The Precision Medicine Initiative's All of Us Research Program: an agenda for research on its ethical, legal, and social issues
TL;DR: It is concluded that PMI's All of Us Research Program represents a significant opportunity and obligation to identify, analyze, and respond to ELSI, and is called on the PMI to initiate a research program capable of taking on these challenges.
About: This article is published in Genetics in Medicine.The article was published on 2017-07-01 and is currently open access. It has received 204 citations till now. The article focuses on the topics: Health care & Research program.
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TL;DR: Current and prospective wearable technologies and their progress toward clinical application are reviewed and technologies underlying common, commercially available wearable sensors and early-stage devices and research to support the use of these devices in healthcare are described.
Abstract: Wearable sensors are already impacting healthcare and medicine by enabling health monitoring outside of the clinic and prediction of health events. This paper reviews current and prospective wearable technologies and their progress toward clinical application. We describe technologies underlying common, commercially available wearable sensors and early-stage devices and outline research, when available, to support the use of these devices in healthcare. We cover applications in the following health areas: metabolic, cardiovascular and gastrointestinal monitoring; sleep, neurology, movement disorders and mental health; maternal, pre- and neo-natal care; and pulmonary health and environmental exposures. Finally, we discuss challenges associated with the adoption of wearable sensors in the current healthcare ecosystem and discuss areas for future research and development.
313 citations
TL;DR: This review summarizes the main classes of problems that AI systems are well suited to solve and describes the clinical diagnostic tasks that benefit from these solutions, and focuses on emerging methods for specific tasks in clinical genomics.
Abstract: Artificial intelligence (AI) is the development of computer systems that are able to perform tasks that normally require human intelligence. Advances in AI software and hardware, especially deep learning algorithms and the graphics processing units (GPUs) that power their training, have led to a recent and rapidly increasing interest in medical AI applications. In clinical diagnostics, AI-based computer vision approaches are poised to revolutionize image-based diagnostics, while other AI subtypes have begun to show similar promise in various diagnostic modalities. In some areas, such as clinical genomics, a specific type of AI algorithm known as deep learning is used to process large and complex genomic datasets. In this review, we first summarize the main classes of problems that AI systems are well suited to solve and describe the clinical diagnostic tasks that benefit from these solutions. Next, we focus on emerging methods for specific tasks in clinical genomics, including variant calling, genome annotation and variant classification, and phenotype-to-genotype correspondence. Finally, we end with a discussion on the future potential of AI in individualized medicine applications, especially for risk prediction in common complex diseases, and the challenges, limitations, and biases that must be carefully addressed for the successful deployment of AI in medical applications, particularly those utilizing human genetics and genomics data.
169 citations
21 Nov 2019
TL;DR: A precision medicine conundrum is examined: can artificial intelligence offer a breakthrough in predicting risks and prognosis for neurodevelopmental disorders?
Abstract: The ambition of precision medicine is to design and optimize the pathway for diagnosis, therapeutic intervention, and prognosis by using large multidimensional biological datasets that capture individual variability in genes, function and environment. This offers clinicians the opportunity to more carefully tailor early interventions- whether treatment or preventative in nature-to each individual patient. Taking advantage of high performance computer capabilities, artificial intelligence (AI) algorithms can now achieve reasonable success in predicting risk in certain cancers and cardiovascular disease from available multidimensional clinical and biological data. In contrast, less progress has been made with the neurodevelopmental disorders, which include intellectual disability (ID), autism spectrum disorder (ASD), epilepsy and broader neurodevelopmental disorders. Much hope is pinned on the opportunity to quantify risk from patterns of genomic variation, including the functional characterization of genes and variants, but this ambition is confounded by phenotypic and etiologic heterogeneity, along with the rare and variable penetrant nature of the underlying risk variants identified so far. Structural and functional brain imaging and neuropsychological and neurophysiological markers may provide further dimensionality, but often require more development to achieve sensitivity for diagnosis. Herein, therefore, lies a precision medicine conundrum: can artificial intelligence offer a breakthrough in predicting risks and prognosis for neurodevelopmental disorders? In this review we will examine these complexities, and consider some of the strategies whereby artificial intelligence may overcome them.
