Cancer Patient Perspectives on Clinical Trial Discussion and Informed Consent Through Telemedicine

Cancer Patient Perspectives on Clinical Trial Discussion and Informed Consent Through Telemedicine
Original Reports
Yasin Khadem Charvadeh, Sahil D. Doshi, Kenneth Seier, Erin M. Bange, Bobby Daly, Allison Lipitz-Snyderman, Fernanda C.G. Polubriaginof, Michael Buckley, Gilad Kuperman, Peter D. Stetson, Deb Schrag, Michael J. Morris, Katherine S. Panageas
JSO Oncology Practice, 18 March 2025
Abstract
Purpose
Clinical trials are integral for patients with cancer but remain inaccessible to many because of barriers including geographic and transportation challenges. This study aimed to evaluate cancer patients’ preferences for telemedicine versus in-person visits for clinical trial discussions and informed consent (IC).
Methods
An electronic survey was administered to first-time telemedicine users at Memorial Sloan Kettering Cancer Center from 2021 to 2023. The survey assessed patients’ preferences for telemedicine versus in-person visits for the IC process and their comfort with discussing clinical trials virtually. The primary outcome was the proportion of patients who indicated that they preferred a telemedicine visit for the IC process. Patient comfort and preference for discussing clinical trials through telemedicine versus an in-person visit was also explored. Structured responses provided quantitative data over the 2021-2023 observation period and demographic variations. To gain a more detailed understanding, unstructured free-text responses describing clinical trial discussions were also analyzed through language modeling.
Results
Overall, 57% of patients (540/955) preferred telemedicine, 26% (249/955) had no preference, and 17% (166/955) preferred in-person visits for the IC process. The preference for telemedicine remained consistent across the 2021-2023 observation period. Most patients reported no difference between a telemedicine versus in-person visit for clinical trial discussion, including asking questions, sharing concerns, declining participation, and asking for more time to make a decision. Language modeling analysis revealed areas for improvement.
Conclusion
A majority of patients at a comprehensive cancer center who participated in clinical trial discussions through telemedicine reported a preference for telemedicine to complete the IC process. Telemedicine thus represents a valuable tool for reducing barriers to clinical trial participation, particularly in reducing travel and time barriers.

Effectiveness of Telehealth Versus In-Person Informed Consent: Randomized Study of Comprehension and Decision-Making

Effectiveness of Telehealth Versus In-Person Informed Consent: Randomized Study of Comprehension and Decision-Making
Saif Khairat, Paige Ottmar, Prabal Chourasia, Jihad Obeid
Journal of Medical Internet Research, 5 March 2025
Abstract
Background
Obtaining informed consent (IC) is vital for ethically and effectively recruiting participants in research projects. However, traditional in-person IC approaches encounter notable obstacles, such as geographic barriers, transportation expenses, and literacy challenges, which can lead to delays in enrollment and increased costs. Telehealth, especially teleconsent, offers a potential way to overcome these obstacles by facilitating the IC process in a digital setting. Nonetheless, there are concerns about whether teleconsent can achieve levels of understanding and involvement that are equivalent to those of in-person IC meetings.
Objective
This study aims to evaluate comprehension and decision-making in participants undergoing teleconsent versus traditional in-person IC. We used validated assessments to determine whether teleconsent is a viable alternative that maintains participants’ understanding and decision-making abilities.
Methods
A randomized comparative study design was used, recruiting potential participants for a parent study assessing patient experiences with patient portals. Participants were randomly assigned to 2 groups: teleconsent and in-person consent. The teleconsent group used Doxy.me software, allowing real-time interaction between researchers and participants while reviewing and electronically signing the IC documents. Recruitment involved using an institutional web-based platform to identify interested individuals, who were then contacted to assess eligibility and gather demographic information. The Decision-Making Control Instrument (DMCI) survey was used to assess the perceived voluntariness, trust, and decision self-efficacy. The Quality of Informed Consent (QuIC) was used to measure the comprehension level of the consent form. The validated Short Assessment of Health Literacy-English tool was used to measure participants’ health literacy levels.
Results
A total of 64 participants were enrolled in the study, with 32 in the teleconsent group and 32 in the in-person group. Of 64 participants, 32 (50%) were in the teleconsent group, 54 (84.4%) were females, 44 (68.7%) were aged 18-34 years, 50 (78.1%) were White, and 31 (48.4%) had a bachelor degree. The mean SAHL-E scores were different between the teleconsent and in-person groups (16.72, SD 1.88 vs 17.38, SD 0.95; P=.03). No significant differences were found between the average scores at baseline and follow-up for QuIC part A (P=.29), QuIC part B (P=.25), and DMCI (P=.38) within the teleconsent and in-person groups. Additionally, there were no significant differences in QuIC or DMCI between subgroups based on age, sex, and ethnicity.
Conclusions
This study assessed the effectiveness of IC processes through telehealth compared to traditional in-person visits. Findings indicate that telehealth offers similar participant understanding and engagement while overcoming geographic and accessibility barriers. As health care adopts digital solutions, these results highlight telehealth’s potential to improve recruitment and retention in clinical research, suggesting that policy makers should integrate telehealth practices into regulations for better access and health outcomes.

