1 Szolovits P, Pauker SG. Computers and clinical decision making: Whether, how much, and for whom? Proceedings of the IEEE 1979;67:1224–6; Miller RA, Schaffner KF, Meisel A. Ethical and legal issues related to the use of computer programs in clinical medicine. Annals of Internal Medicine 1985;102:529–36; de Dombal FT. Ethical considerations concerning computers in medicine in the 1980s. Journal of Medical Ethics 1987;13:179–84; Goodman KW, ed. Ethics, Computing and Medicine: Informatics and the Transformation of Health Care. New York and Cambridge: Cambridge University Press; 1998.
2 Miller RA. Why the standard view is standard: People, not machines, understand patients’ problems. Journal of Medicine and Philosophy 1990;15:581–91.
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.
4 Alpert SA. Health care information: Access, confidentiality, and good practice. In: Goodman KW, ed. Ethics, Computing, and Medicine: Informatics and the Transformation of Health Care. New York and Cambridge: Cambridge University Press; 1998:75–101.
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.
9 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.
10 Goodman KW. Moral foundations of data mining. In: Wang J, ed. Encyclopedia of Data Mining. Hershey, PA: IDEA Group Reference; 2006:832–36.
12 Arnason V. Coding and consent: Moral challenges of the database project in Iceland. Bioethics 2004;18(1):27–49; Merz JF, McGee GE, Sankar P. “Iceland Inc.”? On the ethics of commercial population genomics. Social Science and Medicine 2004;58(6):1201–9.
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.
15 Marturano A, Chadwick R. How the role of computing is driving new genetics’ public policy. Ethics and Information Technology 2004;6:43–53.
17 See note 6, Goodman 2006.
18 Kafatos FC, Eisner T. Unification in the century of biology. Science 2004;303:1257.
19 Swen JJ, Huizinga TW, Gelderblom H, de Vries EG, Assendelft WJ, Kirchheiner J, Guchelaar HJ. Translating pharmacogenomics: Challenges on the road to the clinic. PLoS Medicine 2007;4(8):e209.
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.
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.
25 Court MH. A pharmacogenomics primer. Journal of Clinical Pharmacology 2007;47:1087–103; McCarthy AD, Kennedy JL, Middleton LT. Pharmacogenetics in drug development. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 2005;360:1579–88.
26 See note 19, Swen et al. 2007.
27 Phillips KA. The intersection of biotechnology and pharmacogenomics: Health policy implications. Health Affairs 2006;25:1271–80.
28 Malinowski M, Rao R. Legal limitations on genetic research and the commercialization of its results. American Journal of Comparative Law 2006;54:45–65.
29 Evans JP. Health care in the age of genetic medicine. JAMA 2007;298:2670–72.
30 Foster MW, Mulvihill JJ, Sharp RR. Investments in cancer genomics: Who benefits and who decides? American Journal of Public Health 2006;96:1960–64.
31 Willard HF, Angrist M, Ginsburg GS. Genomic medicine: genetic variation and its impact on the future of health care. Philosophical Transactions of the Royal Society of London. Series B, Biological sciences 2005;360:1543–50.
32 D'Souza S. Gene meets machine: Intellectual property issues in bioinformatics. Health Law Review 2004;12(2):34–43.
33 Moyer TJ, Anway SP. Biotechnology and the bar: A response to the growing divide between science and the legal environment. Berkeley Technology Law Journal 2007;22:671–731.
34 Werther WB, Chandler D. Strategic Corporate Social Responsibility: Stakeholders in a Global Environment. Thousand Oaks, CA: Sage Publications Inc.; 2006.
35 McLaughlin N. Welcome to accountability. Modern Healthcare, 11 Jun 2007, Issue 24:19.