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Bioethics, Business Ethics, and Science: Bioinformatics and the Future of Healthcare

Published online by Cambridge University Press:  01 October 2008

KENNETH W. GOODMAN  and
ANITA CAVA
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Extract

The intersection of ethics, computing, and genetics plots a space not yet adequately mapped, despite its importance, indeed, its rapidly growing importance. Its subdomains are well-enough known: “Genethics,” or the study of ethical issues in genetics and genomics, is part of core curricula everywhere. Ethics and computing is an established subfield. Computing and genetics—bioinformatics—has in little more than a decade progressed from subsubspecialty to the sine qua non of contemporary biomedical research, and it bids fair to transform clinical practice. We must prepare for the complete digitization of the genomes of individual patients and the storage of millions of these genomes in very large databases.

Type
Special Section: The Newest Frontier: Ethical Landscapes in Electronic Healthcare
Information
Cambridge Quarterly of Healthcare Ethics , Volume 17 , Issue 4 , October 2008 , pp. 361 - 372
Copyright
Copyright © Cambridge University Press 2008

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References

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3 Goodman KW, Miller RA. Ethics and health informatics: Users, standards, and outcomes. In: Shortliffe EH, Cimino JJ, eds. Biomedical Informatics: Computer Applications in Health Care and Biomedicine, 3rd ed. New York: Springer; 2006:379–402.

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5 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.

6 Goodman KW. Ethics, genomics, and information retrieval. Computers in Biology and Medicine 1996;26:223–9; Tavani HT. Ethics at the intersection of computing and genomics. In: Tavani HT, ed. Ethics, Computing and Genomics. Boston: Jones and Bartlett; 2006:5–26; Goodman KW. Bioinformatics: Challenges at the frontier. In: Tavani HT, ed. Ethics, Computing and Genomics. Boston: Jones and Bartlett; 2006:317–21; De Bouvet A, Deschamps C, Boitte P, Boury D. Bioinformatics: The philosophical and ethical issues at stake in a new modality of research practices. Medicine, Health Care and Philosophy 2006;9:201–9.

7 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.

8 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.

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10 Goodman KW. Moral foundations of data mining. In: Wang J, ed. Encyclopedia of Data Mining. Hershey, PA: IDEA Group Reference; 2006:832–36.

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13 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.

14 See note 7, Fenstermacher 2005:441.

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17 See note 6, Goodman 2006.

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20 But compare Grossman I. Routine pharmacogenetic testing in clinical practice: Dream or reality? Pharmacogenomics 2007;10:1449–59.

21 Jurisica I, Wigle DA, Wong B. Cancer Informatics in the Post Genomic Era: Toward Information-Based Medicine. New York: Springer; 2007:4. Note the authors’ assessment that cancer research using high-throughput genome analysis tools has increased nearly 20 times in less than a decade.

22 The HapMap web site is a masterpiece of accessible science and introductory ethics: http://www.hapmap.org/index.html.en.

23 See note 6, Goodman 1996.

24 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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26 See note 19, Swen et al. 2007.

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35 McLaughlin N. Welcome to accountability. Modern Healthcare, 11 Jun 2007, Issue 24:19.