118 citations
TL;DR: It is argued that AI's ability to advance personalized medicine will depend critically on not only the refinement of relevant assays, but also on ways of storing, aggregating, accessing, and ultimately integrating, the data they produce.
Abstract: The development of high-throughput, data-intensive biomedical research assays and technologies has created a need for researchers to develop strategies for analyzing, integrating, and interpreting the massive amounts of data they generate. Although a wide variety of statistical methods have been designed to accommodate ‘big data,’ experiences with the use of artificial intelligence (AI) techniques suggest that they might be particularly appropriate. In addition, the results of the application of these assays reveal a great heterogeneity in the pathophysiologic factors and processes that contribute to disease, suggesting that there is a need to tailor, or ‘personalize,’ medicines to the nuanced and often unique features possessed by individual patients. Given how important data-intensive assays are to revealing appropriate intervention targets and strategies for treating an individual with a disease, AI can play an important role in the development of personalized medicines. We describe many areas where AI can play such a role and argue that AI’s ability to advance personalized medicine will depend critically on not only the refinement of relevant assays, but also on ways of storing, aggregating, accessing, and ultimately integrating, the data they produce. We also point out the limitations of many AI techniques in developing personalized medicines as well as consider areas for further research.
111 citations
University of Washington1, University of Texas Health Science Center at Houston2, University of Maryland, Baltimore3, Boston University4, Broad Institute5, Baylor College of Medicine6, National Institutes of Health7, Harvard University8, United States Department of Veterans Affairs9, University of Virginia10, UCLA Medical Center11, University of Mississippi Medical Center12, Kaiser Permanente13
TL;DR: Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology, shows the potential of big data to improve the quality of research in the rapidly changing environment.
Abstract: Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology
90 citations
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TL;DR: In this paper, the authors analyzed responses to a cross-sectional telephone survey to assess the independent relationship of self-reported race (non-Hispanic black or non-Hispanic white) with trust in physicians, hospitals, and health insurance plans.
Abstract: Objective. A legacy of racial discrimination in medical research and the health care system has been linked to a low level of trust in medical research and medical care among African Americans. While racial differences in trust in physicians have been demonstrated, little is known about racial variation in trust of health insurance plans and hospitals. For the present study, the authors analyzed responses to a cross sectional telephone survey to assess the independent relationship of self-reported race (non-Hispanic black or non-Hispanic white) with trust in physicians, hospitals, and health insurance plans. Methods. Respondents ages 18–75 years were asked to rate their level of trust in physicians, health insurance plans, and hospitals. Items from the Medical Mistrust Index were used to assess fear and suspicion of hospitals. Results. Responses were analyzed for 49 (42%) non-Hispanic black and 69 (58%) non-Hispanic white respondents (N=118; 94% of total survey population). A majority of respondents trusted physicians (71%) and hospitals (70%), but fewer trusted their health insurance plans (28%). After adjustment for potential confounders, non-Hispanic black respondents were less likely to trust their physicians than non-Hispanic white respondents (adjusted absolute difference 37%; p=0.01) and more likely to trust their health insurance plans (adjusted absolute difference 28%; p=0.04). The difference in trust of hospitals (adjusted absolute difference 13%) was not statistically significant. Non-Hispanic black respondents were more likely than non-Hispanic white respondents to be concerned about personal privacy and the potential for harmful experimentation in hospitals. Conclusions. Patterns of trust in components of our health care system differ by race. Differences in trust may reflect divergent cultural experiences of blacks and whites as well as differences in expectations for care. Improved understanding of these factors is needed if efforts to enhance patient access to and satisfaction with care are to be effective.
835 citations
TL;DR: A new ethics framework is put forward to support the transformation to a learning health care system and to help ensure that learning activities carried out within such a system are conducted in an ethically acceptable fashion.
