Publications

View details for DOI 10.1001/jama.2025.18506

View details for PubMedID 41284277

Abstract

Genomic sequencing (GS) is increasingly part of diagnostic evaluations of children with congenital heart disease (CHD). Past research suggests that clinicians might envision the most immediate benefits of GS to be guidance in decisions regarding withdrawal of care and rationing of scarce resources. This study sought to document whether providing GS information to physicians caring for critically ill children with CHD might lead them to alter recommendations regarding care strategies.A probability sample of 202 cardiologists, cardiac surgeons, neonatologists, and critical-care physicians who provide care for critically ill children with CHD in the United States, using the well-established vignette methodology, were randomly assigned different patient scenarios and different genomic information. Physicians then reported their likelihood of recommending particular treatment options for each patient.Of the participating physicians, 98.3% had seen genetic test results, 84.5% had used results when making recommendations regarding clinical care, 67.5% had seen GS results, and 37.9% had used GS results when making treatment recommendations. In the survey experiments, receipt of GS results predicting intellectual disability, autism, or a childhood onset cancer syndrome each independently significantly reduced the likelihood of physicians recommending surgical palliation, extracorporeal membrane oxygenation or heart transplant.This survey demonstrates that physicians caring for critically ill children with CHD might be influenced by genomic results when making recommendations about whether to forego or withdraw certain treatment options. This raises important ethical issues that should be considered carefully in the future.

View details for DOI 10.1016/j.gimo.2025.103470

View details for PubMedID 41431475

View details for PubMedCentralID PMC12718151

View details for DOI 10.1080/15265161.2025.2552711

View details for PubMedID 40965474

View details for DOI 10.1080/15265161.2025.2582997

View details for PubMedID 41252499

View details for DOI 10.1080/15265161.2025.2516974

View details for PubMedID 40539983

Abstract

Machine learning predictive analytics (MLPA) are utilized increasingly in health care, but can pose harms to patients, clinicians, health systems, and the public. The dynamic nature of this technology creates unique challenges to evaluating safety and efficacy and minimizing harms. In response, regulators have proposed an approach that would shift more responsibility to MLPA developers for mitigating potential harms. To be effective, this approach requires MLPA developers to recognize, accept, and act on responsibility for mitigating harms. In interviews of 40 MLPA developers of health care applications in the United States, we found that a subset of ML developers made statements reflecting moral disengagement, representing several different potential rationales that could create distance between personal accountability and harms. However, we also found a different subset of ML developers who expressed recognition of their role in creating potential hazards, the moral weight of their design decisions, and a sense of responsibility for mitigating harms. We also found evidence of moral conflict and uncertainty about responsibility for averting harms as an individual developer working in a company. These findings suggest possible facilitators and barriers to the development of ethical ML that could act through encouragement of moral engagement or discouragement of moral disengagement. Regulatory approaches that depend on the ability of ML developers to recognize, accept, and act on responsibility for mitigating harms might have limited success without education and guidance for ML developers about the extent of their responsibilities and how to implement them.

View details for PubMedID 36541003

Abstract

Regulatory agencies need to ensure the safety and equity of AI in biomedicine, and the time to do so is now.

