Personal Public Disclosure: A New Paradigm for Meeting Regulatory Requirements Under Exception From Informed Consent
Research Report
Catherine E. Ross, Monica E. Kleinman, Michael W. Donnino
Critical Care Medicine, 12 February 2025
Abstract
Objectives
To describe a novel approach to the requirement for public disclosure under regulations for Exception From Informed Consent (EFIC) in an inpatient clinical trial.
Design
Single-arm intervention study within a clinical trial.
Setting
Medical and medical/surgical PICUs at an academic children’s hospital.
Participants
Families of children and young adults younger than 26 years old receiving care in a PICU.
Interventions
As part of a multipronged approach to meeting requirements for public disclosure for EFIC, we developed and implemented a process termed “personal public disclosure,” in which a member of the study team notifies all potentially eligible patients/families in-person or by phone about the trial as soon as possible upon PICU admission. Patients/families may choose to opt out of future participation in the trial.
Measurements and Main Results
Over a 16-month period, 1577 potentially eligible patients/families were successfully contacted for personal public disclosure. Of these, 473 (30%) opted out of future participation in the trial. In the same period, 64 patients developed the emergent event of interest for the primary trial. Of these, only 9 (14%) were enrolled. Upon notification of enrollment, all 9 (100%) agreed to continue in the data collection phase of the study. Of the remaining 55 missed enrollments, 38 (69%) were due to the event occurring before personal public disclosure had been completed.
Conclusions
Personal public disclosure supports patient/family autonomy within an EFIC trial; however, this approach is limited by low cost-effectiveness, feasibility and appropriateness in many circumstances.
Potential role of ChatGPT in simplifying and improving informed consent forms for vaccination: a pilot study conducted in Italy
Potential role of ChatGPT in simplifying and improving informed consent forms for vaccination: a pilot study conducted in Italy
Original Research
Claudia Cosma, Alessio Radi, Rachele Cattano, Patrizio Zanobini, Guglielmo Bonaccorsi, Chiara Lorini, Marco Del Riccio
BMJ Health & Care Informatics, 22 February 2025
Open Access
Abstract
Objectives
Informed consent forms are important for assisting patients in making informed choices regarding medical procedures. Because of their lengthy nature, complexity and specialised terminology, consent forms usually prove challenging for the general public to comprehend. This pilot study aims to use Chat Generative Pretrained Transformer (ChatGPT), a large language model (LLM), to improve the readability and understandability of a consent form for vaccination.
Methods
The study was conducted in Italy, within the Central Tuscany Local Health Unit. Three different consent forms were selected and approved: the standard consent form currently in use (A), a new form totally generated by ChatGPT (B) and a modified version of the standard form created by ChatGPT (C). Healthcare professionals in the vaccination unit were asked to evaluate the consent forms regarding adequacy, comprehensibility and completeness and to give an overall judgement. The Kruskal–Wallis test and Dunn’s test were used to evaluate the median scores of the consent forms across these variables.
Results
Consent forms A and C achieved the top scores in every category; consent form B obtained the lowest score. The median scores were 4.0 for adequacy on consent forms A and C and 3.0 on consent form B. Consent forms A and C received high overall judgement ratings with median scores of 4.0, whereas consent form B received a median score of 3.0.
Conclusions
The findings indicate that LLM tools such as ChatGPT could enhance healthcare communication by improving the clarity and accessibility of consent forms, but the best results are seen when these tools are combined with human knowledge and supervision.
