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Mitchell Internet Play Nov2008


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Robert Mitchell (Duke University), "Ends and Means: Digital Labor in the Context of Health," Presentation for "The Internet as Playground and Factory," The New School, November 13, 2009

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Mitchell Internet Play Nov2008

  1. 1. Ends and Means: Digital Labor in the Context of Health<br />The Internet as Playground and Factory<br /> Robert Mitchell, Duke University<br /><br /> November 13, 2009<br />
  2. 2. Overview<br />I. Background<br />The “promise” of genomics & “personalized medicine”<br />Bayh-Dole & the Liberalist Competition State <br />II. Strategies for harnessing the promise of genomics<br />1. State/corporate de novo efforts (e.g., UK Biobank)<br />2. networking existing small collections<br />3. Repurposing existing collections or protocols<br />III. Clinical Labor<br />Model 1: short-term intensive extraction<br />Model 2: ongoing minimally invasive “skimmings”<br />IV. Ends, Means & Images of Health<br />
  3. 3. Relationship to “Internet as Playground and Factory”<br />Within our “information society,” similar dynamics and paradoxes emerge in multiple arenas (see e.g., James Boyle, Shamans, Software, and Spleens)<br />Significant similarities between digital information- gathering/updating techniques in commerce and medicine <br />Active patient participation in health projects not unlike active participation in sites like Facebook<br />Yet emphasis on “health” (rather than “play”) focuses our attention on different aspects of these information dynamics<br />
  4. 4. I. Background<br />A. The “promise” of genomics:<br />Completion of Human Genome Project (2000) encouraged belief in the “promise” of genetic/personalized medicine<br />But this “promise” is often presented as depending in part on creating very large collections of specimens and associated data (e.g., deCODE Genetics, Inc.’s use of existing Icelandic national genetic data)<br />Increasing importance of “environmental” data (lifestyle choices—e.g., smoking—and environmental exposures) linked to genetic profiles and health events (e.g., illnesses)<br />
  5. 5. I. Background<br />Environmental data<br />Genetic analysis<br />Health events<br />In general, establishing significant linkages between environmental factors, health events, and genetic differences requires very large numbers of subjects (100s of thousands)<br />
  6. 6. I. Background—cont.<br />B. Genomic and Personalized Medicine unfolding within a system established/codified by Bayh-Dole Act (1980)<br />Purported “innovation crisis” in the U.S. in the 1970s<br />Legislative solution: make it easier for universities to patent “inventions” produced with U.S. grant money<br />Led to current system of “technology transfer offices”<br />Basic principles of this system exported to other countries via Trade Related Aspects of Intellectual Property Rights (TRIPS) agreements and other mechanisms<br />
  7. 7. Bayh-Dole “Innovation Ecology”<br />patents<br />“Pure” Research Institutions<br />Corporations<br />$<br />$<br />$(taxes)<br />Drugs/ therapies<br />Genetic information and biological samples<br />Public<br />(see Waldby and Mitchell 2006)<br />
  8. 8. II. How to harness the “promise” of genomics?<br />The problem: how to create large collections of human genetic material linked—in an ongoing way—with health records and environmental data?<br />Strategy 1: create a de novo “biobank” (e.g., UK Biobank: aiming at 500,000 volunteer subjects, who will be followed for 20 years)<br />Advantage: tissue and data uniformity<br />Disadvantages: very expensive (requires state funding) <br />Proposals for such a project in the U.S. (see Collins 2004, 2007) but not clear whether there is legislative will for this<br />
  9. 9. II. How to harness the “promise” of genomics?<br />Strategy 2: use bioinformatic tools to “network” many smaller existing collections of tissue/data<br />Advantages: (a) less costly than the de novo approach (one can use existing tissue collections); (b) can be expanded indefinitely; (c) history of success with “networks” in medicine (e.g., NCI’s Cooperative Human Tissue Network (CHTN)<br />Disadvantage: not clear whether data uniformity can be established across different collections (counter-example of National Biospecimen Network)<br />
  10. 10. II. How to harness the “promise” of genomics?<br />Strategy 3: “repurpose” existing collection or protocols<br />E.g., of repurposed collection: Genome Austria Tissue Bank (GATiB)<br />E.g., of repurposed protocols: efforts at many U.S. hospitals to create collection from “waste” blood and data collected during normal clinical visits<br />Advantages: less costly than the de novo approach (one can use existing tissue collections)<br />Disadvantages: (a) data may not be set up for biobank purposes; (b) environmental data often difficult to gather; (c) informed consent issues become even more complex than usual<br />
