to commercial access to health data. https://www.ipsos.com/sites/default/files/publication/5200-03/sri-wellcome-trust-commercial-access-to-health-data.pdf. Accessed 10 Jun 2019

  • National Data Guardian for Health and Care (2013) Information: to share or not to share? The Information Governance Review

    Google Scholar 

  • National Data Guardian for Health and Care (2016) Review of data security, consent and opt-outs

    Google Scholar 

  • Piel F et al (2018) The challenge of opt-outs from NHS data: a small-area perspective. J Public Health 40(4):594–600

    Article  Google Scholar 

  • Powles J, Hodson H (2017) Google DeepMind and healthcare in an age of algorithms. Health Technol 7(4):351–367

    Article  Google Scholar 

  • Quigley M, Stokes E (2015) Nudging and Evidence-Based Policy in Europe: Problems of Normative Legitimacy and Effectiveness. In: Alemanno A, Sibony A (eds) Nudge and the law: a European perspective, modern studies in European Law. Hart Publishing, Oxford

    Google Scholar 

  • Rothstein MA, Shoben AB (2013) Does consent bias research? Am J Bioeth 13(4):27

    Article  Google Scholar 

  • Safran C et al (2007) Toward a national framework for the secondary use of health data: an American Medical Informatics Association white paper. J Am Med Inform Assoc 14(1):1–9

    Article  Google Scholar 

  • Shafir E (ed) (2013) The behavioral foundations of public policy. Princeton University Press, Princeton

    Google Scholar 

  • Solove D (2013) Introduction: privacy self-management and the consent dilemma. Harvard Law Rev 126(7):1880–1903

    Google Scholar 

  • Sterckx S, Cockbain J (2014) The UK National Health Service’s ‘Innovation Agenda’: lessons on commercialization and trust. Med Law Rev 22(2):227–228

    Article  Google Scholar 

  • Stockdale J, Cassell J, Ford E (2018) Giving something back: a systematic review and ethical enquiry of public opinions on the use of patient data for research in the United Kingdom and the Republic of Ireland [version 1]. Wellcome Open Res 3:6

    Article  Google Scholar 

  • Stoknes P (2015) What we think about when we try not to think about global warming. Chelsea Green Publishing, Vermont

    Google Scholar 

  • Sunstein C (2014) Why nudge?. Yale University Press, New Haven, The Politics of Libertarian Paternalism

    Google Scholar 

  • Sunstein C (2016) Green by default: how a nudge and wink can save the planet. https://theecologist.org/2016/sep/27/green-default-how-nudge-and-wink-can-save-planet. Accessed 10 Jun 2019

  • Tanner M (2007) Leviathan on the right: how big-government conservatism brought down the Republican Revolution. Cato Institute, Washington, D.C.

    Google Scholar 

  • Thaler R (2009) Opting in vs. opting out. The New York Times. http://www.nytimes.com/2009/09/27/business/economy/27view.html?_r=0. Accessed 10 Jun 2019

  • Thaler R, Sunstein C (2009) Nudge: improving decisions about health, wealth, and happiness. Penguin Group, New York

    Google Scholar 

  • Vezyridis P, Timmons S (2017) Understanding the care.data conundrum: new information flows for economic growth. Big Data Soc 4(1):2

    Google Scholar 

  • Willis O (2015) Behavioral economics for better decisions, ABC.net. https://www.abc.net.au/radionational/programs/allinthemind/better-life-decisions-with-behavioural-economics/6798918. Accessed 10 Jun 2019

  • Wyatt D, Cook J, McKevitt C (2018) Perceptions of the uses of routine general practice data beyond individual care in England: a qualitative study. BMJ Open 8(1):1–8

    Article  Google Scholar 

  • Zamir E (2015) Law, psychology, and morality: the role of loss aversion. Oxford University Press, Oxford

    Google Scholar 

  • Zeiler K, Teitelbaum J (eds) (2015) Research handbook on behavioral law and economics. Edward Elgar Publishing, Northampton

    Google Scholar 

Download references

Acknowledgements

This research is supported by a Novo Nordisk Foundation grant for a scientifically independent Collaborative Research Program in Biomedical Innovation Law (grant agreement number NNF17SA0027784) and the Multidisciplinary Health Cloud Research Program: Technology Development and the Application of Big Health Data. Academia Sinica, Taipei, Taiwan.

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Meszaros, J., Ho, Ch., Corrales Compagnucci, M. (2020). Nudging Consent and the New Opt-Out System to the Processing of Health Data in England. In: Corrales Compagnucci, M., Forgó, N., Kono, T., Teramoto, S., Vermeulen, E.P.M. (eds) Legal Tech and the New Sharing Economy. Perspectives in Law, Business and Innovation. Springer, Singapore. https://doi.org/10.1007/978-981-15-1350-3_5

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