View details for DOI 10.1126/scitranslmed.adi0336

View details for PubMedID 37703349

Abstract

Stepped-wedge cluster randomized trial (SW-CRT) designs are increasingly employed in pragmatic research; they differ from traditional parallel cluster randomized trials in which an intervention is delivered to a subset of clusters, but not to all. In a SW-CRT, all clusters receive the intervention under investigation by the end of the study. This approach is thought to avoid ethical concerns about the denial of a desired intervention to participants in control groups. Such concerns have been cited in the literature as a primary motivation for choosing SW-CRT design, however SW-CRTs raise additional ethical concerns related to the delayed implementation of an intervention and consent. Yet, PCT investigators may choose SW-CRT designs simply because they are concerned that other study designs are infeasible. In this paper, we examine justifications for the use of SW-CRT study design, over other designs, by drawing on the experience of the National Institutes of Health's Health Care Systems Research Collaboratory (NIH Collaboratory) with five pragmatic SW-CRTs. We found that decisions to use SW-CRT design were justified by practical and epistemic reasons rather than ethical ones. These include concerns about feasibility, the heterogeneity of cluster characteristics, and the desire for simultaneous clinical evaluation and implementation. In this paper we compare the potential benefits of SW-CRTs against the ethical and epistemic challenges brought forth by the design and suggest that the choice of SW-CRT design must balance epistemic, feasibility and ethical justifications. Moreover, given their complexity, such studies need rigorous and informed ethical oversight.

View details for DOI 10.1016/j.cct.2022.106703

View details for PubMedID 35176501

Abstract

The rapid advancement of artificial intelligence (AI) in biomedical research presents considerable potential for misuse, including authoritarian surveillance, data misuse, bioweapon development, increase in inequity and abuse of privacy. We propose a multi-pronged framework for researchers to mitigate these risks, looking first to existing ethical frameworks and regulatory measures researchers can adapt to their own work, next to off-the-shelf AI solutions, then to design-specific solutions researchers can build into their AI to mitigate misuse. When researchers remain unable to address the potential for harmful misuse, and the risks outweigh potential benefits, we recommend researchers consider a different approach to answering their research question, or a new research question if the risks remain too great. We apply this framework to three different domains of AI research where misuse is likely to be problematic: (1) AI for drug and chemical discovery; (2) generative models for synthetic data; (3) ambient intelligence.

View details for DOI 10.1038/s42256-024-00926-3

View details for PubMedID 40994707

View details for PubMedCentralID PMC12456743

View details for Web of Science ID 001376888000001

View details for PubMedID 39587290

View details for DOI 10.1038/s42256-024-00926-3

View details for Web of Science ID 001363449700001

Abstract

Well-recognized challenges in rare disease diagnosis include limited awareness of rare diseases among healthcare providers and barriers to accessing genetic testing. Less well understood are the ways in which communication between parents of undiagnosed children and providers may impact access to diagnosis, as well as quality of care broadly. We sought to characterize key dynamics of communication between parents of undiagnosed children and healthcare providers during the diagnostic odyssey.Parents of undiagnosed children undergoing genomic sequencing were recruited from clinical and research settings and Facebook groups. Participants completed up to three sequential, in-depth interviews. Data were analyzed inductively to identify key themes.Parents (n = 36) identified three key dimensions of their experiences communicating with providers during the diagnostic odyssey, including examples of both effective and challenging communication related to: 1) providers' availability and responsiveness; 2) trust and validation of their concerns by providers; and 3) communication across multiple providers. Parents also described employing divergent strategies, such as increased persistence and advocacy, or minimized communication and resignation, in response to challenges.Our study identified ways in which parent-provider communication can facilitate or hinder access to diagnosis and care for children with undiagnosed diseases. However, communication challenges were not universal, suggesting opportunities for intervention. Additional research is needed to identify interventions to improve parent-provider interactions during the diagnostic odyssey and to systematically evaluate the impact on time to diagnosis, access to care and patient health outcomes.