Analyzing patient perspectives with large language models: a cross-sectional study of sentiment and thematic classification on exception from informed consent
Analyzing patient perspectives with large language models: a cross-sectional study of sentiment and thematic classification on exception from informed consent
Scientific Reports
Aaron E. Kornblith, Chandan Singh, Johanna C. Innes, Todd P. Chang, Kathleen M. Adelgais, Maija Holsti, Joy Kim, Bradford McClain, Daniel K. Nishijima, Steffanie Rodgers, Manish I. Shah, Harold K. Simon, John M. VanBuren, Caleb E. Ward, Catherine R. Counts
Nature, 20 February 2025
Open Access
Abstract
Large language models (LLMs) can improve text analysis efficiency in healthcare. This study explores the application of LLMs to analyze patient perspectives within the exception from informed consent (EFIC) process, which waives consent in emergency research. Our objective is to assess whether LLMs can analyze patient perspectives in EFIC interviews with performance comparable to human reviewers. We analyzed 102 EFIC community interviews from 9 sites, each with 46 questions, as part of the Pediatric Dose Optimization for Seizures in Emergency Medical Services study. We evaluated 5 LLMs, including GPT-4, to assess sentiment polarity on a 5-point scale and classify responses into predefined thematic classes. Three human reviewers conducted parallel analyses, with agreement measured by Cohen’s Kappa and classification accuracy. Polarity scores between LLM and human reviewers showed substantial agreement (Cohen’s kappa: 0.69, 95% CI 0.61–0.76), with major discrepancies in only 4.7% of responses. LLM achieved high thematic classification accuracy (0.868, 95% CI 0.853–0.881), comparable to inter-rater agreement among human reviewers (0.867, 95% CI 0.836–0.901). LLMs enabled large-scale visual analysis, comparing response statistics across sites, questions, and classes. LLMs efficiently analyzed patient perspectives in EFIC interviews, showing substantial sentiment assessment and thematic classification performance. However, occasional underperformance suggests LLMs should complement, not replace, human judgment. Future work should evaluate LLM integration in EFIC to enhance efficiency, reduce subjectivity, and support accurate patient perspective analysis.
Transforming Informed Consent Generation Using Large Language Models: Mixed Methods Study
Transforming Informed Consent Generation Using Large Language Models: Mixed Methods Study
Qiming Shi, Katherine Luzuriaga, Jeroan J Allison, Asil Oztekin, Jamie M Faro, Joy L Lee, Nathaniel Hafer, Margaret McManus, Adrian H Zai
JMIR Medical Informatics, 13 February 2025
Abstract
Background
Informed consent forms (ICFs) for clinical trials have become increasingly complex, often hindering participant comprehension and engagement due to legal jargon and lengthy content. The recent advances in large language models (LLMs) present an opportunity to streamline the ICF creation process while improving readability, understandability, and actionability.
Objectives
This study aims to evaluate the performance of the Mistral 8x22B LLM in generating ICFs with improved readability, understandability, and actionability. Specifically, we evaluate the model’s effectiveness in generating ICFs that are readable, understandable, and actionable while maintaining the accuracy and completeness.
Methods
We processed 4 clinical trial protocols from the institutional review board of UMass Chan Medical School using the Mistral 8x22B model to generate key information sections of ICFs. A multidisciplinary team of 8 evaluators, including clinical researchers and health informaticians, assessed the generated ICFs against human-generated counterparts for completeness, accuracy, readability, understandability, and actionability. Readability, Understandability, and Actionability of Key Information indicators, which include 18 binary-scored items, were used to evaluate these aspects, with higher scores indicating greater accessibility, comprehensibility, and actionability of the information. Statistical analysis, including Wilcoxon rank sum tests and intraclass correlation coefficient calculations, was used to compare outputs.
Results
LLM-generated ICFs demonstrated comparable performance to human-generated versions across key sections, with no significant differences in accuracy and completeness (P>.10). The LLM outperformed human-generated ICFs in readability (Readability, Understandability, and Actionability of Key Information score of 76.39% vs 66.67%; Flesch-Kincaid grade level of 7.95 vs 8.38) and understandability (90.63% vs 67.19%; P=.02). The LLM-generated content achieved a perfect score in actionability compared with the human-generated version (100% vs 0%; P<.001). Intraclass correlation coefficient for evaluator consistency was high at 0.83 (95% CI 0.64-1.03), indicating good reliability across assessments.
Conclusions
The Mistral 8x22B LLM showed promising capabilities in enhancing the readability, understandability, and actionability of ICFs without sacrificing accuracy or completeness. LLMs present a scalable, efficient solution for ICF generation, potentially enhancing participant comprehension and consent in clinical trials.
