"Real-World" De-Identification of High-Dimensional Transactional Health Datasets

This paper presents a framework for addressing data access challenges associated with secondary use of high-dimensional transactional datasets that have been extracted from electronic health/medical records (EHRs). These datasets are subject to the data de-identification "curse of dimensionality" [1] which manifests as substantial challenges to preserving analytical integrity of data contents when high-dimensional datasets must be de-identified and deemed free of Personal Information (PI) prior to disclosure. A large array of methods can achieve this objective - for low dimensional datasets. However, these methods have not been scaled up to the types of high-dimensional data that must be sourced from the transactional EHR if the objective is specifically to generate products that can inform point-of-care clinical decision-making. The Applied Clinical Research Unit (ACRU) in Island Health is implementing a process that addresses key privacy challenges inherent in disclosures of high-dimensional transactional health data. This paper presents a schematic and abbreviated rendering of key principles and processes on which the ACRU approach is based.