Presented By: Department of Learning Health Sciences
Learning to Love De-identification in Biomedical Research
(RSVP Requested)
Brad Malin, PhD is Vice Chair for Research in the Dept. of Biomedical Informatics and Director of the Health Information Privacy Laboratory at Vanderbilt University.
Over the past several decades, numerous approaches have been developed to remove and obscure patient identifying information in the context of biomedical research. Generally, this approach to privacy protection, which is often called “de-identification” has been codified in regulations and laws, including the Common Rule and the Privacy Rule of the Health Insurance Portability and Accountability Act of 1996. There is a now a great opportunity to erect learning health systems on top of de-identified medical record systems; however, there is trepidation because the past decade has also witnessed a number of investigations into how to “re-identify” such information to the patients from whom it was derived. These demonstration attacks have called the strength of such privacy protections into question. The goal of this talk is to review why re-identification happens both from a computational and policy perspective, the extent to which such violations can be averted using risk analysis strategies, and how we can leverage de-identified patient data en masse to support large scale association studies. In this talk, Brad Malin, PhD, will draw upon his experience in building one of the world’s largest de-identified electronic medical record systems and the experiences of the NIH-sponsored Electronic Medical Records and Genomics (eMERGE) Consortium.
An RSVP is requested for this event:
http://dlhs-umi.ch/bradmalin-may11-rsvp
Over the past several decades, numerous approaches have been developed to remove and obscure patient identifying information in the context of biomedical research. Generally, this approach to privacy protection, which is often called “de-identification” has been codified in regulations and laws, including the Common Rule and the Privacy Rule of the Health Insurance Portability and Accountability Act of 1996. There is a now a great opportunity to erect learning health systems on top of de-identified medical record systems; however, there is trepidation because the past decade has also witnessed a number of investigations into how to “re-identify” such information to the patients from whom it was derived. These demonstration attacks have called the strength of such privacy protections into question. The goal of this talk is to review why re-identification happens both from a computational and policy perspective, the extent to which such violations can be averted using risk analysis strategies, and how we can leverage de-identified patient data en masse to support large scale association studies. In this talk, Brad Malin, PhD, will draw upon his experience in building one of the world’s largest de-identified electronic medical record systems and the experiences of the NIH-sponsored Electronic Medical Records and Genomics (eMERGE) Consortium.
An RSVP is requested for this event:
http://dlhs-umi.ch/bradmalin-may11-rsvp
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