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Barbara Evans, "Big Data and the Meaning of Individual Autonomy in a Crowd"

Part of the "2016 Annual Conference: Big Data, Health Law, and Bioethics" held at Harvard Law School on May 6, 2016.

This conference aimed to: (1) identify the various ways in which law and ethics intersect with the use of big data in health care and health research, particularly in the United States; (2) understand the way U.S. law (and potentially other legal systems) currently promotes or stands as an obstacle to these potential uses; (3) determine what might be learned from the legal and ethical treatment of uses of big data in other sectors and countries; and (4) examine potential solutions (industry best practices, common law, legislative, executive, domestic and international) for better use of big data in health care and health research in the U.S.

The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School 2016 annual conference was organized in collaboration with the Berkman Center for Internet & Society at Harvard University and the Health Ethics and Policy Lab, University of Zurich.

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Barbara Evans, "Big Data and the Meaning of Individual Autonomy in a Crowd"

  1. 1. 1 Big  Data  and  Individual  Autonomy  in  a   Crowd   Barbara  J.  Evans,  Ph.D.,  J.D.,  LL.M. Director,  Center  for  Biotechnology  &  Law University  of  Houston  Law  Center 713-­743-­2993  • Big  Data,  Health  Law,  and  Bioethics May  6,  2016
  2. 2. Source: files/1324689388/i-­‐269-­‐7d9-­‐9-­‐2.jpg   1. Protecting  one’s  planet  from  external  impacts* 2. Marshaling  their  data  to  learn  about  themselves Grand  challenges  all  advanced  civilizations  meet   in  the  course  of  living  long  enough  to   become  an    advanced    civilization   *  Rusty  Schweickart,  B612  Foundation  
  3. 3. The  Norm  of  Common  Purpose*   The  moral  framework  for  21st-­century  science   may  differ  significantly  from  traditional   conceptions  of  clinical  and  research  ethics A  “norm  of  common  purpose…  a  principle   presiding  over  matters  that  affect  the  interests   of  everyone”   “Securing  these  common  interests  is  a  shared   social  purpose  that  we  cannot  as  individuals   achieve”     *  Faden,  Kass,  et  al.,  Hastings  Ctr.  Rep.  Supp  (Jan-­Feb  2013)
  4. 4. The  Most  Valuable  Data  Resources  for   21st Century  Informational  Research • Deeply  descriptive:  integrates  data  from  many   different  data  sources  to  provide  a  detailed   characterization  of  each  included  individual • Large-­scale:  reflects  many  different  individuals • Inclusive:  include  everybody  or  almost  everybody   to  capture  rare  events,  minimize  selection  bias,   support  hypothesis-­free  testing  where  we  do  not   know  ex  ante  which  people,  events,  or  traits  hold   the  answer.  
  5. 5. Consumers  → ↓  Data  Holders M A R Y P A U L A M Y J A C K J O H N M A Y A D A N S U E Insurer  1 • • • • Insurer  2 • • • • • Clinic  1 • • • • Clinic  2 • • • • • • Hospital  1 • • • Hospital  2 • • • • • Clinical  Lab  1 • • • • • Clinical  Lab  2 • • • • Research  Lab • • • • DTC  Lab • • • • At  home  sensor • • • • Fitness  Tracker • • • •
  6. 6. What  Are  Information  Commons? Data   Ownership Data   Commons Public   Domain Data  commons  are  not  the  data  resources   themselves   They  are  institutional  arrangements  (like   laws  or  agreements)  to  facilitate  collective   action  to  create  and  sustain  the  data   resources
  7. 7. 7 Major  US  Federal  Regulations     affecting  privacy  and  data  access   • Common  Rule  consent  requirements  for  use  of   data  and  tissues – 45  CFR  Part  46,  Subpt.  A – OHRP  Guidance  (August  2004  as  updated) – NPRM  (Sept.  8,  2015) • HIPAA  individual  authorization  requirements  for   use  of    protected  health  information • FDA  human-­subject  protections  and  medical   device  regulations  affecting  biospecimen use   PLUS:    NEVER  OVERLOOK  STATE  LAW!!
  8. 8. consents to  data  use no  consent to  data  use willing to  share  data Quadrant  1:  consent   alignment Data  holder  and   individual  data  subject   both  consent  to  the   data  use,  possibly  in   response  to  policy   incentives Quadrant  2:  data-­‐ holder-­‐driven  access Data  holder  invokes   regulatory  exceptions   to  individual  consent   (e.g.,  de-­‐identification,   public  health  uses,   consent  waivers)   not  willing  to   share  data Quadrant  3:  consumer-­‐ driven  access Individuals  invoke   access-­‐forcing   mechanisms  (e.g.,   HIPAA’s  individual   access  right)  and   contribute  their  data Quadrant  4:   legislated  access Access  under  laws  that   require  mandatory   data  access  for  specific   public  health  or   regulatory  purposes Individual Data   holder Shared  Control  of  Stored  Data