Landscape of informed consent practices and challenges in point-of-care clinical trials

Landscape of informed consent practices and challenges in point-of-care clinical trials
Policy Analysis
Caleigh Propes, Trevan Locke, Rachele Hendricks-Sturrup
Learning Health Systems, 3 March 2025
Open Access
Abstract
Point-of-care trials, an approach to trial design that assesses medical product effectiveness while fully integrating research and care, represent a promising opportunity to generate practically relevant evidence efficiently for priority disease areas. However, this approach presents unique considerations for appropriate and ethical informed consent. As point-of-care trials evolve, it will be important to examine informed consent through the lens of their supporting technology, of the clinicians who are responsible for administering it, and of their broader regulatory environment. Steps should be taken to establish ethical standards for point-of-care trials that reflect and serve the best interests of patients while reducing administrative burden and complexity. Our commentary describes, overall and through the use of real-world examples, how this work is vital to ensuring a modern clinical trial enterprise that is patient-centered and equips patients to make fully informed decisions about their health care.

Patients’ Information Needs for Informed Consent to Participate in First-in-Human Pig Kidney Xenotransplant Clinical Trials: A Mixed Methods Study

Patients’ Information Needs for Informed Consent to Participate in First-in-Human Pig Kidney Xenotransplant Clinical Trials: A Mixed Methods Study
Original Article
Elisa J. Gordon, Michael K. Gusmano, Jessica Gacki-Smith, Hannah L. Brooks, Margaret M. Matthews, Dahlya Manning, Joseph Leventhal, Karen J. Maschke
Xenotransplantation, 24 February 2025
Open Access
Abstract
Background
Transplant programs preparing to initiate first-in-human pig kidney xenotransplant clinical trials must be especially careful when obtaining participants’ informed consent. Little is known about the kind of information patients want for making an informed decision about trial participation.
Methods
We conducted semi-structured telephone interviews with waitlisted kidney transplant patients about information needs regarding participating in a first-in-human pig kidney xenotransplant trial, which guided development of a prototype consent form. Subsequent usability testing interviews sought patient feedback on the consent form. We analyzed qualitative data by thematic analysis and quantitative data by descriptive statistics.
Results
Twenty-eight patients participated in semi-structured interviews; 16 patients participated in usability testing interviews. Most interview participants were male (68%, 56%), White (54%, 56%), or Black (36%, 31%), respectively. Interview participants identified five types of information needs: (1) the potential for infection contraction and transmission; (2) risks, benefits, and impact of xenotransplant trials; (3) xenotransplant clinical trial and recipient experience; (4) clinical trial logistics; and (5) the pig and its kidney. Usability testing participants suggested adding details to the prototype. Participants’ preparedness to make a decision about participating in a xenotransplant trial increased after reviewing the prototype (12.5% vs. 31.3%, n.s.).
Conclusion
We identified multiple unique types of information patients desired to make informed decisions about pig kidney xenotransplant trial participation. Transplant programs initiating xenotransplant trials should be prepared to address patients’ information needs to optimize informed decision-making for trial participation. The prototype consent form may support a patient-centered approach to informed consent.

Application of Digital Engagement Tools for Exception from Informed Consent Community Consultation and Public Disclosure in the Pediatric Prehospital Airway Resuscitation Trial

