Month: February 2025

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Informed Consent: A Monthly Review
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March 2025 :: Issue 75

In preparing this digest, we monitor a broad range of academic journals and utilize Google Scholar to search articles referencing  informed consent or assent. After careful consideration, a selection of these results appear in the digest. We also monitor other research, analysis, guidance and commentary beyond the academic literature globally, including calls for public consultation and symposia/conferences which address consent/assent in whole or in part. We recognize that some of the arguments presented in this edition may be controversial and may warrant closer scrutiny. We have elected to be generous in our inclusion with the goal of presentating of a wholistic landscape of informed consent literature as it is being published. We acknowledge that this scope yields an indicative and not an exhaustive digest product.

Informed Consent: A Monthly Review is a service of the Center for Informed Consent Integrity, a program of the GE2P2 Global Foundation. The Foundation is solely responsible for its content. Comments and suggestions should be directed to:

Editor
Paige Fitzsimmons, MA
Associate Director, Center for Informed Consent Integrity
GE2P2 Global Foundation
paige.fitzsimmons@ge2p2global.org

PDF Version: Center for Informed Consent Integrity – A Monthly Review_March 2025

Pulling Out the Rug on Informed Consent — New Legal Threats to Clinicians and Patients

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This month we have chosen to highlight an article which appeared in the New England Journal of Medicine, Pulling Out the Rug on Informed Consent — New Legal Threats to Clinicians and Patients, reflecting the impact of changes in US law on informed consent. In their perspective piece, Underhill and Nelson write about the change to Utah’s Malpractice Act which allows the retroactive withdrawal of consent given as a minor, leaving clinicians vulnerable to future litigation. We are highlighting this article as we are concerned about the intersection of politics and ideology here. Consent is an important to the patient as to the physician, and the role of consent should not be used to further an agenda. We are reaching out to the authors to explore potential pathways forward.

Pulling Out the Rug on Informed Consent — New Legal Threats to Clinicians and Patients
Perspective
Kristen Underhill, Kimberly M. Nelson
New England Journal of Medicine, 1 February 2025
Abstract
A legal technique deployed by Utah to restrict gender-affirming care for minors aims at a core component of the clinician–patient relationship: clinicians’ ability to rely on patients’ informed consent.
Excerpt
   In recent years, state legislators in large portions of the United States have devised and enacted new legal strategies to limit access to health care for transgender people. To date, 26 states have enacted outright bans on gender-affirming care, which thus far apply only to minors. Other state laws create financial or procedural obstacles to this type of care, such as bans on insurance coverage, requirements to obtain opinions from multiple clinicians, or consent protocols that are stricter than those for other health care…
Allowing patients to withdraw their consent retroactively is an acute threat to the legal infrastructure supporting U.S. healthcare. Informed-consent requirements exist to ensure that patients have the information and agency to participate in their own healthcare. They also protect clinicians. In all practice areas, clinicians expect to rely on patients’ consent at the time of care, without having to guess which patients will later change their minds. If laws eliminate clinicians’ ability to rely on consent at the time of care, the resulting legal instability may undermine access to all types of health care services. Pulling out the rug on informed consent threatens the core of the clinician–patient relationship. Clinicians in every area should recognize that these laws are not just attacking gender-affirming care — they are attacking the foundation of the U.S. health care system.

Verbal Consent in Biomedical Research: Moving Toward a Future Standard Practice?

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Verbal Consent in Biomedical Research: Moving Toward a Future Standard Practice?
Review Article
Alycia Noe, Emilie Vaillancourt, Ma’n H Zawati
Frontiers in Genetics, 12 February 2025
Abstract
Properly obtaining informed consent is a core obligation for research conducted using human subjects. The traditional informed consent process involves written forms and obtaining signatures. This process remains the standard, but in various research settings, such as COVID-19 and rare disease research, verbal consent has increasingly become the norm. Although verbal consent is used in these settings, its use is still a subject of debate. This article reviews in what medical settings verbal consent is commonly seen today, various advantages and disadvantages of verbal consent, and its legislative and policy ecosystem. In doing so, this review article asserts that it is time for the debate over verbal consent to come to an end and for legislator and policymakers to acknowledge its use and to formalize the process. This will allow verbal consent to be regulated in a similar manner to written consent and will give clinician-researchers guidance on how to better implement verbal consent in their studies to addressing ongoing concerns with the consenting process as a whole.

