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The psychological and psychiatric communities are generating data on an ever-increasing scale. To ensure that society reaps the greatest utility in research and clinical care from such rich resources, there is significant interest in wide-scale, open data sharing to foster scientific endeavors. However, it is imperative that such open-science initiatives ensure that data-privacy concerns are adequately addressed. In this article, we focus on these issues in clinical research. We review the privacy risks and then discuss how they can be mitigated through appropriate governance mechanisms that are both social (e.g., the application of data-use agreements) and technological (e.g., de-identification of structured data and unstructured narratives). We also discuss the benefits and drawbacks of these mechanisms, particularly as regards data fidelity. Our focus is on de-identification methods that meet regulatory requirements, such as the Privacy Rule of the Health Insurance Portability and Accountability Act of 1996. To illustrate their potential, we show how the principles we discuss have been applied in a large-scale clinical database and distributed research networks. We close this article with a discussion of challenges in supporting data privacy as open-science initiatives grow in their scale and complexity.
Link to resource: https://doi.org/10.1177/2515245917749652
Type of resources: Primary Source, Reading, Paper
Education level(s): College / Upper Division (Undergraduates)
Primary user(s): Student
Subject area(s): Applied Science, Social Science