Previously submitted to: JMIR AI (no longer under consideration since May 11, 2024)
Date Submitted: Apr 7, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Practical Steps in Implementing Privacy Measures with Synthetic Health Data
ABSTRACT
Privacy concerns related to the use of sensitive personal healthcare information is a consistent concern for innovators in both academic and industrial sectors as a barrier to healthcare data access. Synthetic data (new data generated from the original data) is becoming one of the approaches that innovators use to reduce privacy concerns while conducting research or building translational tools. Synthetic data serve to replicate the patterns within the original data, without containing the personal information of “real” participants. In this article, we discuss the importance of collaboration between industry, academia, and legislative bodies to address the pervasive challenge of privacy concerns associated with the use of sensitive personal healthcare information. Synthetic data represents a nexus for academia, industry, and lawmakers, which offers a compelling solution for innovations in healthcare if done through a pragmatic lens.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.