Publications

Abstract

Identifying ethical concerns with ML applications to healthcare (ML-HCA) before problems arise is now a stated goal of ML design oversight groups and regulatory agencies. Lack of accepted standard methodology for ethical analysis, however, presents challenges. In this case study, we evaluate use of a stakeholder "values-collision" approach to identify consequential ethical challenges associated with an ML-HCA for advanced care planning (ACP). Identification of ethical challenges could guide revision and improvement of the ML-HCA.We conducted semistructured interviews of the designers, clinician-users, affiliated administrators, and patients, and inductive qualitative analysis of transcribed interviews using modified grounded theory.Seventeen stakeholders were interviewed. Five "values-collisions"-where stakeholders disagreed about decisions with ethical implications-were identified: (1) end-of-life workflow and how model output is introduced; (2) which stakeholders receive predictions; (3) benefit-harm trade-offs; (4) whether the ML design team has a fiduciary relationship to patients and clinicians; and, (5) how and if to protect early deployment research from external pressures, like news scrutiny, before research is completed.From these findings, the ML design team prioritized: (1) alternative workflow implementation strategies; (2) clarification that prediction was only evaluated for ACP need, not other mortality-related ends; and (3) shielding research from scrutiny until endpoint driven studies were completed.In this case study, our ethical analysis of this ML-HCA for ACP was able to identify multiple sites of intrastakeholder disagreement that mark areas of ethical and value tension. These findings provided a useful initial ethical screening.

View details for DOI 10.1093/jamia/ocad022

View details for PubMedID 36826400

Abstract

OBJECTIVES: Most pediatric patients on ventricular assist device (VAD) survive to transplantation. Approximately 15% will die on VAD support, and the circumstances at the end-of-life are not well understood. We, therefore, sought to characterize patient location and invasive interventions used at the time of death.DESIGN: Retrospective database study of a cohort meeting inclusion criteria.SETTING: Thirty-six centers participating in the Advanced Cardiac Therapies Improving Outcomes Network (ACTION) Registry.PATIENTS: Children who died on VAD therapy in the period March 2012 to September 2021.INTERVENTIONS: None.MEASUREMENTS AND MAIN RESULTS: Of the 117 of 721 patients (16%) who died on VAD, the median (interquartile range) age was 5 years (1-16 yr) at 43 days (17-91 d) postimplant. Initial goals of therapy were bridge to consideration for candidacy for transplantation in 60 of 117 (51%), bridge to transplantation in 44 of 117 (38%), bridge to recovery 11 of 117 (9%), or destination therapy (i.e., VAD as the endpoint) in two of 117 (2%). The most common cause of death was multiple organ failure in 35 of 117 (30%), followed by infection in 12 of 117 (10%). Eighty-five of 92 (92%) died with a functioning device in place. Most patients were receiving invasive interventions (mechanical ventilation, vasoactive infusions, etc.) at the end of life. Twelve patients (10%) died at home.CONCLUSIONS: One-in-six pediatric VAD patients die while receiving device support, with death occurring soon after implant and usually from noncardiac causes. Aggressive interventions are common at the end-of-life. The ACTION Registry data should inform future practices to promote informed patient/family and clinician decision-making to hopefully reduce suffering at the end-of-life.

View details for DOI 10.1097/PCC.0000000000003115

View details for PubMedID 36398973

View details for DOI 10.1080/15265161.2022.2123976

View details for PubMedID 36332051

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

As genomic technologies rapidly develop, polygenic scores (PGS) are entering into a growing conversation on how to improve precision in public health and prevent chronic disease. While the integration of PGS into public health and clinical services raises potential benefits, it also introduces potential harms. In particular, there is a high level of uncertainty about how to incorporate PGS into clinical settings in a manner that is equitable, just, and aligned with the long-term goals of many healthcare systems to support person-centered and value-based care. This paper argues that any conversation about whether and how to design and implement PGS clinical services requires dynamic engagement with local communities, patients, and families. These parties often face the consequences, both positive and negative, of such uncertainties and should therefore drive clinical translation. As a collaborative effort between hospital stakeholders, community partners, and researchers, this paper describes a community-empowered co-design process for addressing uncertainty and making programmatic decisions about the implementation of PGS into clinical services. We provide a framework for others interested in designing clinical programs that are responsive to, and inclusive and respectful of, local communities.

View details for DOI 10.1007/s12687-023-00638-y

View details for PubMedID 36765027

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

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

Ethics is a growing interest for neuroscientists, but rather than signifying a commitment to the protection of human subjects, care of animals, and public understanding to which the professional community is engaged in a fundamental way, interest has been consumed by administrative overhead and the mission creep of institutional ethics reviews. Faculty, trainees, and staff (n = 605) in North America whose work involves brain imaging and brain stimulation completed an online survey about ethics in their research. Using factor analysis and linear regression, we found significant effects for invasiveness of imaging technique, professional position, gender, and local presence of bioethics centers. We propose strategies for improving communication between the neuroscience community and ethics review boards, collaborations between neuroscientists and biomedical ethicists, and ethics training in graduate neuroscience programs to revitalize mutual goals and interests.

