We need to consider the ethical challenges inherent in implementing machine learning in health care if its benefits are to be realized. Some of these challenges are straightforward, whereas others ...have less obvious risks but raise broader ethical concerns.
This Viewpoint summarizes the major issues that led to the decision to draft a revision of the Uniform Determination of Death Act, the alternatives that were considered, why there was failure to ...reach consensus, and what this means for the future.
Accepting Brain Death Magnus, David C; Wilfond, Benjamin S; Caplan, Arthur L
The New England journal of medicine,
03/2014, Letnik:
370, Številka:
10
Journal Article
Recenzirano
The cases of Jahi McMath and Marlise Muñoz have reopened public debate about brain death. But the law and ethics have long recognized that deferring to medical expertise regarding the diagnosis of ...brain death is the most reasonable way to manage the process of dying.
Two cases in which patients have been determined to be dead according to neurologic criteria (“brain death”) have recently garnered national headlines. In Oakland, California, Jahi McMath's death was determined by means of multiple independent neurologic examinations, including one ordered by a court. Her family refused to accept that she had died and went to court to prevent physicians at Children's Hospital and Research Center in Oakland from discontinuing ventilator support. Per a court-supervised agreement, the body was given to the family 3 weeks after the initial determination. The family's attorney stated that ventilatory support was continued and nutritional support . . .
Genome sequencing is enabling precision medicine—tailoring treatment to the unique constellation of variants in an individual’s genome. The impact of recurrent pathogenic variants is often ...understood, however there is a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance with functionally consequential variants and associated mechanisms. Variants of uncertain significance (VUSs) in these genes are discovered at a rate that outpaces current ability to classify them with databases of previous cases, experimental evaluation, and computational predictors. Clinicians are thus left without guidance about the significance of variants that may have actionable consequences. Computational prediction of the impact of rare genetic variation is increasingly becoming an important capability. In this paper, we review the technical and ethical challenges of interpreting the function of rare variants in two settings: inborn errors of metabolism in newborns and pharmacogenomics. We propose a framework for a genomic learning healthcare system with an initial focus on early-onset treatable disease in newborns and actionable pharmacogenomics. We argue that (1) a genomic learning healthcare system must allow for continuous collection and assessment of rare variants, (2) emerging machine learning methods will enable algorithms to predict the clinical impact of rare variants on protein function, and (3) ethical considerations must inform the construction and deployment of all rare-variation triage strategies, particularly with respect to health disparities arising from unbalanced ancestry representation.
Genome sequencing is enabling precision medicine—tailoring treatment to the unique constellation of variants in an individual’s genome. The impact of recurrent pathogenic variants is often understood, leaving a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance with functionally consequential variants and associated mechanisms. Variants of uncertain significance (VUSs) in these genes are discovered at a rate that outpaces current ability to classify them with databases of previous cases, experimental evaluation, and computational predictors. Clinicians are thus left without guidance about the significance of variants that may have actionable consequences. Computational prediction of the impact of rare genetic variation is increasingly becoming an important capability. In this paper, we review the technical and ethical challenges of interpreting the function of rare variants in two settings: inborn errors of metabolism in newborns and pharmacogenomics. We propose a framework for a genomic learning healthcare system with an initial focus on early-onset treatable disease in newborns and actionable pharmacogenomics. We argue that (1) a genomic learning healthcare system must allow for continuous collection and assessment of rare variants, (2) emerging machine learning methods will enable algorithms to predict the clinical impact of rare variants on protein function, and (3) ethical considerations must inform the construction and deployment of all rare-variation triage strategies, particularly with respect to health disparities arising from unbalanced ancestry representation.
OBJECTIVES:To explore how nonphysicians and physicians interpret the word “treatable” in the context of critical illness.
DESIGN:Qualitative study using in-depth interviews.
SETTING:One academic ...medical center.
SUBJECTS:Twenty-four nonphysicians (patients and community members) purposively sampled for variation in demographic characteristics and 24 physicians (attending physicians and trainees) purposively sampled from four specialties (critical care, palliative care, oncology, and surgery).
INTERVENTIONS:None.
MEASUREMENTS AND MAIN RESULTS:We identified two distinct concepts that participants used to interpret the word “treatable”1) a “good news” concept, in which the word “treatable” conveys a positive message about a patient’s future, thereby inspiring hope and encouraging further treatment and 2) an “action-oriented” concept, in which the word “treatable” conveys that physicians have an action or intervention available, but does not necessarily imply an improved prognosis or quality of life. The overwhelming majority of nonphysicians adopted the “good news” concept, whereas physicians almost exclusively adopted the “action-oriented” concept. For some nonphysicians, the word “treatable” conveyed a positive message about prognosis and/or further treatment, even when this contradicted previously stated negative information.
CONCLUSIONS:Physician use of the word “treatable” may lead patients or surrogates to derive unwarranted good news and false encouragement to pursue treatment, even when physicians have explicitly stated information to the contrary. Further work is needed to determine the extent to which the word “treatable” and its cognates contribute to widespread decision-making and communication challenges in critical care, including discordance about prognosis, misconceptions that palliative treatments are curative, and disputes about potentially inappropriate or futile treatment.
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