View details for DOI 10.1016/j.ymgme.2025.109283

View details for PubMedID 41232198

Abstract

Exome sequencing (ES) and genome sequencing (GS) are increasingly used as standard genetic tests to identify diagnostic variants in rare disease cases. However, prioritizing these variants to reduce the time and burden of manual interpretation by clinical teams remains a significant challenge. The Exomiser/Genomiser software suite is the most widely adopted open-source software for prioritizing coding and noncoding variants. Despite its ubiquitous use, limited data-driven guidelines currently exist to optimize its performance for diagnostic variant prioritization. Based on detailed analyses of Undiagnosed Diseases Network (UDN) probands, this study presents optimized parameters and practical recommendations for deploying the Exomiser and Genomiser tools. We also highlight scenarios where diagnostic variants may be missed and propose alternative workflows to improve diagnostic success in such complex cases.We analyzed 386 diagnosed probands from the UDN, including cases with coding and noncoding diagnostic variants. We systematically evaluated how tool performance was affected by key parameters, including gene:phenotype association data, variant pathogenicity predictors, phenotype term quality and quantity, and the inclusion and accuracy of family variant data.Parameter optimization significantly improved Exomiser's performance over default parameters. For GS data, the percentage of coding diagnostic variants ranked within the top 10 candidates increased from 49.7% to 85.5%, and for ES, from 67.3% to 88.2%. For noncoding variants prioritized with Genomiser, the top 10 rankings improved from 15.0% to 40.0%. We also explored refinement strategies for Exomiser outputs, including using p-value thresholds and flagging genes that are frequently ranked in the top 30 candidates but rarely associated with diagnoses.This study provides an evidence-based framework for variant prioritization in ES and GS data using Exomiser and Genomiser. These recommendations have been implemented in the Mosaic platform to support the ongoing analysis of undiagnosed UDN participants and provide efficient, scalable reanalysis to improve diagnostic yield. Our work also highlights the importance of tracking solved cases and diagnostic variants that can be used to benchmark bioinformatics tools. Exomiser and Genomiser are available at https://github.com/exomiser/Exomiser/ .

View details for DOI 10.1186/s13073-025-01546-1

View details for PubMedID 41121346

View details for PubMedCentralID PMC12539062

Abstract

The Undiagnosed Diseases Network (UDN) exists at an intersection of diagnostic research and clinical care and has 2 main goals: to advance diagnostic science and to facilitate accurate diagnoses in individual patient-participants with rare conditions or so-called "medical mysteries." The value of the UDN's translational research and clinical application model and of the results it produces derive from whether and to what extent the UDN achieves its 2 goals. This article considers criteria currently used to assess achievement of those goals, identifies their merits and drawbacks, and offers strategies by which the UDN might further advance diagnostic science and individual patient-participants' interests.

View details for DOI 10.1001/amajethics.2025.750

View details for PubMedID 41032346

View details for Web of Science ID 001282805300174

View details for DOI 10.1001/jamanetworkopen.2023.50821

View details for PubMedID 38190187

Abstract

The rise of genomic technologies has catalyzed shifts in the health care landscape through the commercialization of genome sequencing and testing services in the genomics marketplace. The development of consumer genomics into a growing array of information technologies aimed at collecting, curating, and broadly sharing personal data and biological materials reconstitutes the meaning of health and reframes patients into biocitizens. In this context, the good biocitizen is expected to assume personal responsibility for health through consumption of genomic information and acquiescence to public and private efforts at data surveillance and aggregation. These shifts raise fundamental questions about how competing interests of the public, the state, and corporate entities will be reconciled and what trade-offs are demanded for the promise of precision health.

View details for DOI 10.1002/hast.1156

View details for Web of Science ID 000543918300008

View details for PubMedID 32597530

Abstract

This study describes challenges faced while incorporating sometimes conflicting stakeholder feedback into study design and development of patient-facing materials for a translational genomics study aiming to reduce health disparities among diverse populations.We conducted an ethnographic analysis of study documents including summaries of patient advisory committee meetings and interviews, reflective field notes written by study team members, and correspondence with our institutional review board (IRB). Through this analysis, we identified cross-cutting challenges for incorporating stakeholder feedback into development of our recruitment, risk assessment, and informed consent processes and materials.Our analysis revealed three key challenges: (1) balancing precision and simplicity in the design of study materials, (2) providing clinical care within the research context, and (3) emphasizing potential study benefits versus risks and limitations.While involving patient stakeholders in study design and materials development can increase inclusivity and responsiveness to patient needs, patient feedback may conflict with that of content area experts on the research team and IRBs who are tasked with overseeing the research. Our analysis highlights the need for further empirical research about ethical challenges when incorporating patient feedback into study design, and for dialogue with genomic researchers and IRB representatives about these issues.