ChatGPT’s role in alleviating anxiety in total knee arthroplasty consent process: a randomized controlled trial pilot study
ChatGPT’s role in alleviating anxiety in total knee arthroplasty consent process: a randomized controlled trial pilot study
Randomised Controlled Trial
Wenyi Gan, Jianfeng Ouyang, Guorong She, Zhaowen Xue, Lingxuan Zhu, Anqi Lin, Weiming Mou, Aimin Jiang, Chang Qi, Quan Cheng, Peng Luo, Hua Li, Xiaofei Zheng
International Journal of Surgery, 4 February 2025
Open Access
Abstract
Background
Recent advancements in artificial intelligence (AI) like ChatGPT have expanded possibilities for patient education, yet its impact on perioperative anxiety in total knee arthroplasty (TKA) patients remains unexplored.
Methods
In this single-blind, randomized controlled pilot study from April to July 2023, 60 patients were randomly allocated using sealed envelopes to either ChatGPT-assisted or traditional surgeon-led informed consent groups. In the ChatGPT group, physicians used ChatGPT 4.0 to provide standardized, comprehensive responses to patient queries during the consent process, while maintaining their role in interpreting and contextualizing the information. Outcomes were measured using Hospital Anxiety and Depression Scales (HADS), Perioperative Apprehension Scale-7 (PAS-7), Visual Analogue Scales for Anxiety and Pain (VAS-A, VAS-P), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and satisfaction questionnaires.
Results
Of 55 patients completing the study, the ChatGPT group showed significantly lower anxiety scores after informed consent (HADS-A: 10.48 ± 3.84 vs 12.75 ± 4.12, P = .04, Power = .67; PAS-7: 12.44 ± 3.70 vs 14.64 ± 2.11, P = .01, Power = .85; VAS-A: 5.40 ± 1.89 vs 6.71 ± 2.27, P = .02, Power = .75) and on the fifth postoperative day (HADS-A: 8.33 ± 3.20 vs 10.71 ± 3.83, P = .01, Power = .79; VAS-A: 3.41 ± 1.58 vs 4.64 ± 1.70, P = .008, Power = .85). The ChatGPT group also reported higher satisfaction with preoperative education (4.22 ± 0.51 vs 3.43 ± 0.84, P<.001, Power = .99) and overall hospitalization experience (4.11 ± 0.65 vs 3.46 ± 0.69, P = .001, Power = .97). No significant differences were found in depression scores, knee function, or pain levels.
Conclusions
ChatGPT-assisted informed consent effectively reduced perioperative anxiety and improved patient satisfaction in TKA patients. While these preliminary findings are promising, larger studies are needed to validate these results and explore broader applications of AI in preoperative patient education.
Artificial Intelligence and Informed Consent: an information science perspective on privacy policies and terms of use in major AI platforms
Artificial Intelligence and Informed Consent: an information science perspective on privacy policies and terms of use in major AI platforms
Conference Presentation
Jonas Ferrigolo Melo, Moises Rockembach
Artificial Intelligence in Library and Information Science: Exploring the Intersection, January 2025; Istanbul, Turkey
Abstract
This study examines how AI platforms incorporate the principles of informed consent (IC) concerning transparency, privacy, and user autonomy. The research aims to identify areas of convergence, divergence, and potential integration between the conceptual framework of IC in Information Science and the Privacy Policies of ChatGPT, Gemini, and Co-pilot. By analyzing these policies, the study explores how these platforms communicate data practices, ensure user control over personal information, and align with ethical standards of informed decision-making. The findings contribute to a broader understanding of AI governance and user rights in digital environments.
Analysis of informed consent forms of patients undergoing cancer genetic testing in the era of next-generation sequencing
Analysis of informed consent forms of patients undergoing cancer genetic testing in the era of next-generation sequencing
Research
Tina Kerševan, Tina Kogovšek, Ana Blatnik, Mateja Krajc
Hereditary Cancer in Clinical Practice, 21 February 2025
Open Access
Abstract
Background
The Department of Clinical Cancer Genetics at the Institute of Oncology Ljubljana offers genetic counselling and testing to cancer patients and their relatives. Before undergoing genetic testing, patients sign the informed consent form. In addition to giving consent for collection of biological material and genetic testing, patients decide about storage of biological material and participation in international databases. Furthermore, patients decide whether the information regarding their test results may be revealed to their blood relatives and whether they want to be informed about secondary findings.
Methods
Using the signed consent forms, we investigated the effect of selected factors on patients’ decisions. Using different statistical methods, we tried to determine the proportion of patients who opted for different items and the effect of gender, age and cancer diagnoses on their decisions.
