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Journal of Scientific Information Research

Keywords

medical health information, information sensitivity, privacy preference, contextual integrity theory, discrete choice experiment, binary logistic regression model, sensitive information protection

Abstract

[Purpose/significance] This study investigates differences in digital medical-health information privacy preferences by measuring privacy sensitivity across diverse user groups in multiple contextual scenarios, analyzing variations both within user groups (across contexts) and between user groups (within the same context), thereby providing actionable insights for developing context-aware health information protection frameworks in digital healthcare services.

[Method/process] A model of users' sensitivity to digital medical health information was constructed based on contextual integrity theory, and an experimental study was conducted using a discrete choice experiment method. The model was verified and analyzed using a binary logistic regression model.

[Result/conclusion] The study shows that in the information subject dimension, highly educated users are more willing to pay extra fees to choose platforms with a higher level of privacy protection or the ability to set a higher level of privacy on their own compared to less educated users. Regarding communication principles, users prefer platforms that offer transparent and easily comprehensible privacy policies. In terms of information types, users tend to safeguard their health status information and payment data while being willing to share basic personal information and medical application data to a certain extent in exchange for more convenient healthcare services or health management solutions.

First Page

1

Last Page

12

Submission Date

April 2025

Revision Date

May 2025

Acceptance Date

May 2025

Publication Date

October 2025

Digital Object Identifier (DOI)

ꎺ 10.19809/j.cnki.kjqbyj.2025.04.001

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