View details for DOI 10.1038/s41436-020-0763-z

View details for PubMedID 32089547

Abstract

Stress and stress-related chronic illness are increasing worldwide while mental health care access remains limited. Recent neurophysiological advances support the effectiveness and safety of hypnosis for stress management. In this retrospective observational study, we studied app-delivered hypnosis in 84,395 users across 282,893 stress reduction sessions. Users rated pre- and post-session stress on a 10-point Likert Scale. Data analysis utilized Linear Mixed Effects (LME) models to accommodate repeated measures and missing data. Effects of session type, user hypnotizability, age, sex, and membership were assessed. Pre-to-post stress reduction occurred consistently in each of the first 10 sessions (Cohen's d values ranging from -0.71 to -0.78), demonstrating significant improvement in stress management. Across the first 10 sessions, greater stress reduction was observed with interactive and regular-length sessions, higher hypnotizability, older age groups, and paying members. Findings provide evidence that disseminable digital formulations of hypnosis contribute meaningfully to stress reduction.

View details for DOI 10.1038/s41746-025-02182-0

View details for PubMedID 41413254

Abstract

Melatonin is an easily accessible, widely used drug for sleep issues, disrupted sleep-wake cycles, and jet lag, available in a variety of forms and dosages. Melatonin is also used in hospital settings to promote sleep onset, particularly in elderly patients, as a circadian rhythm regulator. Despite the popularity of melatonin, it is not approved by the US Food and Drug Administration (FDA). This creates ambiguity surrounding its proper usage for optimum results, including dosage and time of administration. The objective of this article is to shed light on the best timing to administer melatonin. Melatonin is a hormone that our body naturally produces to regulate our biological clock. Even though our body has a built-in "sleep system," many people still suffer from chronic sleep disorders such as insomnia. Melatonin has also proved to help prevent delirium in hospitalized patients due to its circadian rhythm regulatory effects. The elderly are at risk of developing insomnia because as one ages, melatonin production decreases. The most convenient solution for insomnia is to take melatonin supplements. To optimize the effects of melatonin supplements, proper dosage and timing must be considered. Additionally, patients who are oppositional to bedtime, which is known as bedtime resistance, are typically more willing to go to bed following melatonin administration. Melatonin administration at around 6PM (1-2hours before bedtime) is optimal to regulate sleep cycles of patients, and it can help with bedtime resistance. This should be the standard of care in all hospitals, nursing homes, and at home.

View details for DOI 10.1017/S109285292510062X

View details for PubMedID 41126740

View details for DOI 10.4274/eajem.galenos.2025.49358

View details for Web of Science ID 001570928800001

View details for DOI 10.1080/15265161.2024.2308128

View details for PubMedID 38529987

Abstract

The study provides a comprehensive review of OpenAI's Generative Pre-trained Transformer 4 (GPT-4) technical report, with an emphasis on applications in high-risk settings like healthcare. A diverse team, including experts in artificial intelligence (AI), natural language processing, public health, law, policy, social science, healthcare research, and bioethics, analyzed the report against established peer review guidelines. The GPT-4 report shows a significant commitment to transparent AI research, particularly in creating a systems card for risk assessment and mitigation. However, it reveals limitations such as restricted access to training data, inadequate confidence and uncertainty estimations, and concerns over privacy and intellectual property rights. Key strengths identified include the considerable time and economic investment in transparent AI research and the creation of a comprehensive systems card. On the other hand, the lack of clarity in training processes and data raises concerns about encoded biases and interests in GPT-4. The report also lacks confidence and uncertainty estimations, crucial in high-risk areas like healthcare, and fails to address potential privacy and intellectual property issues. Furthermore, this study emphasizes the need for diverse, global involvement in developing and evaluating large language models (LLMs) to ensure broad societal benefits and mitigate risks. The paper presents recommendations such as improving data transparency, developing accountability frameworks, establishing confidence standards for LLM outputs in high-risk settings, and enhancing industry research review processes. It concludes that while GPT-4's report is a step towards open discussions on LLMs, more extensive interdisciplinary reviews are essential for addressing bias, harm, and risk concerns, especially in high-risk domains. The review aims to expand the understanding of LLMs in general and highlights the need for new reflection forms on how LLMs are reviewed, the data required for effective evaluation, and addressing critical issues like bias and risk.

