Consent to recontact for future research using linked primary healthcare data: Outcomes and general practice perceptions from the ATHENA COVID-19 study
Research Article
Kim Greaves, Amanda King, Zoltan Bourne, Jennifer Welsh, Mark Morgan, Maria Ximena Tolosa, Trisha Johnston, Carissa Bonner, Tony Stanton, Rosemary Korda
Clinical Trials, 29 December 2024
Abstract
Background
The ATHENA COVID-19 study was set up to recruit a cohort of patients with linked health information willing to be recontacted in future to participate in clinical trials and also to investigate the outcomes of people with COVID-19 in Queensland, Australia, using consent. This report describes how patients were recruited, their primary care data extracted, proportions consenting, outcomes of using the recontact method to recruit to a study, and experiences interacting with general practices requested to release the primary care data.
Methods
Patients diagnosed with COVID-19 from 1 January 2020 to 31 December 2020 were systematically approached to gain consent to have their primary healthcare data extracted from their general practice into a Queensland Health database and linked to other datasets for ethically approved research. Patients were also asked to consent to allow future recontact to discuss participation in clinical trials and other research studies. Patients who consented to recontact were later approached to recruit to a long-COVID study. Patients’ general practices were contacted to export the patient files. All patient and general practice interactions were recorded. Outcome measures were proportions of patients consenting to data extraction and research, permission to recontact, proportions of general practices agreeing to participate. A thematic analysis was conducted to assess attitudes regarding export of healthcare data, and the proportions consenting to participate in the long-COVID study were also reported.
Results
Of 1212 patients with COVID-19, contact details were available for 1155; 995 (86%) were successfully approached, and 842 (85%) reached a consent decision. Of those who reached a decision, 581 (69%), 615 (73%) and 629 (75%) patients consented to data extraction, recontact, and both, respectively. In all, 382 general practices were contacted, of whom 347 (91%) had an electronic medical record compatible for file export. Of these, 335 (88%) practices agreed to participate, and 12 (3%) declined. In total, 526 patient files were exported. The majority of general practices supported the study and accepted electronic patient consent as legitimate. For the long-COVID study, 376 (90%) of those patients recontacted agreed to have their contact details passed onto the long-COVID study team and 192 (53%) consented to take part in their study.
Conclusion
This report describes how primary care data were successfully extracted using consent, and that the majority of patients approached gave permission for their healthcare information to be used for research and be recontacted. The consent-to-recontact concept demonstrated its effectiveness to recruit to new research studies. The majority of general practices were willing to export identifiable patient healthcare data for linkage provided consent had been obtained.
Improving the compliance of informed consent documentation for expanded access patients
Improving the compliance of informed consent documentation for expanded access patients
Elias Samuels, Misty Gravelin, Ellen Champagne, Haj Ghaffari Dorsa, Jeanne Wright
Journal of Clinical and Translational Science, April 2025
Abstract
Objectives/Goals
The informed consent (IC) process is similar between clinical trials and expanded access (EA), which allows clinical use of investigational products outside studies. Physicians face unique barriers to IC in clinical environments. This project assesses IC documentation, identifies potential barriers, and evaluates efforts to improve compliance.
Methods/Study Population
This is a continuous quality improvement project. To assess the compliance of IC processes for EA patients, informed consent documents signed by EA patients in 2023 were collected and reviewed against institutional standards. Five components of each form were evaluated, and the number and type of noncompliant documentation were tracked. Five physicians who provided EA treatments in 2023 were interviewed and the transcripts were analyzed to identify barriers to physician’s and teams’ IC processes. Efforts made to address these barriers and improve the compliance of informed consent documentation are being tracked and trends in compliance are being evaluated.
Results/Anticipated Results
Sixty seven (67) signed informed consent documents for EA treatments were systematically reviewed and 34% were found to be compliant in all key aspects assessed. Analyses of interview notes, transcripts, and memos identified barriers to informed consent processes for expanded access treatments, including the infrequent or irregular occurrence of EA treatments making it difficult for care teams to develop and maintain their understanding of IC process and resources. Efforts made to improve compliance by pre-populating available information into informed consent documentation and removing unnecessary boxes in these forms may have driven improvement in compliance with further efforts underway.
