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Nicolas Terry, "Big Data, Regulatory Disruption, and Arbitrage in Health Care"

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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.

Learn more at http://petrieflom.law.harvard.edu/events/details/2016-annual-conference.

Published in: Healthcare
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Nicolas Terry, "Big Data, Regulatory Disruption, and Arbitrage in Health Care"

  1. 1. Regulatory Disruption and Arbitrage in Healthcare Data Protection @nicolasterry Hall RenderProfessor of Law & Executive Director of the Hall Center for Law and Health Indiana University Robert H. McKinney School of Law
  2. 2. • Increasingly large amounts of sensitive healthcare data exist in lightly regulated space outside reach of traditional healthcare data protection • Results: • Regulatory disruption as stakeholders struggle with under- regulation and indeterminacy • For data brokers, successful regulatory arbitrage enabled; doing in lightly-protected space what they are prohibited from doing in HIPAA-space • Deprecation of traditionally high levels of health data protection and challenges to ACA principles • Proposals for legislative reform generally have failed to gain traction • Specific and general powers of FTC remain the primary defenses against big data abuses.
  3. 3. Clinical: Point of Care Clinical: Operations Research: Outcomes Research: Intramural/ Corporate Research: Population Health Caveats
  4. 4. Sectoral Data Protection Health Care Financial Services Genetic Privacy Internet Other HIPAA GLBA GINALaw Agency HHS-OCR FTC/SEC/ Banking EEOC FTC General (but limited) Broadband FCC
  5. 5. Anonymization: Identity not Associated with Data Inalienablility; Designed to Disincent Collection Privacy; Limits on Collection Right to Erasure; Selective Removal Regulation at Point of Use; e.g., anti-discrimination Security; Repelling Outsiders (Identity Thieves) Confidentiality; Limits on Disclosure Breach Notification; “The Horse Has Left the Barn” Upstream Downstream
  6. 6. Disclosers “Payment or  Healthcare   Operations” Business Associates Care  Team Traditional   Health  Data   Space
  7. 7. http://thedatamap.org/
  8. 8. “We have one of the largest and most comprehensive collections of healthcare information in the world, spanning sales, prescription and promotional data, medical claims, electronic medical records and social media. Our scaled and growing data set, containing over 10 petabytes of unique data, includes over 85% of the world’s prescriptions by sales revenue and approximately 400 million comprehensive, longitudinal, anonymous patient records. We standardize, organize, structure and integrate this data by applying our sophisticated analytics and leveraging our global technology infrastructure to help our clients run their organizations more efficiently and make better decisions to improve their operational and financial performance.” IMS  Health   Holdings,   Inc.,  Form  S-­‐‑1,  Registration  Statement   under   The  Securities  Act  Of  1933,
  9. 9. Scoring personal health.…In 2014, there were at least a dozen health scores available in the marketplace, including the Affordable Care Act (ACA) Individual Health Risk Score, FICO Medication Adherence Score, several frailty scores, personal health scores (e.g., WebMD, One Health Score), and medical complexity scores (e.g., Aristotle for scoring of surgery for congenital health conditions). Consumers are largely unaware of the existence and use of these scores and the algorithms that create them. [A] “body score” may someday be even more important than your credit score. Mobile medical apps and social networks offer powerful opportunities to find support, form communities, and address health issues. But they also offer unprecedented surveillance of health data, largely ungoverned by traditional health privacy laws (which focus on doctors, hospitals, and insurers). Furthermore, they open the door to frightening and manipulative uses of that data by ranking intermediaries— data scorers and brokers— and the businesses, employers, and government agencies they inform.
  10. 10. • The ACA prohibits pre-existing condition exclusions, discriminatory premium rates, and generally requires guaranteed issue • Guaranteed issue and related regulations generally do not apply to life insurers who are customers for big data proxies • Health insurers who use data-mined prescription drug data to continue their discrimination against high cost patients • There is evidence that insurers move drugs associated with patients with expensive chronic conditions to high cost- sharing tiers in the hope of discouraging those patients from applying for coverage • Unregulated big data has the potential to frustrate some of the mainstay policies of our healthcare system.
  11. 11. Reform Incoherence 1. The White House,Consumer Data Privacy in a Networked World:A Framework for Protecting Privacy and Promoting Innovation in the Global Digital Economy (2012) 2. Fed. Trade Comm’n,Protecting Consumer Privacy in an Era of Rapid Change:Recommendations for Businesses and Policymakers (2012) 3. The White House,The Big Data and Privacy Review (2014) 4. Fed. Trade Comm’n,Data Brokers: A Call for Transparency and Accountability (2014) 5. PCAST, Big Data and Privacy: A Technological Perspective (2014) 6. Administration Discussion Draft:Consumer Privacy Bill of Rights Act of 2015 7. Health IT Policy Committee,Privacy and Security Workgroup,Recommendations on Health Big Data (2015) 8. The White House,Big Data:Seizing Opportunities,Preserving Values (2015) 9. FTC, Big Data: A Tool for Inclusion or Exclusion? (2016) 10.The White House,Big Data:A Reporton Algorithmic Systems, Opportunity, and Civil Rights (2016)
  12. 12. • FCRA • ECOA • FTC § 5(a)(1) • Deceptive Prong/Unfai r Prong • LabMD • Wyndham https://www.ftc.gov/system/files/documents/re ports/big-data-tool-inclusion-or-exclusion- understanding-issues/160106big-data-rpt.pdf
  13. 13. Q & A Mail: npterry@iupui.edu Twitter: @nicolasterry Podcast: TWIHL.com Nicolas Terry Hall Render Professor of Law & Executive Director of the Hall Center for Law and Health Indiana University Robert H. McKinney School of Law

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