{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:05:09Z","timestamp":1772042709578,"version":"3.50.1"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Objective Maintaining patient privacy is a challenge in large-scale observational research. To assist in reducing the risk of identifying study subjects through publicly available data, we introduce a method for obscuring date information for clinical events and patient characteristics.<\/jats:p>\n               <jats:p>Methods The method, which we call Shift and Truncate (SANT), obscures date information to any desired granularity. Shift and Truncate first assigns each patient a random shift value, such that all dates in that patient\u2019s record are shifted by that amount. Data are then truncated from the beginning and end of the data set.<\/jats:p>\n               <jats:p>Results The data set can be proven to not disclose temporal information finer than the chosen granularity. Unlike previous strategies such as a simple shift, it remains robust to frequent \u2013 even daily \u2013 updates and robust to inferring dates at the beginning and end of date-shifted data sets. Time-of-day may be retained or obscured, depending on the goal and anticipated knowledge of the data recipient.<\/jats:p>\n               <jats:p>Conclusions The method can be useful as a scientific approach for reducing re-identification risk under the Privacy Rule of the Health Insurance Portability and Accountability Act and may contribute to qualification for the Safe Harbor implementation.<\/jats:p>","DOI":"10.1093\/jamia\/ocw001","type":"journal-article","created":{"date-parts":[[2016,3,25]],"date-time":"2016-03-25T02:15:54Z","timestamp":1458872154000},"page":"1040-1045","source":"Crossref","is-referenced-by-count":21,"title":["Preserving temporal relations in clinical data while maintaining privacy"],"prefix":"10.1093","volume":"23","author":[{"given":"George","family":"Hripcsak","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032, USA"}]},{"given":"Parsa","family":"Mirhaji","sequence":"additional","affiliation":[{"name":"Montefiore Medical Center\/Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA"}]},{"given":"Alexander FH","family":"Low","sequence":"additional","affiliation":[{"name":"Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY 10065, USA"}]},{"given":"Bradley A","family":"Malin","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN 37203, USA"},{"name":"Department of Electrical Engineering and Computer Science, School of Engineering, Vanderbilt University, Nashville, TN 37203, USA"}]}],"member":"286","published-online":{"date-parts":[[2016,3,24]]},"reference":[{"key":"2020110612383166500_ocw001-B1","first-page":"574","article-title":"Observational Health Data Sciences and Informatics (OHDSI): opportunities for observational 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