Nudging Consent and the New Opt Out System to the Processing of Health Data in England
<|reference_start|>Nudging Consent and the New Opt Out System to the Processing of Health Data in England: 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 very 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 other kinds of sensitive data and failed to comply with data protection law. Since May 2018, the government has launched the national data opt out system with the hope of regaining public trust. Nevertheless, there are 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.<|reference_end|>
@article{meszaros2024nudging,
    title={Nudging Consent and the New Opt Out System to the Processing of Health
  Data in England},
    author={Janos Meszaros, Chih-hsing Ho and Marcelo Corrales Compagnucci},
    journal={arXiv preprint arXiv:2407.19447},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19447},
    primaryClass={cs.CY cs.AI}
}