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A Nuanced Understanding of Privacy

A case pending before the U.S. Supreme Court, Sorrell v. IMS Health Inc., has serious implications for how privacy protections are interpreted.  But understanding the various risks posed in this case requires some careful unpacking of the ways in which "privacy" is—and is not—at issue here.  CDT's Health Privacy Project team has taken a look those risks and published an in-depth memo about its findings.

In this memo CDT focuses on two aspects raised by Sorrell v IMS Health Inc.: First, an explanation of why it is important to recognize the valid distinctions between personally identifiable data and "de-identified" data.  The paper explains that privacy could actually be harmed if the Court were to accept the claims, made in some briefs in the case, that there is no difference between identified and de-identified data.  

The second aspect of the case the paper examines is the claim that doctors have a "privacy" right in their drug prescribing practices.  CDT disagrees and explains here that, while the patient-doctor relationship is based on confidentiality and the trust it generates, it is not useful – and would undermine other health care goals – to speak of doctors as having a "privacy" right in their drug prescribing practices.

The paper concludes by saying:  

So in many ways, Sorrell v. IMS Health is not about privacy in the way that defenders of the Vermont law claim.  Yet a broad ruling by the court on de-identified data could have a negative impact on patient privacy.  And a broad statement by the Court on doctor 'privacy' could derail other very timely initiatives. This is not the case, nor is the Supreme Court the institution, to make policy on either set of issues; the parties have offered other viable rationale for the Court to use to decide this case. There needs to be a policy conversation about the viability of the current de-identification standard, but this case needs to preserve the concept that there is a meaningful distinction between identified and de-identified data. It is up to other processes to ensure a continually robust de-identification standard and strict accountability for re-identification.