View details for DOI 10.3389/fnhum.2010.00167

View details for Web of Science ID 000289308000001

View details for PubMedID 20953291

View details for PubMedCentralID PMC2955400

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

View details for DOI 10.1080/15265161.2022.2146794

View details for PubMedID 36595008

Abstract

Social media provides a potential avenue for genetic counselors to address gaps in access to reliable genetics information for rare disease communities. However, only limited research has examined patient and family attitudes toward engaging with genetic counselors through social media.Our study assessed the attitudes of members of rare disease social media groups toward engaging with genetic counselors through social media, characteristics associated with greater interest, and the benefits and potential pitfalls of various approaches to such engagement.We conducted a mixed methods survey of patients and family members recruited from a systematic sample of rare disease Facebook groups. Patient characteristics and their associations with interest in engagement with genetic counselors were evaluated using univariate and bivariate statistics. Responses to open-ended questions were analyzed using thematic content analysis.In total, 1053 individuals from 103 rare disease groups participated. The median overall interest in engaging with genetic counselors on social media was moderately high at 7.0 (IQR 4.0-9.0, range 0-10). No past experience with a genetic counselor was associated with greater interest in engaging with one through social media (µ=6.5 vs 6.0, P=.04). Participants expressed greatest interest (median 9.0, IQR 5.0-10.0) in engagement models allowing direct communication with genetic counselors, which was corroborated by the majority (n=399, 61.3%) of individuals who responded to open-ended questions explicitly stating their interest in 1-on-1 interactions. When asked what forms of support they would request from genetic counselors through social media, participants desired individualized support and information about how to access services. However, participants also expressed concerns regarding privacy and confidentiality.Patients and family members in rare disease social media groups appear interested in engaging with genetic counselors through social media, particularly for individualized support. This form of engagement on social media is not meant to replace the current structure and content of genetic counseling (GC) services, but genetic counselors could more actively use social media as a communication tool to address gaps in knowledge and awareness about genetics services and gaps in accessible patient information. Although encouraging, concerns regarding privacy and feasibility require further consideration, pointing to the need for professional guidelines in this area.

View details for DOI 10.2196/42084

View details for PubMedID 36542454

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

Background: Severe alcohol withdrawal syndrome (SAWS) is highly morbid, costly, and common among hospitalized patients, yet minimal evidence exists to guide inpatient management. Research needs in this field are broad, spanning the translational science spectrum. Goals: This research statement aims to describe what is known about SAWS, identify knowledge gaps, and offer recommendations for research in each domain of the Institute of Medicine T0-T4 continuum to advance the care of hospitalized patients who experience SAWS. Methods: Clinicians and researchers with unique and complementary expertise in basic, clinical, and implementation research related to unhealthy alcohol consumption and alcohol withdrawal were invited to participate in a workshop at the American Thoracic Society 2019 International Conference. The committee was subdivided into four groups on the basis of interest and expertise: T0-T1 (basic science research with translation to humans), T2 (research translating to patients), T3 (research translating to clinical practice), and T4 (research translating to communities). A medical librarian conducted a pragmatic literature search to facilitate this work, and committee members reviewed and supplemented the resulting evidence, identifying key knowledge gaps. Results: The committee identified several investigative opportunities to advance the care of patients with SAWS in each domain of the translational science spectrum. Major themes included 1) the need to investigate non-γ-aminobutyric acid pathways for alcohol withdrawal syndrome treatment; 2) harnessing retrospective and electronic health record data to identify risk factors and create objective severity scoring systems, particularly for acutely ill patients with SAWS; 3) the need for more robust comparative-effectiveness data to identify optimal SAWS treatment strategies; and 4) recommendations to accelerate implementation of effective treatments into practice. Conclusions: The dearth of evidence supporting management decisions for hospitalized patients with SAWS, many of whom require critical care, represents both a call to action and an opportunity for the American Thoracic Society and larger scientific communities to improve care for a vulnerable patient population. This report highlights basic, clinical, and implementation research that diverse experts agree will have the greatest impact on improving care for hospitalized patients with SAWS.

View details for DOI 10.1164/rccm.202108-1845ST

View details for Web of Science ID 000705466800002

View details for PubMedID 34609257

View details for PubMedCentralID PMC8528516

View details for Web of Science ID 000671776700003

View details for DOI 10.1177/1073110520917008

View details for PubMedID 32342787

Abstract

Applications of facial recognition technology (FRT) in health care settings have been developed to identify and monitor patients as well as to diagnose genetic, medical, and behavioral conditions. The use of FRT in health care suggests the importance of informed consent, data input and analysis quality, effective communication about incidental findings, and potential influence on patient-clinician relationships. Privacy and data protection are thought to present challenges for the use of FRT for health applications.

View details for DOI 10.1001/amajethics.2019.180

View details for PubMedID 30794128

View details for DOI 10.1038/s41746-018-0075-8

View details for Web of Science ID 000453910600001

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

View details for DOI 10.1080/15265161.2022.2075981

View details for PubMedID 35737504

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

View details for DOI 10.1001/jama.2022.5383

View details for PubMedID 35727275

View details for Web of Science ID 000802790300092

View details for Web of Science ID 000802790300076

View details for Web of Science ID 000802790300089