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.
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’s record are shifted by that amount. Data are then truncated from the beginning and end of the data set.
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 – even daily – 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.
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.