Discussion/Significance of Impact
This project evaluated the compliance of IC documentation for EA treatments and identified drivers affecting physicians’ IC processes for these patients. Different strategies to improve the compliance of IC documentation were evaluated and potential best practices for EA support were identified.
Enhancing informed consent in oncological surgery through digital platforms and artificial intelligence
Enhancing informed consent in oncological surgery through digital platforms and artificial intelligence
Review Article
Alex Boddy
Clinical Surgical Oncology, June 2025
Open Access
Abstract
Informed consent is a cornerstone of ethical medical practice, particularly in high-stakes oncological surgery where treatment options are complex and risks are significant. This paper explores the potential of digital platforms and artificial intelligence (AI) to enhance the informed consent process. The traditional consent process, reliant on face-to-face interactions and paper-based documentation, is increasingly being supplemented by digital solutions that offer remote consultations, personalized patient information, and electronic consent forms. These digital pathways not only improve accessibility and patient comprehension but also streamline documentation, reducing errors and administrative burdens. AI technologies, including ambient digital scribes and large language models (LLMs), could further augment this process by generating personalized risk assessments, simplifying complex medical information, and facilitating multilingual communication. However, success will also depend on addressing ethical concerns, ensuring equitable access, and preserving the irreplaceable human connection between patients and clinicians. By augmenting rather than replacing clinician expertise, digital platforms and AI can empower patients to make truly informed decisions in oncological care.
The Digital Double: Data Privacy, Security, and Consent in AI Implants
The Digital Double: Data Privacy, Security, and Consent in AI Implants
Research Article
Omid Panahi, Soren Falkner
Digital Journal of Engineering Science and Technology, 17 March 2025
Open Access
Abstract
Artificial intelligence (AI) implants are rapidly emerging as a transformative technology with the potential to revolutionize healthcare, enhance human capabilities, and blur the boundaries between humans and machines. However, the integration of AI into the human body raises complex ethical, legal, and social questions, particularly concerning data privacy, security, and consent. This paper explores the concept of the “digital double,” a virtual representation of an individual generated from the data collected by AI implants. It examines the potential benefits and risks of creating and utilizing digital doubles, focusing on the implications for data privacy, security, and informed consent. The paper analyses the challenges of protecting sensitive health information, ensuring data security, and obtaining meaningful consent from individuals with AI implants. It also discusses the potential for misuse and abuse of digital doubles, including unauthorized access, surveillance, and discrimination. Finally, the paper proposes a framework for addressing these challenges, emphasizing the need for robust data protection measures, transparent consent processes, and ethical guidelines to safeguard individual autonomy and privacy in the age of AI implants.
Enabling Demonstrated Consent for Biobanking with Blockchain and Generative AI
Editor’s Note:
The following Barnes et al. article “ Enabling Demonstrated Consent for Biobanking with Blockchain and Generative AI” has been previously shared in this digest. We are sharing it again as this target article in the American Journal of Bioethics has resulted in a number of peer commentaries which follow below. These commentaries offer a range of perspectives on biobanking, blockchain and generative AI and consent. These are areas which we continue to examine in our work.
Enabling Demonstrated Consent for Biobanking with Blockchain and Generative AI
Caspar Barnes, Mateo Riobo Aboy, Timo Minssen, Jemima Winifred Allen, Brian D. Earp, Julian Savulescu
The American Journal of Bioethics, 5 November 2024
Abstract
Participation in research is supposed to be voluntary and informed. Yet it is difficult to ensure people are adequately informed about the potential uses of their biological materials when they donate samples for future research. We propose a novel consent framework which we call “demonstrated consent” that leverages blockchain technology and generative AI to address this problem. In a demonstrated consent model, each donated sample is associated with a unique non-fungible token (NFT) on a blockchain, which records in its metadata information about the planned and past uses of the sample in research, and is updated with each use of the sample. This information is accessible to a large language model (LLM) customized to present this information in an understandable and interactive manner. Thus, our model uses blockchain and generative AI technologies to track, make available, and explain information regarding planned and past uses of donated samples.