Application of Digital Engagement Tools for Exception from Informed Consent Community Consultation and Public Disclosure in the Pediatric Prehospital Airway Resuscitation Trial
Henry E. Wang, Shannon W. Stephens, Kammy Jacobsen, Brittany Brown, Cara Elsholz, Jennifer A. Frey, John M. VanBuren, Marianne Gausche-Hill, Manish I. Shah, Nichole Bosson, Julie C. Leonard, Nancy Glober, Caleb Ward, Daniel K. Nishijima, Kathleen Adelgais, Katherine E. Remick, Joshua B. Gaither, M. Riccardo Colella, Douglas Swanson, Sara F. Goldkind, Alexander Keister, Matthew Hansen
Resuscitation Plus, 28 February 2025
Abstract
Background
Emergency care trials may require compliance with federal Exception from Informed Consent (EFIC) regulations, including community consultation (CC) and public disclosure (PD). The reach of traditional CC and PD modalities is limited. We describe the application of novel digital engagement tools to enrich CC and PD in a pediatric emergency care trial.
Methods
In support of EFIC CC and PD efforts for the Pediatric Prehospital Airway Resuscitation Trial (Pedi-PART), a multicenter trial of paramedic airway management in critically ill children, we deployed two digital engagement tools: 1) social media advertisements, and 2) marketing research panels. We disseminated social media advertisements (Facebook and Instagram) describing the study to targeted users in 10 communities. We determined social media advertisement impressions and engagements (shares, reactions, saves, comments, likes and clicks). We also disseminated community surveys using a marketing research panel (Qualtrics Marketing Research Services), determining the number of completed surveys, time to achieve 200 surveys, demographics of survey respondents and percentage with supportive responses.
Results
There were 23.3 million social media advertisement impressions (range 1.8-2.7 million per community) reaching 3.4 million unique users (range 239,494-439,360 per community) and resulting in 13,873 engagements (range 828-1,656 per community). Distribution of the community survey through the marketing research panel resulted in 6,771 completed surveys (range 531-914 per community). Across communities, time to 200 completed surveys ranged from 5-28 days. Survey respondents were 61.9% female, 27.0% minority race and 40.8% household income <$50,000. Most survey respondents (90.7%) supported the trial.
Conclusions
Digital engagement tools efficiently reached a large and diverse population and yielded key community feedback to inform research trial deployment. Digital engagement tools offer valuable techniques to enrich EFIC CC and PD efforts.

AI meets informed consent: a new era for clinical trial communication

AI meets informed consent: a new era for clinical trial communication
Michael Waters
JNCI Cancer Spectrum, 18 March 2025
Abstract
    Clinical trials are fundamental to evidence-based medicine, providing patients with access to novel therapeutics and advancing scientific knowledge. However, patient comprehension of trial information remains a critical challenge, as registries like ClinicalTrials.gov often present complex medical jargon that is difficult for the general public to understand. While initiatives such as plain-language summaries and multimedia interventions have attempted to improve accessibility, scalable and personalized solutions remain elusive.
This study explores the potential of Large Language Models (LLMs), specifically GPT-4, to enhance patient education regarding cancer clinical trials. By leveraging informed consent forms (ICFs) from ClinicalTrials.gov, the researchers evaluated two AI-driven approaches—direct summarization and sequential summarization—to generate patient-friendly summaries. Additionally, the study assessed the capability of LLMs to create multiple-choice question-answer pairs (MCQAs) to gauge patient understanding. Findings demonstrate that AI-generated summaries significantly improved readability, with sequential summarization yielding higher accuracy and completeness. MCQAs showed high concordance with human-annotated responses, and over 80% of surveyed participants reported enhanced understanding of the authors in-house BROADBAND trial.
While LLMs hold promise in transforming patient engagement through improved accessibility of clinical trial information, concerns regarding AI hallucinations, accuracy, and ethical considerations remain. Future research should focus on refining AI-driven workflows, integrating patient feedback, and ensuring regulatory oversight. Addressing these challenges could enable LLMs to play a pivotal role in bridging gaps in clinical trial communication, ultimately improving patient comprehension and participation.

Artificial intelligence in educational games and consent under general data protection regulation

Artificial intelligence in educational games and consent under general data protection regulation
Eirini Mougiakou, Spyros Papadimitriou, Konstantina Chrysafiadi, Maria Virvou
Intelligent Decision Technologies, 18 March 2025
Abstract
As Artificial Intelligence becomes increasingly integrated into educational games, conforming with the General Data Protection Regulation (GDPR)—a legal framework governing data protection and privacy in the European Union—remains an important yet complex challenge, particularly when minors are involved. Users are required to provide consent multiple times, often unexpectedly, at different game levels. This process is further complicated by the varying durations for which consent remains valid. As a result, users—especially minors—may become confused about the consent they have given. Additional concerns arise when the educational game is AI-equipped. If AI is not involved, no new data is generated. However, if AI is present, new data is continuously produced, necessitating ongoing consent. For example, a user may consent to personalisation, which could lead the game to categorise them in unintended ways, such as labelling them a ‘poor student’. This paper explores GDPR challenges in AI-empowered educational games, focusing on user consent, AI-inferred data, and compliance gaps. Intelligent educational games rely on adaptive decision-making algorithms to personalise learning experiences, making them a subset of Intelligent Decision Technologies. Our research is based on a fuzzy-based educational game developed as a testbed for studying GDPR compliance in AI-driven decision-making. The findings provide insights into ethical AI governance, dynamic consent management, and the intersection of regulatory compliance with adaptive, data-driven decision systems in intelligent educational technologies. Based on our research, not all personal data exist from the beginning and upon original consent granting, as personal data are also generated throughout the process.

Editor’s note: we recognise that the proposals in this article are at odds with a number of regulatory structures.

