title: Anonymization of longitudinal electronic medical records.
abstract: Electronic medical record (EMR) systems have enabled 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 generate personalized clinical decision support protocols. Emerging policies encourage investigators to disseminate such data in a deidentified form for reuse and collaboration, but organizations are hesitant to do so because they fear such actions will jeopardize patient privacy. In particular, there are concerns that residual demographic 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 paper proposes a novel approach to share patient-specific longitudinal data that offers robust privacy guarantees, while preserving 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.
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37232, USA. acar.tamersoy@vanderbilt.edu
authors.forename: Acar
authors.initials: A
authors.lastname: Tamersoy
authors.forename: Grigorios
authors.initials: G
authors.lastname: Loukides
authors.forename: Mehmet Ercan
authors.initials: ME
authors.lastname: Nergiz
authors.forename: Yucel
authors.initials: Y
authors.lastname: Saygin
authors.forename: Bradley
authors.initials: B
authors.lastname: Malin
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D015331:Cohort Studies; D003219:Confidentiality; D003628:Database Management Systems; D057286:Electronic Health Records; D006801:Humans