Demonstrated Consent and the Common Good: On Withdrawal of Consent in Stem Cell Research
Demonstrated Consent and the Common Good: On Withdrawal of Consent in Stem Cell Research
Open Peer Commentaries
Tijs Rosema, Martine de Vries, Hanna Lammertse, Roland Bertens, Nienke de Graeff
American Journal of Bioethics, 7 April 2025
Excerpt
Barnes et al. (Citation2025) argue that demonstrated consent enhances donor autonomy. This is because demonstrated consent offers donors “ongoing accessibility of information according to donor preferences” and so gives donors “actionable rights to reassess or withdraw consent” (Barnes et al. Citation2025, 99).
Since demonstrated consent uses broad consent as default, it allows researchers to conduct various research projects based on a single initial consent procedure, and so helps contribute to societal interests (Barnes et al. Citation2025). Therefore, demonstrated consent meets the so-called “balance criterion” which Barnes et al. (Citation2025) introduced to underline that informed consent frameworks should also balance donor autonomy with broader societal interests, including progress in science and medicine.
But what does the balance criterion imply for situations in which donor autonomy leads to significant negative consequences for societal interests? This question may arise when donors withdraw their consent. By taking stem cell research as an example, we reason that although demonstrated consent enhances donor autonomy, the exercise of donor autonomy by withdrawing consent should not always lead to the discontinuation of research.
We argue that the right of withdrawal can be limited in stem cell research if a donor is properly informed about limits of withdrawal when providing initial consent. Additionally, we see opportunities for demonstrated consent to compensate for this proposed limitation of donor autonomy. We thus provide a more detailed elaboration on demonstrated consent and the balance criterion in the context of stem cell research…
On the Complexities of Enabling Demonstrated Consent
On the Complexities of Enabling Demonstrated Consent
Open Peer Commentaries
Panagiotis Alexiou, Joel Azzopardi, Claude Julien Bajada, Jean-Paul Ebejer, Gillian M. Martin, Nikolai Paul Pace
American Journal of Bioethics, 7 April 2025
Excerpt
Barnes et al. (Citation2025) introduce a novel vision for biobanking consent in their article “Enabling Demonstrated Consent for Biobanking with Blockchain and Generative AI.” Their concept of “demonstrated consent” leverages blockchain technology and non fungible tokens, along with large language models to enhance transparency and participant engagement. We put this novel framework in context of existing approaches, and highlight some key questions that arise.
The central question that Barnes et al. seek to address is the inherent limitations of the following two traditional consent models in biobanking, namely:
- Study-specific consent: A type of consent where individuals give permission for the use of their bio-samples and data to be used in a single, well defined, research project. While ethically robust, it is often impractical in the context of long-term biobanking due to the increased administrative burden and inflexibility.
- Broad consent: A type of consent where individuals allow their bio-samples and data to be used in future, sometimes unspecified, research projects, with few or no specific restrictions. Importantly, without the need to be re-contacted or consulted. This approach is more efficient, but can undermine the autonomy of participants by failing to provide sufficient information about future research uses. Broad consent needs to be paired with strategies of risk mitigation, and continuous provision of information to participants.
