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Use of medical data, also known as electronic health records, in researchhelps develop and advance medical science. However, protecting patientconfidentiality and identity while using medical data for analysis is crucial.Medical data can be in the form of tabular structures (i.e. tables), free-formnarratives, and images. This study focuses on medical data in the free formlongitudinal text. De-identification of electronic health records provides theopportunity to use such data for research without it affecting patient privacy,and avoids the need for individual patient consent. In recent years there isincreasing interest in developing an accurate, robust and adaptable automaticde-identification system for electronic health records. This is mainly due tothe dilemma between the availability of an abundance of health data, and theinability to use such data in research due to legal and ethical restrictions.De-identification tracks in competitions such as the 2014 i2b2 UTHealth and the2016 CEGS N-GRID shared tasks have provided a great platform to advance thisarea. The primary reasons for this include the open source nature of thedataset and the fact that raw psychiatric data were used for 2016 competitions.This study focuses on noticeable trend changes in the techniques used in thedevelopment of automatic de-identification for longitudinal clinicalnarratives. More specifically, the shift from using conditional random fields(CRF) based systems only or rules (regular expressions, dictionary orcombinations) based systems only, to hybrid models (combining CRF and rules),and more recently to deep learning based systems. We review the literature andresults that arose from the 2014 and the 2016 competitions and discuss theoutcomes of these systems. We also provide a list of research questions thatemerged from this survey.

A survey of automatic de-identification of longitudinal clinical narratives