Legal provision for the use of big data in medicine

Abstract

The use of big data in medical practice is a promising direction in the development of digital personalised medicine. Legal regulation of the use of big data in medical practice is a relevant issue. Legal regulation of the use of big data is based on the need to ensure the confidentiality of medical data related to consequentialist (risks of negative consequences of dissemination of confidential information) and deontological (protection of confidential information as an independent value) concerns. The purpose of our review is to summarise the issues arising from the point of view of legal regulation of the use of big data in medical practice and approaches to legal provision of the use of big data in medicine. Material and methods. The analysis of the main foreign and domestic sources was carried out according to the RINC, Google Scholar, Science Direct, PubMed databases for the past 10 years. Results. The main foreign models of legal regulation of big data use in medical practice have been analysed. The American model of legal regulation, stipulated in the HIPAA Privacy Rules (The Health Insurance Portability and Accountability Act Privacy Rule), is mainly focused on prevention of negative consequences of confidential medical data dissemination, due to which it is more open to innovation, but still contains some gaps in regulation. The European model of legal regulation, stipulated in the General European Regulation on the Protection of Personal Data (GDPR), is based on the protection of medical data as an independent value, which may not always be effective in terms of the use of big data in medical practice. The Russian regulation of big data use in medical practice is more similar to the European regulation and is also currently not sufficiently adapted for the use of big data in regular medical practice. Conclusion. In Russia there is a number of legal initiatives aimed at forming a legal foundation for the use of big data in medical practice.

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