AI, Big Data, e-Health and the Right to be Forgotten

Research output: Chapter in Book/Report/Conference proceedingBook chapter


title = "AI, Big Data, e-Health and the Right to be Forgotten",
abstract = "This paper examines the rights of users of health, fitness and wellness e-services concerning the use, re-use and conservation of their health and health related data. In particular, it will analyse the scope and limits of the {\textquoteleft}right to be forgotten{\textquoteright} recently codified under Article 17 in the General Data Protection Regulation. It will address generally patience records and clinical trial data, and focus on health and health related data provided outside a traditional health care and medical research setting. Big data, machine learning and artificial intelligence promise to enable great advances in personalised medicine, public health and generally in bio-medical research. Already we start to see a proliferation of e-health, e-fitness and e-wellness services. These are emerging inserted in strategies to optimise and personalize public health services, in particular in the areas of general health information and pre-clinical advice, emergency services triage, pre-screening, long-term monitoring of patients, elderly care, etc. Some types of e-health services start to be offered by or subcontracted to private entities, such as insurance companies, private clinics, long term care facilities and e-health care providers. Also in the market, we can already observe emerging a long list of other e-services with health relevance, offered by a variety of heterogeneous commercial enterprises with different goals and business models, but which include inter alia genetic testing, biometric data monitoring, sample analyses and health, wellness and fitness questionnaires. Mostly data flows multidirectional, using a combination of testing devises, {\textquoteleft}apps{\textquoteright} and machine learning tools. Physiologic and psychologic health indicators can also be retrieved from social networks, using e.g. language, speech pattern and biometrics analysis. Digitalization of medical journals, biobanks, clinical trials and medical research, also offers possibilities for personalised and precision medicine, public health decision making and bio-pharmaceutical innovation. Since most non-communicable diseases are caused, aggravated or mitigated by social-economic conditions, life-style, cultural factors and socialization habits, comprehensive health data will likely tend to include highly private information. In theory, in the near future it will be possible to predict, detect and prevent or treat at an early stage an increase number of health conditions. It will also be possible to more accurately tailor treatments and health interventions to specific individual needs. As recently stressed by the EU Parliament in the resolution on the fundamental rights implications of big data, public health interest in creation and access to health big data will necessarily interface and need to be balanced with the protection of other fundamental rights. Such will be particularly pertinent to consider in cases where the supply and/or creation of such data involves commercial entities and other actors operating outside the scope of the traditional confidentiality protected relationship between a patient and a health care professional or medical researcher. ",
keywords = "Health law, Right to be forgotten, GDPR, Data Protection, Machine Learning, Big data, H{\"a}lsor{\"a}tt, GDPR, Dataskydd",
author = "Ana Nordberg",
year = "2020",
month = nov,
language = "English",
isbn = "9788205531963",
pages = "275",
editor = "Befring, {Anne Kjersti} and Inger-Johanne Sand",
booktitle = "Kunstig intelligens og big data i helsesektoren",
publisher = "Gyldendal Norsk Forlag A/S",
address = "Norway",