Editor’s Note: We note that if verbal consent is employed there must also be a record of this transaction, either by way of recording or transcription, to allow for adequate documentation.

The Use of Electronic Consent (eConsent) Within the Ketamine for Long-Lasting Pain Relief After Surgery (KALPAS) Multicenter Trial

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The Use of Electronic Consent (eConsent) Within the Ketamine for Long-Lasting Pain Relief After Surgery (KALPAS) Multicenter Trial
Lisa V Doan, Jeri Burr, Raven Perez, Hamleini Martinez, Randy Cuevas, Kevin Watt, Jing Wang
Journal of Pain Research, 4 January 2025
Abstract
Background
The informed consent process has traditionally taken place in person. The introduction of electronic consent (eConsent) has made remote consenting processes possible. Use of eConsent has increased since the COVID-19 pandemic. It has streamlined the process of consenting patients and has been shown to benefit the research study team and participants.
eConsent in the Ketamine Analgesia for Long-Lasting Pain Relief After Surgery (Kalpas) Study
The KALPAS study is a multicenter, double-blind, randomized controlled study investigating the effectiveness of ketamine in reducing chronic post-mastectomy pain in women undergoing mastectomy for oncologic indication. The study uses a two-part consent form consisting of a master consent with information applicable to all sites and site-specific information. All potential participants receive the full two-part consent form for review. When signing the eConsent, however, all potential participants are provided with a concise summary of the informed consent document, an approach not widely used by multicenter studies. eConsent has been noted to be beneficial to research staff when trying to gather informed consent from participants who live far away from the hospital, want to include their family and friends, and for researchers who can approach patients outside of their clinical appointments.
Conclusion
The ability to consent patients remotely has allowed for a flexible workflow within sites and a more patient-centric process that focuses on including loved ones in the discussion and scheduling time to speak to a principal investigator. Demand for eConsent will likely continue in the post-COVID era, and use of a concise summary can allow for a more efficient consenting process.

Impact of informed consent quality on illness uncertainty among patients with cancer in clinical trials: a cross-sectional study

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Impact of informed consent quality on illness uncertainty among patients with cancer in clinical trials: a cross-sectional study
Original Article
Sihan Kang, Jie Zhang, Dong Pang , Hong Yang, Xiaohong Liu, Renxiu Guo, Yuhan Lu
Asia-Pacific Journal of Oncology Nursing, 20 February 2025
Open Access
Abstract
Objective
This study aimed to examine the level of illness uncertainty and the quality of informed consent among patients with cancer participating in clinical trials and explore their interrelationship.
Methods
A cross-sectional study was conducted with 265 patients with cancer recruited from a tertiary hospital in Beijing, China, from April to November 2023. Participants completed a questionnaire encompassing demographic details, the Mishel Uncertainty in Illness Scale, and the Quality of Informed Consent Questionnaire. Descriptive statistics, correlation analyses, and multiple regression analyses were performed to assess the data.
Results
The mean illness uncertainty score was 40.63 ± 10.12, reflecting a moderately low level of uncertainty, with “Ambiguity” scoring the highest among its dimensions. The mean score for informed consent quality was 3.30 ± 1.20, indicating a moderate level of understanding, with notable gaps in elements such as alternatives and confidentiality. A significant negative correlation was found between the “Foreseeable risks or discomforts” element of informed consent and overall illness uncertainty (P < 0.05). Regression analysis revealed that factors such as clinical trial phase, primary caregiver relationship, and health insurance model significantly influenced illness uncertainty and its dimensions.
Conclusions
Enhancing the quality of informed consent can effectively reduce illness uncertainty among patients with cancer in clinical trials. Greater emphasis should be placed on clear communication of risks and discomforts and patient-centered interventions to mitigate psychological stress.

Personal Public Disclosure: A New Paradigm for Meeting Regulatory Requirements Under Exception From Informed Consent

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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

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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

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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

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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

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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.