In response to these challenges, various alternative models have been proposed, including tiered informed consent (Tiffin Citation2018), meta-consent (Ploug and Holm Citation2016), and dynamic consent (Kaye et al. Citation2015; Budin-Ljøsne et al. Citation2017) These models try to increase the involvement of participants, but are vulnerable to issues similar to study-specific consent. Dynamic consent in particular, has gained traction as a means of allowing participants to dynamically update their consent preferences in real time, thus tailoring their participation to studies based on their preferences. Individuals however, have to constantly manage their consent, leading to potential choice overload and consent fatigue. The “demonstrated consent” model proposed by Barnes et al. aims to circumvent these issues by providing a secure, transparent, and easily accessible source of information, without requiring participants to continuously manage their preferences. The central contribution of this manuscript is the proposed integration of non-fungible tokens (NFTs) and large language models (LLMs) to tackle these issues…
Challenges to Demonstrated Consent in Biobanking: Technical, Ethical, and Regulatory Considerations
Challenges to Demonstrated Consent in Biobanking: Technical, Ethical, and Regulatory Considerations
Open Peer Commentaries
Jasmine E. McNealy, Megan Doerr
American Journal of Bioethics, 7 April 2025
Excerpt
We read with interest Barnes and colleagues’ recent article, “Enabling Demonstrated Consent for Biobanking with Blockchain and Generative AI” (Barnes et al. 2025). We appreciate their efforts to succinctly ground their proposal within consent scholarship and their distillation of the ethical challenges of informed consent for repository contexts. Like many, we are vocal advocates for improving the informed consent process, especially within repository enabled research (Doerr et al. 2021). We also strongly support the creative use of technology to mitigate consent’s shortcomings (Moore et al. 2017; Kraft and Doerr 2018). However, we are concerned that Barnes et al.’s proposal faces several critical technical challenges to implementation, does not account for key features of repository enabled research, and adds novel regulatory concerns to the mix…
Narrative Transparency in AI-Driven Consent
Narrative Transparency in AI-Driven Consent
Open Peer Commentaries
Jarrel De Matas, Jiefei Wang, Vibhuti Gupta
American Journal of Bioethics, 7 April 2025
Excerpt
As artificial intelligence (AI) systems become more prevalent, ethical inquiry into transparency, trust, and patient autonomy must develop with similar pace. One area where such inquiry required is in the process of obtaining informed consent, particularly in a biobanking context, where participants are asked to share their biological data for research purposes. Although Barnes et al. (Citation2025) proposes using blockchain and AI to improve transparency and engagement in biobanking through demonstrated consent, their approach lacks a concrete framework: informed consent should not only be considered a transactional process, as Manson and O’Neill (Citation2007) argue, but more importantly a user-centered, communicative act that requires participants to understand complex information, balance risks and benefits, and make decisions that overlap with their values and preferences. To complement what we identify in Barnes et al. (Citation2025) as an overstatement of the transactional approach to informed consent, we suggest a Narrative Transparency Framework. This framework applies storytelling principles to drive AI-assisted consent processes and aims to improve decision-making, enhance understanding, and foster trust by enhancing personalized, ethically framed, and user-adaptive narratives. In this paper, we explore the theoretical basis of narrative transparency which is premised on the role of narrative structure in shaping participant understanding and decision-making. We also outline the components of the Narrative Transparency Framework and discuss practical strategies for utilizing narrative-driven AI consent interactions…
Consent Is Dead, Long Live Ethical Oversight: Integrating Ethically Sourced Data into Demonstrated Consent Models
Consent Is Dead, Long Live Ethical Oversight: Integrating Ethically Sourced Data into Demonstrated Consent Models
Open Peer Commentaries
Jean-Christophe Bélisle-Pipon, Vardit Ravitsky
American Journal of Bioethics, 7 April 2025
Excerpt
Barnes et al. (Citation2025) propose a demonstrated consent model that seeks to address challenges in modern biomedicine by transforming consent from a static, one-time transaction into a dynamic process. Their model integrates blockchain technology with generative artificial intelligence (AI) to allow donors to monitor the use of their biological samples in real time and adjust their preferences as research evolves. This approach helps to respond to the limitations of traditional consent frameworks—a concern echoed by Evans and Bihorac (Citation2024), who note that “informed consent for data use, as conceived in the 1970s, seems dead.” They argue that modern computational methods introduce privacy risks not only through direct data breaches but also via inferences drawn from aggregated data, affecting even those who have not directly consented. Barnes and colleagues’ model embeds increased transparency and user agency into consent processes. However, it also raises ethical questions: Does this approach truly empower donors, or might it overwhelm them with technical complexity? Can blockchain’s transparency and AI’s capacity to personalize consent overcome systemic inequities, or will they obscure deeper structural imbalances? These questions are essential to assessing whether demonstrated consent can adequately safeguard autonomy, privacy, and justice in biomedical research…
Center for Informed Consent Integrity 