Automatic end-to-end De-identification: Is high accuracy the only metric?
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Abstract (arXiv)
De-identification of electronic health records (EHR) is a vital step towards advancing health informatics research and maximising the use of available data. It is a two-step process where step one is the identification of protected health information (PHI), and step two is replacing such PHI with surrogates. Despite the recent advances in automatic de-identification of EHR, significant obstacles remain if the abundant health data available are to be used to the full potential. Accuracy in de-identification could be considered a necessary, but not sufficient condition for the use of EHR without individual patient consent. We present here a comprehensive review of the progress to date, both the impressive successes in achieving high accuracy and the significant risks and challenges that remain. To best of our knowledge, this is the first paper to present a complete picture of end-to-end automatic de-identification. We review 18 recently published automatic de-identification systems -designed to de-identify EHR in the form of free text- to show the advancements made in improving the overall accuracy of the system, and in identifying individual PHI. We argue that despite the improvements in accuracy there remain challenges in surrogate generation and replacements of identified PHIs, and the risks posed to patient protection and privacy.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Jan 2019.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1.
4 papers.
- 2022A Comparative Evaluation Of Transformer Models For De-Identification Of Clinical Text Datacited by 5
- 2023Beyond Accuracy: Automated De-Identification of Large Real-World Clinical Text Datasetscited by 3
- 2025Beyond De-Identification: A Structured Approach for Defining and Detecting Indirect Identifiers in Medical Textscited by 2
- 2024AspirinSum: an Aspect-based utility-preserved de-identification Summarization frameworkcited by 0
- 2025Beyond De-Identification: A Structured Approach for Defining and Detecting Indirect Identifiers in Medical Texts18 Feb 2025
- 2024AspirinSum: an Aspect-based utility-preserved de-identification Summarization framework20 Jun 2024
- 2023Beyond Accuracy: Automated De-Identification of Large Real-World Clinical Text Datasets13 Dec 2023
- 2022A Comparative Evaluation Of Transformer Models For De-Identification Of Clinical Text Data25 Mar 2022