Designing Value-Centered Consent Interfaces: A Mixed-Methods Approach to Support Patient Values in Data-Sharing Decisions

In the digital health domain, ethical data collection practices are crucial for ensuring the availability of quality datasets that drive medical advancement. Data donation, allowing patients to share their medical data for secondary research purposes, presents a promising resource for such datasets. Yet, current consent interfaces mediating data-sharing decisions are found to favor data collectors’ values over those of data subjects. This raises ethical concerns about the use of data collected, as well as concerning the quality of the resulting datasets. Seeking to establish patient-centered data collection practices in digital health, we investigate the design of consent interfaces that support end-users in making value-congruent health data-sharing decisions. Focusing our research efforts on the situated context of health data donation at the psychosomatic unit of a German university hospital, we demonstrate how a human-centered design can ground technology within the perspective of a vulnerable group. We employed an exploratory sequential mixed-method approach consisting of five phases: (1) Participatory workshops elicit patient values, informing the (2) design of a proposed Value-Centered Consent Interface. An (3) online experiment demonstrates our interface element’s effect, increasing value congruence in data-sharing decisions. Our proposed consent user interface design is then adapted to the research context through a (4) co-creation workshop with domain experts and (5) a user evaluation with patients. Our work contributes to recent discourse in CSCW concerning ethical implications of new data practices within their socio-technological context by exploring patient values on medical data-sharing, introducing a novel consent interface leveraging reflection to support value-congruent decision-making, and providing a situated evaluation of the proposed consent interface with patients.

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https://hci.top/en/papers/cscw/211536/2025

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2025
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