Start Date
10-12-2017 12:00 AM
Description
The rapidly growing use of healthcare wearable devices has spurred the increased supply of personal health information. However, in practice, privacy issues are key challenges since these devices are mutually connected to share data, which have not been adequately examined in the literature. This paper provides a new framework for sharing participants’ health information by considering the individuals’ own decision-making process, which meets individual privacy boundaries. Information Boundary Theory is used to explain the psychological process of how privacy-safe features encourage users to share their health information with appropriate privacy controls. Specifically, this paper establishes a new link between QS (Quantified-Self) data and online healthcare data, based on privacy sharing decisions. It posits that the privacy controls will bring about higher levels of physiological comfort. The proposed framework will motivate a higher level of users' longitudinal health record sharing, and as a result, better medical decisions can be made.
Recommended Citation
Shin, Jieun, "Patient Privacy Decision Making in the Health Big Data Era" (2017). ICIS 2017 Proceedings. 17.
https://aisel.aisnet.org/icis2017/IT-and-Healthcare/Presentations/17
Patient Privacy Decision Making in the Health Big Data Era
The rapidly growing use of healthcare wearable devices has spurred the increased supply of personal health information. However, in practice, privacy issues are key challenges since these devices are mutually connected to share data, which have not been adequately examined in the literature. This paper provides a new framework for sharing participants’ health information by considering the individuals’ own decision-making process, which meets individual privacy boundaries. Information Boundary Theory is used to explain the psychological process of how privacy-safe features encourage users to share their health information with appropriate privacy controls. Specifically, this paper establishes a new link between QS (Quantified-Self) data and online healthcare data, based on privacy sharing decisions. It posits that the privacy controls will bring about higher levels of physiological comfort. The proposed framework will motivate a higher level of users' longitudinal health record sharing, and as a result, better medical decisions can be made.