Objective The objective of this study is to address the critical issue of deidentification of clinical reports to allow access to data for research purposes, while ensuring patient privacy. The study highlights the difficulties faced in sharing tools and resources in this domain and presents the experience of the Greater Paris University Hospitals (AP-HP for Assistance Publique-Hôpitaux de Paris) in implementing a systematic pseudonymization of text documents from its Clinical Data Warehouse.

Methods We annotated a corpus of clinical documents according to 12 types of identifying entities and built a hybrid system, merging the results of a deep learning model as well as manual rules.

Results and Discussion Our results show an overall performance of 0.99 of F1-score. We discuss implementation choices and present experiments to better understand the effort involved in such a task, including dataset size, document types, language models, or rule addition. We share guidelines and code under a 3-Clause BSD license.

All authors designed the study. X.T. drafted the manuscript. All authors interpreted data and made critical intellectual revisions of the manuscript. X.T. did the literature review. P.W. checked all the annotations. P.W., A.C. and B.D. developed the deidentification algorithms. P.W. conducted the experiments and computed the statistical results. X.T., A.M., M.H. and R.B. supervised the project.

Access to the Clinical Data Warehouse's raw data can be granted following the process described on its Web site: eds.aphp.fr. Prior validation of the access by the local institutional review board is required. In the case of non-AP-HP researchers, the signature of a collaboration contract is moreover mandatory.

Received: 24 March 2023

Accepted: 28 November 2023

Article published online:
05 March 2024

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