Sovereign Digital Consent through Privacy Impact Quantification and Dynamic Consent

Appenzeller, Arno; Hornung, M.; Kadow, Thomas; Krempel, Erik; Beyerer, Jürgen

doi:10.3390/technologies10010035

Cited by 6 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A discernible trend toward the implementation of DC systems utilizing blockchain technology has been identified. With two exceptions [ 52 , 56 ] that put forth a web-based DC system, the majority of the articles examined the application of blockchain technology. Permissioned blockchain was mostly utilized, rather than permissionless public blockchain.…”

Section: Resultsmentioning

confidence: 99%

Identifying facilitators of and barriers to the adoption of dynamic consent in digital health ecosystems: a scoping review

Lee,

Koo,

Kim

et al. 2023

BMC Med Ethics

View full text Add to dashboard Cite

Background Conventional consent practices face ethical challenges in continuously evolving digital health environments due to their static, one-time nature. Dynamic consent offers a promising solution, providing adaptability and flexibility to address these ethical concerns. However, due to the immaturity of the concept and accompanying technology, dynamic consent has not yet been widely used in practice. This study aims to identify the facilitators of and barriers to adopting dynamic consent in real-world scenarios. Methods This scoping review, conducted in December 2022, adhered to the PRISMA Extension for Scoping Reviews guidelines, focusing on dynamic consent within the health domain. A comprehensive search across Web of Science, PubMed, and Scopus yielded 22 selected articles based on predefined inclusion and exclusion criteria. Results The facilitators for the adoption of dynamic consent in digital health ecosystems were the provision of multiple consent modalities, personalized alternatives, continuous communication, and the dissemination of up-to-date information. Nevertheless, several barriers, such as consent fatigue, the digital divide, complexities in system implementation, and privacy and security concerns, needed to be addressed. This study also investigated current technological advancements and suggested considerations for further research aimed at resolving the remaining challenges surrounding dynamic consent. Conclusions Dynamic consent emerges as an ethically advantageous method for digital health ecosystems, driven by its adaptability and support for continuous, two-way communication between data subjects and consumers. Ethical implementation in real-world settings requires the development of a robust technical framework capable of accommodating the diverse needs of stakeholders, thereby ensuring ethical integrity and data privacy in the evolving digital health landscape.

show abstract

Section: Resultsmentioning

confidence: 99%

Identifying facilitators of and barriers to the adoption of dynamic consent in digital health ecosystems: a scoping review

Lee,

Koo,

Kim

et al. 2023

BMC Med Ethics

View full text Add to dashboard Cite

show abstract

Blockchain-Based Dynamic Consent for Healthcare and Research

Charles

2023

Future of Business and Finance

View full text Add to dashboard Cite

Data sovereignty requirements for patient-oriented AI-driven clinical research in Germany

Radic,

Busch-Casler,

Vosen

et al. 2024

Ethik Med

View full text Add to dashboard Cite

Background The rapidly growing quantity of health data presents researchers with ample opportunity for innovation. At the same time, exploitation of the value of Big Data poses various ethical challenges that must be addressed in order to fulfil the requirements of responsible research and innovation (Gerke et al. 2020; Howe III and Elenberg 2020). Data sovereignty and its principles of self-determination and informed consent are central goals in this endeavor. However, their consistent implementation has enormous consequences for the collection and processing of data in practice, especially given the complexity and growth of data in healthcare, which implies that artificial intelligence (AI) will increasingly be applied in the field due to its potential to unlock relevant, but previously hidden, information from the growing number of data (Jiang et al. 2017). Consequently, there is a need for ethically sound guidelines to help determine how data sovereignty and informed consent can be implemented in clinical research. Methods Using the method of a narrative literature review combined with a design thinking approach, this paper aims to contribute to the literature by answering the following research question: What are the practical requirements for the thorough implementation of data sovereignty and informed consent in healthcare? Results We show that privacy-preserving technologies, human-centered usability and interaction design, explainable and trustworthy AI, user acceptance and trust, patient involvement, and effective legislation are key requirements for data sovereignty and self-determination in clinical research. We outline the implications for the development of IT solutions in the German healthcare system.

show abstract

Sovereign Digital Consent through Privacy Impact Quantification and Dynamic Consent

Cited by 6 publications

References 26 publications

Identifying facilitators of and barriers to the adoption of dynamic consent in digital health ecosystems: a scoping review

Identifying facilitators of and barriers to the adoption of dynamic consent in digital health ecosystems: a scoping review

Blockchain-Based Dynamic Consent for Healthcare and Research

Data sovereignty requirements for patient-oriented AI-driven clinical research in Germany

Contact Info

Product

Resources

About