Bioethics, business ethics, and science: Bioinformatics and the future of healthcare

Cambridge Quarterly of Healthcare Ethics

Volumn 17, Issue 4, 2008, Pages 361-372

  Goodman, Kenneth W ^a,b,c   Cava, Anita ^a  

^a UNIVERSITY OF MIAMI   (United States)

^b Bioethics Program

^c Department of Medicine ^*

Author keywords

[No Author keywords available]

Indexed keywords

BIOETHICS; BIOLOGY; BUSINESS ETHICS; CONSUMER HEALTH INFORMATION; ETHICS; HEALTH CARE DELIVERY; HUMAN; PHARMACOGENETICS; REVIEW; SOCIAL BEHAVIOR;

BIOETHICS; COMPUTATIONAL BIOLOGY; CONSUMER HEALTH INFORMATION; DELIVERY OF HEALTH CARE; ETHICS, BUSINESS; HUMANS; PHARMACOGENETICS; SOCIAL RESPONSIBILITY;

EID: 50249110547     PISSN: 09631801     EISSN: 14692147     Source Type: Journal    
DOI: 10.1017/S096318010808050X     Document Type: Review

References (43)

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4. Goodman KW, ed. Ethics, Computing and Medicine: Informatics and the Transformation of Health Care. New York and Cambridge: Cambridge University Press; 1998.
5. Miller RA. Why the standard view is standard: People, not machines, understand patients' problems. Journal of Medicine and Philosophy 1990;15:581-91.
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8. Here we defer an account of the debate over "genetic exceptionalism" or the contestable idea that genetic information is or should be regarded as different than other kinds of health data. At ground, those who argue against genetic exceptionalism contend that assigning special status to genetic information perpetuates bias and discrimination. This parallels the debate over HIV exceptionalism. See Juengst ET. FACE facts: Why human genetics will always provoke bioethics. Journal of Law, Medicine and Ethics 2004;32(2):267-75.
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13. See note 6, Goodman 2006:317. A fuller definition would expand on each of these properties; for instance, "analyze" is vague and ranges across a number of discrete functions and tasks. A number of different definitions have been proposed. Compare Fenstermacher D. Introduction to bioinformatics. Journal of the American Society for Information Science and Technology 2005;56:440-6.
14. See, for instance, Alpert SA. Privacy and the analysis of stored tissues. In: Research Involving Human Biological Materials: Ethical Issues and Policy Guidance, Volume II: Commissioned Papers. Rockville, MD: National Bioethics Advisory Commission; 2000:A1-A36.
15. Goodman KW. Ethics, information technology and public health: Duties and challenges in computational epidemiology. In: O'Carroll PW, Yasnoff WA, Ward ME, Ripp LH, Martin EL, eds. Public Health Informatics and Information Systems. New York: Springer-Verlag; 2003:251-66.
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20. Here is how GWAS are described at the government site, http://www.genome.gov/20019523: To carry out a genome-wide association study, researchers use two groups of participants: people with the disease being studied and similar people without the disease. . . . Each person's complete set of DNA, or genome, is then purified from the blood or cells, placed on tiny chips and scanned on automated laboratory machines. The machines quickly survey each participant's genome for strategically selected markers of genetic variation, which are called single nucleotide polymorphisms, or SNPs. . . . If certain genetic variations are found to be significantly more frequent in people with the disease compared to people without disease, the variations are said to be "associated" with the disease. The associated genetic variations can serve as powerful pointers to the region of the human genome where the disease-causing problem resides.
21. See note 7, Fenstermacher 2005:441.
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24. See note 6, Goodman 2006.
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29. The HapMap web site is a masterpiece of accessible science and introductory ethics: http://www.hapmap.org/index.html.en.
30. See note 6, Goodman 1996.
31. See, for instance, http://www.hapmap.org/ethicalconcerns.html.en: Since the samples include no personal identifiers, the risk to individual donors that privacy might be compromised is minimal. However, because each sample is identified as coming from a particular population, . . . group stigmatization and discrimination may occur from studies that use the HapMap. For example, researchers may find that a genetic variant associated with a higher-than-average risk of a disease is more common in one population than another. This information may be misinterpreted to mean that every member of a group has a higher-than-average risk of the disease, even though the higher risk may apply only to those individuals, inside the group or out, who have that variant. . . . Also, genetic findings could undermine established cultural or religious traditions or legal or political status. Many groups have firm beliefs about the origin of the group or about the relationship of the group to other groups, and these beliefs may be challenged by findings built on the use of the HapMap. In addition, genetic findings may conflict with the social and cultural methods that groups have developed to determine who is a member of that group.
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34. See note 19, Swen et al. 2007.
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