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University of Toronto

Structural Limitations of Data Protection Legislation for the Learning Health System: Proposing a Lex Specialis for Longitudinal Biomedical Data Use

Abstract

dc:description.abstract

Data protection legislation imposes presumptive limitations on the capacity of institutions to collect, use, and disclose personal information to safeguard individual interests such as informational self-determination and privacy. This legislation has shown itself ill-suited to the regulation of emergent data-driven activities in the health sector, including public health surveillance, biomedical research, and personalised medicine. These difficulties are accentuated in transnational and intersectoral efforts, due to the heightened legal compliance challenges that arise from the simultaneous application of multiple laws. To alleviate these difficulties, a novel legal paradigm of data stewardship is proposed. A model of longitudinal information stewardship in reliance on appropriate organisational structures, expert oversight, and technical safeguards is proposed as an alternative to data protection law. Such a model should be enacted through special purpose health-sector legislation to align individual privacy and the pro-social use of health-related personal information in the public interest.

Degree

thesis:*
Department dc:contributor.department
Law
Year dc:date.issued
2021

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Bernier, Alexander
Advisor dc:contributor.advisor
  • Austin, Lisa

Subjects

dc:subject × 6

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1807/125525
OAI identifier oai:identifier
oai:utoronto.scholaris.ca:1807/125525

Chain of custody

source
Harvested from
University of Toronto
Base URL
utoronto.scholaris.ca/server/oai/request
Last updated
2026-07-27
Source record
OAI-PMH GetRecord
citation

Bernier, Alexander. Structural Limitations of Data Protection Legislation for the Learning Health System: Proposing a Lex Specialis for Longitudinal Biomedical Data Use. 2021. http://hdl.handle.net/1807/125525