Dynamic Consent – A Patient Interface for 21st Century Research Networks

By J. Kaye, Edgar A. Whitley, David Lund, Michael Morrison, Harriet J. A. Teare, Karen Melham. European Journal of Human Genetics, 2014

676 citations recorded by the source corpus.

Biomedical research is being transformed through the application of information technologies that allow ever greater amounts of data to be shared on an unprecedented scale. However, the methods for involving participants have not kept pace with changes in research capability. In an era when information is shared digitally at the global level, mechanisms of informed consent remain static, paper-based and organised around national boundaries and legal frameworks. Dynamic consent (DC) is both a specific project and a wider concept that offers a new approach to consent; one designed to meet the needs of the twenty-first century research landscape. At the heart of DC is a personalised, digital communication interface that connects researchers and participants, placing participants at the heart of decision making. The interface facilitates two-way communication to stimulate a more engaged, informed and scientifically literate participant population where individuals can tailor and manage their own consent preferences. The technical architecture of DC includes components that can securely encrypt sensitive data and allow participant consent preferences to travel with their data and samples when they are shared with third parties. In addition to improving transparency and public trust, this system benefits researchers by streamlining recruitment and enabling more efficient participant recontact. DC has mainly been developed in biobanking contexts, but it also has potential application in other domains for a variety of purposes.

Subfields: Health Informatics, Public Health, Environmental and Occupational Health, Safety Research

View Paper: https://www.semanticscholar.org/paper/68b92d0e13c5128aaf7a909e3ff8bf483b348e28

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In Health Informatics

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Citations in the corpus

How to Design AI for Social Good: Seven Essential Factors (2020, background): “Another alternative is the evolving notion of “dynamic consent”, whereby individuals can monitor and adjust their privacy preferences on a granular level (Kaye et al. 2015).”