Eric M. Meslin, Ph.D. Director, Indiana University Center for Bioethics

1. Data Sharing in Health and Science:Something to Fear or Embrace?April 8 2014Centre for Medical Ethics and LawUniversity of Hong Kong Eric M. Meslin, Ph.D. Director, Indiana University Center for Bioethics Associate Dean for Bioethics, IU School of Medicine Professor of Bioethics

2. “The more the data banks record about each one of us, the less we exist.”

3. “Consumer scores – created by either the government or the private sector – threaten privacy, fairness, and due process because scores, particularly opaque scores with unknown ingredients or factors, can too easily evade the rules established to protect consumers”.

4. Outline • Conceptual understanding of data (big and small) • Data on data sharing • Comment on the value of the fear vs embrace data sharing dichotomy • Promising approaches for balancing privacy protection and public health promotion

5. Support:Collegial and Financial • UL1RR025761-01/NIH Indiana Clinical and Translational Sciences Institute • Pierre de Fermat Chaire d’Excellence Program, Midi-Pyrénées Regional Council, France • State Grants to Promote Health Information Technology (Health Information Exchange Challenge Program) Award No: 90HT0054/01, CFDA • IU Center for Law, Ethics, and Applied Research in Health Information (CLEAR) which receives funding from the Lilly Endowment, Inc. • Anne Cambon-Thomsen • Fred Cate • Stan Crosley • Brad Doebbling • Ken Goodman • Jane Kaye • BarthaKnoppers • Kimberly Quaid • Josh Rager • Peter Schwartz • AnanthaShekhar • William Tierney

6. “Big data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze.” “If US healthcare were to use big data creatively and effectively to drive efficiency and quality, the sector could create more than $300 billion in value every year.”

7. “… the products of research [or observation], be they the mathematical calculations or findings of a quantitative intervention (e.g., the number count of patients who responded to a therapy, or the percentage of tumour reduction achieved by chemotherapy); or the experiential findings of qualitative research (e.g., the descriptions that families give of how they cope with loss).”

8. Data ~ Information ~ Knowledge

10. X 54

12. 4.7 GB

14. Hospital • Physician Offices • Insurance Co. • Hospital Billing • Physician Billing • WebMD • Facebook • YouTube • Gmail • PatientsLikeMe • Pharmacy • Google • CancerNet

18. Physicians and Nurses—Pediatric Biobanking • MD and RN attitudes toward pediatric biobanks are similar. • Broad support for a pediatric biobank from HCP, including support for unspecified use of samples. Denne, Wolf , Meslin et al (2008) Women and Pregnant Mothers • 77% felt predictive health research was worthwhile • Most supported consent for future use Haas, Renbarger, Meslin, Drabiak, Flockhart (2006) IU Cancer Patients • ~85% agreed that stored tissue could be used in unspecified future research • 60-70% would not require re-contact each time tissues were used Helft, Champion, Eckles, Johnson, Meslin, (2007)

19. “When people in the general community were asked if they approved of their information being used in this way, they were found to be not only supportive of it, but they questioned why it was not already being done.” Stanley and Meslin (2007)

20. Five Big Questions 1. Who Wants Access? public, private researchers/institutions 2. What Specifically Do They Want? sensitive, identifiable, de-identified, linked 3. When Do They Want It (And For How Long)? 4. Where Will The Data Be Used? domestic or offshore 5. Why Do They Want It? research, treatment, product development

21. What About Researchers?

22. No incentives to deposit such data Having no place to put data Lack of standards Sponsor doesn’t require it No data management plan required by funder Long term data storage process dissatisfcation Dissatifacation with metadata tools Long term data storage support dissatisfication Data may be misinterpreted due to complexity of and poor quality of data Don’t use metadata standards Participation in another consortium Not legally possible Seen as unnecessary and time consuming Insufficient time Lack of funding too busy, too much work no longer had jurisdiction over data as had changed institutions (institution stated: too long to organize and annotate and too much work) forbidden to pass on to third parties Having received a high number of requests Trainees in high-competition labs more likely to have been denied access Perceived competition in group led to withholding info from others • Reorganization of data files for harmonization • Burdensome data retrieval • Inadequate technical resources • Lack of human resources • Lack of financial resources • Difficult to obtain permission from home institute (infrequent) • Difficult to obtain permission from ethics board • Tight deadline for participation • Unclear data sharing policy • Confidentiality of data (not frequent) • Privacy of the data (not frequent) • Data used for purposes not compliant with the original consent • Data shared for other research purposes • Data used for commercial purposes • Contribution would not be recognized • Data already been used for same research purposes • Deadlines were too tight • The data that could have been used became available too late • No human resources available to submit data • Project was not within the scope of own research • Effort required to actually produce materials or information

23. Reasons, Factors and Considerations Researchers Give for Not Sharing Data • Policies or Rules Prevent Data Sharing • Logistical Arrangements Inhibit Data Sharing • Personal, Professional Reasons Disincline Data Sharing

24. Policies/Rules Prevent Data Sharing • Collaboration agreements with industry • Conditions placed on grant awards or contracts • Regulatory policies for protecting privacy

25. Logistical Pragmatic Arrangements Inhibit Data Sharing • Insufficient protected time or human resources • Insufficient infrastructure for physical transfer • Inconsistent standards

26. Personal/Professional Reasons Disincline Data Sharing • Receiving credit in publications, discoveries • Fear of losing scientific edge or advantage • Worries about misuse of “their” data • Concern for protection of privacy/welfare of others

27. On the other hand...inconsistency leads to fear • Researchers are interested in data sharing and recognize its importance and benefits • Truth value of reasons given can be questioned • An unquestioned belief in the power of technology to solve problems

29. “Too little privacy can endanger democracy. But so too can too much privacy”

30. “Finally, at some point most patients should have an opportunity to review their medical record and to make informed choices about whether their entire record is to be available to everyone or whether certain portions of the record are privileged and should only be accessible to their principal physician or to others explicitly designated by the patient.”

32. Good Governance:Public Engagement • Simple engagement • Provide information to public prior to consent • Moderate engagement • Involve the public in discussions prior to developing a bank about policy • Complex engagement • Public given authority to develop priorities for bank

33. Shared norms and Policy Platforms • Focused on real problem • Built from an evidence base • Involve public and experts • Grounded in good science and good ethics

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