View details for DOI 10.1371/journal.pdig.0000417

View details for PubMedID 38236824

View details for PubMedCentralID PMC10795998

Abstract

Self-referred imaging is one of the latest health care services to be marketed directly to consumers. Most aspects of these services are unregulated, and little is known about the messages in advertising used to attract potential consumers. We conducted a detailed analysis of print advertisements and informational brochures for self-referred imaging with respect to themes, content, accuracy, and emotional valence.Forty print advertisements from US newspapers around the country and 20 informational brochures were analyzed by 2 independent raters according to 7 major themes: health care technology; emotion, empowerment, and assurance; incentives; limited supporting evidence; popular appeal; statistics; and images. The Fisher exact test was used to identify significant differences in information content.Both the advertisements and the brochures emphasized health care and technology information and provided assurances of good health and incentives to self-refer. These materials also encouraged consumers to seek further information from company resources; virtually none referred to noncomplying sources of information or to the risks of having a scan. Images of people commonly portrayed European Americans. We found statistical differences between newspaper advertisements and mailed brochures for references to "prevalence of disease" (P<.001), "death" (P<.003), and "radiation" (P<.001). Statements lacking clear scientific evidence were identified in 38% of the advertisements (n = 15) and 25% of the brochures (n = 5).Direct-to-consumer marketing of self-referred imaging services, in both print advertisements and informational brochures, fails to provide prospective consumers with comprehensive balanced information vital to informed autonomous decision making. Professional guidelines and oversight for advertising and promotion of these services are needed.

View details for Web of Science ID 000225701900003

View details for PubMedID 15596630

Abstract

To examine different protocols for handling incidental findings on brain research MRIs, and provide a platform for establishing formal discussions of related ethical and policy issues.Corresponding authors identified from a database of peer-reviewed publications in 1991-2002 involving functional MRI (fMRI), alone or in combination with other imaging modalities, were invited to participate in this web-based survey. The survey asked questions regarding knowledge and handling of incidental findings, as well as characteristics of the scanning environment, training required, IRB protocol requirements, and neuroradiologist involvement.Seventy-four investigators who conduct MRI studies in the United States and abroad responded. Eighty-two percent (54/66) reported discovering incidental findings in their studies, such as arteriovenous malformations, brain tumors, and developmental abnormalities. Substantial variability was found in the procedures for handling and communicating findings to subjects, neuroradiologist involvement, personnel permitted to operate equipment, and training.Guidelines for minimum and optimum standards for detecting and communicating incidental findings on brain MRI research are needed.

View details for DOI 10.1002/jmri.20180

View details for Web of Science ID 000224762700001

View details for PubMedID 15503329

View details for PubMedCentralID PMC1506385

Abstract

Chylothorax is a potentially serious complication of lung and heart-lung transplantation. This article describes the clinical course of chylothorax in 3 heart-lung allograft recipients. We discuss management options, including dietary modifications, octreotide infusion, thoracic duct ligation and embolization, and surgical pleurodesis. In addition, we describe the novel use of aminocaproic acid to reduce lymph flow. We propose a multidisciplinary approach for the management of chylothorax that includes both medical and surgical options.

View details for Web of Science ID 000221393700018

View details for PubMedID 15135382

Abstract

Sharing human brain data can yield scientific benefits, but because of various disincentives, only a fraction of these data is currently shared. We profile three successful data-sharing experiences from the NIH BRAIN Initiative Research Opportunities in Humans (ROH) Consortium and demonstrate benefits to data producers and to users.