Patient consent for the secondary use of health data in artificial intelligence (AI) models: A scoping review

Patient consent for the secondary use of health data in artificial intelligence (AI) models: A scoping review
Khadijeh Moulaei, Saeed Akhlaghpour, Farhad Fatehi
International Journal of Medical Informatics, 8 March 2025
Abstract
Background
The secondary use of health data for training Artificial Intelligence (AI) models holds immense potential for advancing medical research and healthcare delivery. However, ensuring patient consent for such utilization is paramount to uphold ethical standards and data privacy. Patient informed consent means patients are fully informed about how their data will be collected, used, and protected, and they voluntarily agree to allow their data to be used for AI models. In addition to formal consent frameworks, establishing a social license is critical to foster public trust and societal acceptance for the secondary use of health data in AI systems. This study examines patient consent practices in this domain.
Method
In this scoping review, we searched Web of Science, PubMed, and Scopus. We included studies in English that addressed the core issues of interest, namely, privacy, security, legal, and ethical issues related to the secondary use of health data in AI models. Articles not addressing the core issues, as well as systematic reviews, meta-analyses, books, letters, conference abstracts, and study protocols were excluded. Two authors independently screened titles, abstracts, and full texts, resolving disagreements with a third author. Data was extracted using a data extraction form.
Results
After screening 774 articles, a total of 38 articles were ultimately included in the review. Across these studies, a total of 178 barriers and 193 facilitators were identified. We consolidated similar codes and extracted 65 barriers and 101 facilitators, which we then categorized into four themes: “Structure,” “People,” “Physical system,” and “Task.” We identified notable emphasis on “Legal and Ethical Challenges” and “Interoperability and Data Governance.” Key barriers included concerns over privacy and security breaches, inadequacies in informed consent processes, and unauthorized data sharing. Critical facilitators included enhancing patient consent procedures, improving data privacy through anonymization, and promoting ethical standards for data usage.
Conclusion
Our study underscores the complexity of patient consent for the secondary use of health data in AI models, highlighting significant barriers and facilitators within legal, ethical, and technological domains. We recommend the development of specific guidelines and actionable strategies for policymakers, practitioners, and researchers to improve informed consent, ensuring privacy, trust, and ethical use of data, thereby facilitating the responsible advancement of AI in healthcare.

Patient Consent and The Right to Notice and Explanation of AI Systems Used in Health Care

Patient Consent and The Right to Notice and Explanation of AI Systems Used in Health Care
Meghan E Hurley, Benjamin H Lang, Kristin Marie Kostick-Quenet, Jared N Smith, Jennifer Blumenthal-Barby
The American Journal of Bioethics, March 2025
Abstract
Given the need for enforceable guardrails for artificial intelligence (AI) that protect the public and allow for innovation, the U.S. Government recently issued a Blueprint for an AI Bill of Rights which outlines five principles of safe AI design, use, and implementation. One in particular, the right to notice and explanation, requires accurately informing the public about the use of AI that impacts them in ways that are easy to understand. Yet, in the healthcare setting, it is unclear what goal the right to notice and explanation serves, and the moral importance of patient-level disclosure. We propose three normative functions of this right: (1) to notify patients about their care, (2) to educate patients and promote trust, and (3) to meet standards for informed consent. Additional clarity is needed to guide practices that respect the right to notice and explanation of AI in healthcare while providing meaningful benefits to patients.

Editor’s note: The following five articles are commentaries on this article which appeared in the American Journal of Bioethics.

Disclosure as Absolution in Medicine: Disentangling Autonomy from Beneficence and Justice in Artificial Intelligence

Disclosure as Absolution in Medicine: Disentangling Autonomy from Beneficence and Justice in Artificial Intelligence
Guest Editorial
Kayte Spector-Bagdady, Alex John London
The American Journal of Bioethics, 24 February 2025
Introduction
    The rush to deploy artificial intelligence (AI) and machine learning (ML) systems in medicine highlights the need for bioethics to deepen its normative engagement in disentangling autonomy from beneficence and justice in responsible medical practice. One of the reasons that informed consent is such a unique tool is its morally transformative nature. Actions that would otherwise be illegal or unethical are rendered permissible by the provision of free and informed consent. But consent is not a panacea to absolve all risks and burdens. The proliferation of AI/ML systems highlights that every additional call for disclosure warrants a deep introspection of goals, and of what values they reflect (Hurley et al. 2025).
For example, while informed consent might be appropriate when there is a choice whether to use an AI tool in clinical care, we cannot let deference to autonomy substitute for rigorous standards—based in beneficence and justice—that ensure the safe, effective, and equitable deployment of AI in medicine. Shortcomings in AI technologies that do not meet those standards cannot otherwise be absolved through the informed consent process. The assumption that patients are empowered to assess or alleviate such deficiencies is misguided. While much has been written about the inability of informed consent to bear its increasing transformative burden (Grady et al. 2017), further exploration of the appropriate division of moral labor between ethical values in the use of AI in clinical practice is warranted.