  11. 11. III. Clinical Labor in the context of Biobanking<br />Biobanks understood as national or regional “resources” in both research and economic senses<br />These “resources” are potentially in competition with one another <br />Assertion: the economic/research value of these resources do not recognize, but nevertheless depend upon, the “clinical labor” of donors<br />“Clinical labor”: processes in which subjects give clinics and commercial biomedical institutions access to their in vivo and in vitro biology, the biological productivity of living tissues within and outside their bodies (Waldby & Cooper 2008)<br />
  12. 12. III. Clinical Labor in the context of Biobanking<br />Two models of clinical labor:<br />Model 1--intensive and isolated processes of labor (e.g. serving as cell line source; clinical trial participation; selling of oöcytes)<br />Model 2--extensive and distributed processes of labor, with little bodily risk (e.g., participation in biobank)<br />Potential critique: is the term “labor” applicable here? Though these activities/practices of patients are indeed related to economic value, are themselves “labor”?<br />do these activities/practices themselves “transform” anything?<br />and does “intention” lie behind these activities? <br />
  13. 13. III. Clinical Labor in the context of Biobanking<br />A continuum of practices between Model 1 and Model 2:<br />Serving as a cell line source: transformation happens after the extraction of tissue; little if any intention on the part of the subject <br />Participating in a biobank: ongoing relation to value creation; significant intention<br />Acknowledging “labor” of biobank participants doesn’t mean that participants necessarily should be paid for “donations”<br />E.g., Winickoff & Winickoff’s (2003) suggestion to treat biobanks in terms of “charitable trust” model<br />But: danger that “solutions” will still be cast within neo-liberal models<br />
  14. 14. IV. Ends, Means & Images of Health<br />The necessarily “collective” aspect of biobanks can provide an opportunity for rethinking relationship between labor, digital technologies, value-creation, and distribution of benefits<br />In general, participation in biobanks is voluntary, whether explicitly so (e.g., UK Biobank) or “legalistically” so (e.g., repurposing clinical visits): MAKE MEMBERSHIP EXPLICIT<br />Research/economic value of biobanks depends upon same sorts of data collection and analysis protocols as, e.g., Facebook (active intentional updating); (passive updating; locating homologies); etc.: INCREASE ALGORHYTHMIC LITERACY<br />“Rewards” of biobank participation cast, and generally understood by participants, as at the level of the “collective” (“for good of all”--“species being”?): ENABLE SENSE OF COLLECTIVE<br />
  15. 15. IV. Ends, Means & Images of Health<br />But: geneticemphasis of biobanks tends to promote an image of “health” tied to pharmaceutical commodity creation<br />Emphasis on products for “risk factors” (which points to a whole new market)<br />Products for risk factors enable what Joe Dumit calls “surplus health” (that “proportion of health unnecessary for maintaining one’s capacity as a worker”) (Dumit, 2004)<br />“Health” often understood as “ increasing number of years of life”<br />
  16. 16. IV. Ends, Means & Images of Health<br />The questions that we can use biobanks to pose (and solicit collective answers):<br />Is “health” a means or an end? (I.e., what is the role of “health” in mediating between “play” and “work”?)<br />What constitutes a “good life”? <br />If medical understanding of “health” depends upon the active participation of populations, how then can populations be the beneficiaries of their participation?<br />my premise: this cannot come about solely through the neoliberal “magic” of product innovation<br />
  17. 17. References<br />Boyle, J. 1996. Shamans, Software, and Spleens: Law and the Construction of the Information Society. Cambridge, Massachusetts: Harvard University Press.<br />Dumit, J. (2004) “Drugs, algorithms, markets and surplus health.” Workshop paper presented at the Department of Anthropology. University of California, Irvine.<br />Collins, F. 2004. “The case for a US prospective cohort study of genes and environments.” Nature, 429: 475-79.<br />-----. 2007. “Necessary but not sufficient.” Nature 445: 259.<br />R. Mitchell and C. Waldby. (forthcoming). “National Biobanks: Clinical Labour, Risk Production, and the Creation of Biovalue,” Science, Technology, and Human Values. <br />Waldby, C. and M. Cooper. 2008. “The biopolitics of reproduction: post-Fordist biotechnology and women’s clinical labour.” Australian Feminist Studies 23: 57-73.<br />Waldby, C. and R. Mitchell. 2006. Tissue Economies: Blood, Organs and Cell Lines in Late Capitalism.Durham, Duke University Press.<br />Winickoff, D.E. & R.N. Winickoff. 2003. “The Charitable Trust as a Model for Genomic Biobanks.” New England Journal of Medicine 349 (12): 1180-1184.<br />