The Politics of Personal Health Data

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From my Sanger lecture at Virginia Commonwealth University. On the emerging politics of personal health data.

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The Politics of Personal Health Data

  1. 1. the emerging politics of personal data @wilbanks 2/18/14
  2. 2. http://www.technologyreview.com/featuredstory/520426/the-real-privacy-problem/
  3. 3. 1. give me a place to stand, and a lever, and i shall move the world.
  4. 4. every age has its own lever.
  5. 5. ours is cheap data.
  6. 6. cheap data changes how we justify our opinions.
  7. 7. OPS = AB(H + BB + HBP) + TB(AB + BB + SF + HBP) / AB(AB + BB + SF + HBP)
  8. 8. cheap data is changing our politics.
  9. 9. cheap data is going to change our health.
  10. 10. 2. research data v. cheap consumer data
  11. 11. https://www.scienceexchange.com/
  12. 12. 3. increasing tensions.
  13. 13. tension between anonymity and utility.
  14. 14. tension between expectation and reuse.
  15. 15. tension between aggregate value and individual value.
  16. 16. if it can be sold, it will be sold at the lowest possible price.
  17. 17. maybe capital is the wrong metaphor.
  18. 18. tension between technology rate of change and policy rate of change.
  19. 19. our regulatory environment
  20. 20. 4. “make an app for that” isn’t enough in health.
  21. 21. 35
  22. 22. 36
  23. 23. 37
  24. 24. 38
  25. 25. assume 1,000,000 downloads assume 10% false positive rate 100,000 doctor visits $1000 per biopsy
  26. 26. that’s the setup.
  27. 27. 5. ! we need freedoms, not just free stuff, for data to change health for the better.
  28. 28. freedoms granted to small but coherent groups can create asymmetrically valuable resources.
  29. 29. small group sharing
  30. 30. proven to work in: software content
  31. 31. let’s try a small but coherent group to share data and see if it works in breast cancer.
  32. 32. code sharing a prerequisite.
  33. 33. accuracy of model jumped three orders of magnitude in nine days.
  34. 34. 76% accurate. 51
  35. 35. 21 february 2013 17 april 2013 ongoing...
  36. 36. SHOW ME THE CODE!
  37. 37. ...
  38. 38. ...
  39. 39. ...
  40. 40. ...
  41. 41. ...
  42. 42. let’s try a small but coherent group to share data and see if it works in “big science”.
  43. 43. TCGA Pan-Cancer Consortium Analysis of: 12 Tumor types, 6 molecular profiling platforms ! Focus series of: 4 papers in Nature Genetics, with 14 more to follow in other NPG journals Omberg,  et  al.  Nature  Gene*cs
  44. 44. 68 core projects
  45. 45. 1070 datasets
  46. 46. 1723 results
  47. 47. 18 papers in press
  48. 48. let’s try a small but coherent group to share data and see if it works in health.
  49. 49. “eat less and exercise”
  50. 50. the experiment:
  51. 51. all boxes must be checked volunteer must click to proceed
  52. 52. http://opensnp.org/users/615
  53. 53. http://files.snpedia.com/reports/promethease_data/genome_jtw_ui2.html
  54. 54. “Also there is no suggestion of consanguinity in your pedigree.” ! http://www.ianlogan.co.uk/
  55. 55. (not so good)
  56. 56. requires coherence and scale - easier to enforce in closed systems…
  57. 57. 5. someone’s going to achieve coherence and scale.
  58. 58. image via http://jawbone.com/
  59. 59. image via http://macrumors.com/
  60. 60. but will we be allowed to opt out? image via http://gawker.com
  61. 61. thus we have to talk about the politics of data.
  62. 62. three choices for coherence and scale.
  63. 63. a. “just like now, but moreso”
  64. 64. b. the cartel.
  65. 65. c. an open system.
  66. 66. 89
  67. 67. thank you ! @wilbanks john.wilbanks@sagebase.org
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