Abstract

Abstract—Electronic medical record (EMR) systems have en-abled healthcare providers to collect detailed patient information from the primary care domain. At the same time, longitudinal data from EMRs are increasingly combined with biorepositories to gen-erate personalized clinical decision support protocols. Emerging policies encourage investigators to disseminate such data in a dei-dentified form for reuse and collaboration, but organizations are hesitant to do so because they fear such actions will jeopardize pa-tient privacy. In particular, there are concerns that residual demo-graphic and clinical features could be exploited for reidentification purposes. Various approaches have been developed to anonymize clinical data, but they neglect temporal information and are, thus, insufficient for emerging biomedical research paradigms. This pa-per proposes a novel approach to share patient-specific longitu-dinal data that offers robust privacy guarantees, while preserv-ing data utility for many biomedical investigations. Our approach aggregates temporal and diagnostic information using heuristics inspired from sequence alignment and clustering methods. We demonstrate that the proposed approach can generate anonymized data that permit effective biomedical analysis using several patient cohorts derived from the EMR system of the Vanderbilt University Medical Center. Index Terms—Anonymization, data privacy, electronic medical records (EMRs), longitudinal data. I.