Abstract: Calls are increasing for American health care to be organized as a learning health care system, defined by the Institute of Medicine as a health care system “in which knowledge generation is so embedded into the core of the practice of medicine that it is a natural outgrowth and product of the healthcare delivery process and leads to continual improvement in care.” We applaud this conception, and in this paper, we put forward a new ethics framework for it. No such framework has previously been articulated. The goals of our framework are twofold: to support the transformation to a learning health care system and to help ensure that learning activities carried out within such a system are conducted in an ethically acceptable fashion.
451 citations
TL;DR: Higher HCSD among African Americans is explained by a greater burden of experiences of racial discrimination than whites, and efforts to eliminate racial discrimination and restore trust given prior discrimination are needed.
Abstract: Purpose:Factors contributing to racial differences in health care system distrust (HCSD) are currently unknown. Proposed potential contributing factors are prior experiences of racial discrimination and racial residential segregation.Methods:Random digit dialing survey of 762 African American and 12
219 citations
TL;DR: This scoping review for patient/family engagement tools and guides is a good start for a resource inventory and can guide the content development of a patient engagement resource kit to be used by patients/families, healthcare providers and administrators.
Abstract: Extensive literature exists on public involvement or engagement, but what actual tools or guides exist that are practical, tested and easy to use specifically for initiating and implementing patient and family engagement, is uncertain. No comprehensive review and synthesis of general international published or grey literature on this specific topic was found. A systematic scoping review of published and grey literature is, therefore, appropriate for searching through the vast general engagement literature to identify ‘patient/family engagement’ tools and guides applicable in health organization decision-making, such as within Alberta Health Services in Alberta, Canada. This latter organization requested this search and review to inform the contents of a patient engagement resource kit for patients, providers and leaders. Search terms related to ‘patient engagement’, tools, guides, education and infrastructure or resources, were applied to published literature databases and grey literature search engines. Grey literature also included United States, Australia and Europe where most known public engagement practices exist, and Canada as the location for this study. Inclusion and exclusion criteria were set, and include: English documents referencing ‘patient engagement’ with specific criteria, and published between 1995 and 2011. For document analysis and synthesis, document analysis worksheets were used by three reviewers for the selected 224 published and 193 grey literature documents. Inter-rater reliability was ensured for the final reviews and syntheses of 76 published and 193 grey documents. Seven key themes emerged from the literature synthesis analysis, and were identified for patient, provider and/or leader groups. Articles/items within each theme were clustered under main topic areas of ‘tools’, ‘education’ and ‘infrastructure’. The synthesis and findings in the literature include 15 different terms and definitions for ‘patient engagement’, 17 different engagement models, numerous barriers and benefits, and 34 toolkits for various patient engagement and evaluation initiatives. Patient engagement is very complex. This scoping review for patient/family engagement tools and guides is a good start for a resource inventory and can guide the content development of a patient engagement resource kit to be used by patients/families, healthcare providers and administrators.
91 citations
TL;DR: In this article, the author will cover the following issues: whether traditional research regulations should apply to Big Data health research; (2) the relationship between privacy and autonomy in Big Datahealth research; and (3) the role of informed consent in Big data health research.
Abstract: Mark A Rothstein Let me say at the outset that I am not an expert on Big Data I am not even an expert on Little Data I am, like many of you, someone who teaches and writes about research ethics, including privacy, autonomy, and informed consent I am someone who is concerned whenever I hear that a new research method is so exciting and so promising that we ought to set aside the ethical principles and rules that have shaped the world of research ethics since the Nuremberg Code in 1947 I am reminded of the statement of J Robert Oppenheimer, theoretical physicist and father of the atom bomb: “When you see something that is technically sweet, you go ahead and do it and you argue about what to do about it only after you have had your technical success”1 I hope we do not fall into the trap of believing that any new information technology is worth using in health research regardless of the ethical issues in performing the research or the larger implications In this article, I will cover the following issues: (1) whether traditional research regulations should apply to Big Data health research; (2) the relationship between privacy and autonomy in Big Data health research; and (3) the role of informed consent in Big Data health research
76 citations