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3 Translation of Genomics for Patient Care and Research
Pages 19-36

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From page 19...
... (Baker, Chute, Fowler, Moss) • A health care system in which an infrastructure supports complete learning cycles that encompass both the analysis of data to produce results and the use of those results to develop changes in clinical practices is a system that will allow for optimal learning.
From page 20...
... ENGAGING PATIENTS Patients are consumers of health products and services, said Dixie Baker, a senior partner at Martin, Blanck & Associates, and they are also the "primary source of the information and data that we need to create this learning health system." Today's consumers are much more involved in their own care and in the health of their families than consumers in the past, Baker said. In January 2014, for example, nearly one-third of all U.S.
From page 21...
... Rather, they want adherence to principles that have been adopted around the world to gain their trust, including transparency, being asked permission, having access to their own health information, and being provided with knowledge of how their information will be used. The Platform for Engaging Everyone Responsibly The nexus of issues associated with patient consent, patient privacy, and data access offers a variety of challenges to integrating genomics into the learning health care system.
From page 22...
... In between the data entry and the query components, a privacy layer serves as a filter to create and manage permissions for sharing patient data wherever the data reside and at any level of granularity. Participants establish their own sharing preferences based on a simple "stop light" metaphor: allow, deny, or ask me.
From page 23...
... Many of the policy issues facing the 100,000 Genomes Project are similar to the policy issues related to the use of genomic information in the United States, Fowler said. Patients are granting consent to link to lifetime health records, with the information being pseudo-anonymized in a research dataset.
From page 24...
... "This is an area that needs a lot more exploration," Peterson said. PLATFORM-SUPPORTED, COMPLETE LEARNING CYCLES In a health care system that can learn, every patient's characteristics and experiences are available for study, best practice knowledge is immediately available to support decisions, and improvement is continuous and routine, said Charles Friedman, the Josiah Macy Jr.
From page 25...
... SOURCE: Friedman, IOM workshop presentation on December 8, 2014.
From page 26...
... Simultaneous Learning Cycles To create health care systems that can learn, delivery systems and research networks must run many complete learning cycles simultaneously, Friedman said. This requires an infrastructure that makes learning effective, sustainable, and routine, with the accompanying economies of scale.
From page 27...
... "Everybody wants the same thing. The challenge is how to make these aligned interests into a set of activities that will bring the benefits that are commonly desired." Health Care Disparities Groups adversely affected by disparities could be among those that benefit most from a learning health care system, observed both Baker and Jason Vassy, a primary care physician and clinician–investigator at Harvard Medical School, the Veterans Affairs Boston Healthcare System, and Brigham and Women's Hospital.
From page 28...
... In addition, with heterogeneous data collected largely for other purposes, it was demonstrated that decreasing an individual's waist circumference and improving an individual's blood glucose levels produce the largest benefits for subsequent risk and medical costs for metabolic syndromes. Genomic data could greatly extend the benefits of this approach, Hill said.
From page 29...
... , which consists of researchers and clinical providers engaged in implementation research to improve the quality, safety, health equity, and outcomes of their patient populations. Since 2012, membership has more than doubled to roughly 250 people representing about 140 institutions.1 According to a survey performed shortly before the workshop, about 90 percent of ROCC members who conduct research are currently engaged in collaborative research projects with clinical colleagues, and about twothirds are currently engaged in collaborative research projects with teaching faculty, Ommaya said.
From page 30...
... Managing Priorities in a Health System Health systems are being inundated with new forms of data -- not just from genomics, but from gene expression profiles, proteomics, and other high-throughput technologies, said Fred Sanfilippo, the director of the Healthcare Innovation Program at Emory University and the Georgia Institute of Technology and a professor of pathology and laboratory medicine at the Emory University School of Medicine. Furthermore, many other factors besides a person's genetics affect health outcomes, including behaviors, social circumstances, and environmental exposures.
From page 31...
... "But the good news is it probably can be done." Promoting Health Services Research In the Healthcare Innovation Program at Emory University and the Georgia Institute of Technology, it has been a priority to expand health services research and education, Sanfilippo said. Existing programs were scattered across units and institutions, and the resources needed to establish a health services center were limited.
From page 32...
... Among the many reasons that physicians cite, Vassy highlighted in particular a lack of genomic knowledge, low self-efficacy, EHRs that are not equipped to incorporate genomic information, and a lack of evidence for clinical utility. The MedSeq Project was designed to explore this unpreparedness by engaging clinicians to use whole-genome sequencing results at Brigham and Women's Hospital.
From page 33...
... NOTE: DCM, dilated cardiomyopathy; GRC, Genome Resource Center; HCM, hypertrophic cardiomyopathy; PCP, primary care physician. SOURCE: Vassy, IOM workshop presentation on December 8, 2014.
From page 34...
... 34 GENOMICS-ENABLED LEARNING HEALTH CARE SYSTEMS TABLE 3-1 The 12 Online Genomics Educational Modules for Physicians Offered by the MedSeq Project Case Clinical Content Area Genomic Concepts 1 Familial hypercholesterolemia • Autosomal dominant and recessive • Modifying genes and penetrance 2 Maturity onset diabetes of the • Family history and pedigree young analysis • Monogenic forms of common disease 3 Myotonic dystrophy • Expansion repeat disease and anticipation • Variable expressivity 4 BRCA-related disease • Monogenic forms of common disease • Deletion as a mutation mechanism 5 Alzheimer's disease • Monogenic forms of common disease • Non-Mendalian genetic risk for common disease 6 Cystic fibrosis • Autosomal recessive carrier state • Incidental diagnosis of mild disease 7 Hypertrophic cardiomyopathy • Variants of unknown significance • Database variability 8 Clopidogrel pharmacogenomics • Cytochrome p450 genetics splice inducing mutations 9 Vascular Ehlers–Danlos syndrome • Ethical, legal, and social implications of genomic information • Genetic Information Nondiscrimina tion Act and Massachusetts genetic privacy law 10 Age-related macular degeneration • Genome-wide association studies and risk 11 Atrial fibrillation • Management advice in the setting of pre-symptomatic risk 12 Thoracic aortic aneurysm • Syndromic versus non-syndromic disease In discussing the results of the study, Vassy focused on the primary care providers. Among the first 10 healthy patients, 3 had a monogenic
From page 35...
... They understood some of the limitations of sequencing, both on the analytic side and regarding clinical decision making. Physicians who were asked to be part of the study worried about the amount of time it would take, Vassy said, but the researchers tried to integrate the project into the physicians' clinical care processes and not provide it as a separate research visit that was carved out of clinical time.
From page 36...
... What was learned from the study, Vassy said, was that in order to help translate genomic sequencing into improved patient outcomes, clinicians need just-in-time information, including test characteristics and limitations, guidelines or expert recommendations for decision making, and time limits on the validity of the information. "Genomic medicine is a rapidly evolving field, and what is true today may not be true 6 months from now, or 2 years, or certainly 5 years from now," he said.


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