Results
Nearly all (99.6%) patients, regardless of gender, age, and presence of oncological diagnosis, consented to the storage of their biological material, 98.4% of patients, regardless of gender, age, and presence of oncological diagnosis, wanted to be included in international databases in a pseudo-anonymised form, 98.8% of patients, irrespective of gender, age, and presence of oncological diagnosis, allowed blood relatives to see their results, and 98.4% of patients, irrespective of gender, age and presence of oncological diagnosis, wanted to know whether secondary findings were detected when genetic analysis of their biological material was performed. Men are, on average, more likely to consent but the difference between genders is not statistically significant. Patients without oncological disease were more likely to agree to be included in international databases than patients with a confirmed oncological diagnosis.
Conclusions
Our results show that the vast majority of patients were in favour of the options they were offered. Most importantly, the majority of them allow their genetic test results be revealed to their blood relatives when needed and would participate in international databases. Research in rare diseases, including rare cancer genetic predisposition syndromes, is crucial for optimal diagnostic, prevention and treatment options for patients with rare genetic disorders. The results are also important for refining the approach to pre-and post-test cancer genetic counselling.
New Online Consent Tool for Patients
New Online Consent Tool for Patients
Australian Genomics, 23 January 2025
Abstract
Informed consent is a critical component of genomic and genetic testing. It is a process whereby a patient agrees to undergo genomic testing in full knowledge of the potential risks, benefits and outcomes. It is therefore essential that they are given clear and transparent information to support their decision. A new interactive online tool developed by Australian Genomics provides easy and accessible information in bite-sized chunks to guide patients through the key concepts of genomic testing. It is designed to complement the updated genetic and genomic clinical consent package released by Australian Genomics earlier last year. “These consent materials are among the most popular resources we have produced,” said Project Lead Professor Julie McGaughran. “This is another step forward in our work to provide accurate and engaging material to help people better understand the often-complex process of genomic testing.” The tool is designed to provide key details upfront and then options for users to explore more information based on their interests.
Looking Beyond the IRB
Looking Beyond the IRB
Editorial
Quinn Waeiss, Margaret Levi, Leif Wenar, David Magnus
The American Journal of Bioethics, 29 January 2025
Excerpt
…Looking to the informed consent process to address group harms also brings serious complications. The first is defining the groups that could experience harm. Without careful thought to the identification of these groups, researchers run the risk of using social groups as inappropriate proxies for the groups actually under study—and those ultimately at risk of harm (Juengst Citation1998). Blanket calls for community engagement in data-centric research without careful consideration of the communities in question seems likely to reinforce the incorrect use of population descriptors in fields like genomics. Doerr and Meeder (Citation2025) highlight several additional complexities with appropriately demarcating groups in data-intensive research, including groups that researchers can analyze into existence. Even if groups are properly identified, we still need to consider the additional burdens placed on communities and their members through community engagement in the research process, and how burdens could compound if such engagement were mandatory…
Consideration and Disclosure of Group Risks in Genomics and Other Data-Centric Research: Does the Common Rule Need Revision?
Consideration and Disclosure of Group Risks in Genomics and Other Data-Centric Research: Does the Common Rule Need Revision?
Target Article
Carolyn Riley Chapman, Gwendolyn P. Quinn, Heini M. Natri, Courtney Berrios, Patrick Dwyer, Kellie Owens, Síofra Heratyf Birkbeck, Arthur L. Caplan
The American Journal of Bioethics, 2025
Abstract
Harms and risks to groups and third-parties can be significant in the context of research, particularly in data-centric studies involving genomic, artificial intelligence, and/or machine learning technologies. This article explores whether and how United States federal regulations should be adapted to better align with current ethical thinking and protect group interests. Three aspects of the Common Rule deserve attention and reconsideration with respect to group interests: institutional review board (IRB) assessment of the risks/benefits of research; disclosure requirements in the informed consent process; and criteria for waivers of informed consent. In accordance with respect for persons and communities, investigators and IRBs should systematically consider potential group harm when designing and reviewing protocols, respectively. Research participants should be informed about any potential group harm in the consent process. We call for additional public discussion, empirical research, and normative analysis on these issues to determine the right regulatory and policy path forward.
Center for Informed Consent Integrity 