View details for DOI 10.1016/j.neuron.2023.09.029

View details for Web of Science ID 001135333000001

View details for PubMedID 37944519

View details for DOI 10.1080/15265161.2023.2237443

View details for Web of Science ID 001058425800017

View details for PubMedID 37647482

View details for PubMedCentralID PMC10502902

View details for DOI 10.1001/jama.2024.9450

View details for PubMedID 39141373

View details for DOI 10.1213/ANE.0000000000006061

View details for PubMedID 36806237

View details for DOI 10.1213/ANE.0000000000005947

View details for PubMedID 35709451

Abstract

We hypothesized that cancer patients, their family members, and cancer and palliative care practitioners were experiencing significant COVID-related stress and changes to cancer care and sought to characterize contributing factors.We completed a mixed methods study to explore cancer care-related experiences during the COVID-19 pandemic of cancer patients, family members, and cancer and palliative care practitioners. Participants were recruited from an ongoing clinical trial.Patients were pursuing curative-intent surgeries for upper gastrointestinal cancers. Between October 2020 and July 2021, we collected 491 surveys from 181 cancer patients and 26 family caregivers and conducted 71 in-depth interviews with 48 cancer patients, 6 family caregivers, and 19 practitioners. Patient-reported quality-of-life measurements were not associated with a corresponding COVID surge. Patients and caregivers were generally satisfied with their care, trusted providers, and were often unaware of pandemic-related changes. In contrast, practitioners reported significant pandemic-related changes to cancer care delivery with associated practitioner anxiety, fatigue, and moral distress. All felt that visitor restrictions negatively impacted care.Our data suggest that cancer patients coped well overall despite pandemic-related disruptions whereas cancer care clinicians experienced significant stress. These findings can inform future disaster-preparedness planning and policy-setting in institutions providing cancer care.

View details for DOI 10.1186/s12904-025-01944-7

View details for PubMedID 41466257

View details for PubMedCentralID PMC12750573

Abstract

The Undiagnosed Diseases Network (UDN) exists at an intersection of diagnostic research and clinical care and has 2 main goals: to advance diagnostic science and to facilitate accurate diagnoses in individual patient-participants with rare conditions or so-called "medical mysteries." The value of the UDN's translational research and clinical application model and of the results it produces derive from whether and to what extent the UDN achieves its 2 goals. This article considers criteria currently used to assess achievement of those goals, identifies their merits and drawbacks, and offers strategies by which the UDN might further advance diagnostic science and individual patient-participants' interests.

View details for DOI 10.1001/amajethics.2025.750

View details for PubMedID 41032346

Abstract

Genomics research enrolling undiagnosed patients can provide answers for one-third of participants and more can be diagnosed through future reanalysis. The long-term value for participants has raised questions of the "sustainability" of these studies, but the meaning, goals and best practices for sustainability remain unclear.We conducted semi-structured interviews with researchers leading studies enrolling undiagnosed patients in the United States and Canada and utilized thematic content analysis to summarize key themes.Researchers lacked consensus regarding what "sustainability" was actually intended to sustain, variably referencing study procedures, personnel, data access, and participant recontact. However, the primary driver of sustainability was widely shared as the perceived obligation to continue to search for answers for undiagnosed participants. Proposed sustainability strategies included diversifying funding sources, developing centralized data infrastructure, and building collaborations across disciplines and institutions. Researchers also emphasized the need to address ethical concerns, to integrate research with clinical care, and for leadership from research funders to guide these efforts.While genomics researchers perceived continued obligations to undiagnosed participants, they also lacked a shared understanding of the goals of sustainability and called for coordinated efforts to develop centralized infrastructure that integrated research and clinical care.

View details for DOI 10.1016/j.gim.2025.101458

View details for PubMedID 40411343