ArticleJournal of the American Medical Informatics Association : JAMIA
Hiding in plain sight: use of realistic surrogates to reduce exposure of protected health information in clinical text.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Open tools for quantitative anonymization of tabular phenotype data: literature review.Briefings in bioinformatics · 2022Pooled it
- The Potential of Research Drawing on Clinical Free Text to Bring Benefits to Patients in the United Kingdom: A Systematic Review of the Literature.Frontiers in digital health · 2021Pooled it
- A Markov Chain Replacement Strategy for Surrogate Identifiers: Minimizing Re-Identification Risk While Preserving Text Reuse.Electronics · 2025Article
- Operationalizing machine-assisted translation in healthcare.NPJ digital medicine · 2025Review
- A textual dataset of de-identified health records in Spanish and Catalan for medical entity recognition and anonymization.Scientific data · 2025Article
- The DRAGON benchmark for clinical NLP.NPJ digital medicine · 2025Article
- Robust privacy amidst innovation with large language models through a critical assessment of the risks.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- DIRI: Adversarial Patient Reidentification with Large Language Models for Evaluating Clinical Text Anonymization.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- What is the patient re-identification risk from using de-identified clinical free text data for health research?AI and ethics · 2025Review
- Automated redaction of names in adverse event reports using transformer-based neural networks.BMC medical informatics and decision making · 2024Article
- An Extensible Evaluation Framework Applied to Clinical Text Deidentification Natural Language Processing Tools: Multisystem and Multicorpus Study.Journal of medical Internet research · 2024Article
- MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2024Article
- Examining the Generalizability of Pretrained De-identification Transformer Models on Narrative Nursing Notes.Applied clinical informatics · 2024Article
- Pragmatic De-Identification of Cross-Domain Unstructured Documents: A Utility-Preserving Approach with Relation Extraction Filtering.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- The Stanford Medicine data science ecosystem for clinical and translational research.JAMIA open · 2023Article
- Man vs the machine in the struggle for effective text anonymisation in the age of large language models.Scientific reports · 2023Article
- Impact of Different Mammography Systems on Artificial Intelligence Performance in Breast Cancer Screening.Radiology. Artificial intelligence · 2023Article
- Automated deidentification of radiology reports combining transformer and "hide in plain sight" rule-based methods.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Nonspecific deidentification of date-like text in deidentified clinical notes enables reidentification of dates.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Biomedical Ontologies to Guide AI Development in Radiology.Journal of digital imaging · 2021Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectiveSecondary use of clinical text is impeded by a lack of highly effective, low-cost de-identification methods. Both, manual and automated methods for removing protected health information, are known to leave behind residual identifiers. The authors propose a novel approach for addressing the residual identifier problem based on the theory of Hiding In Plain Sight (HIPS). MATERIALS AND
methodsHIPS relies on obfuscation to conceal residual identifiers. According to this theory, replacing the detected identifiers with realistic but synthetic surrogates should collectively render the few 'leaked' identifiers difficult to distinguish from the synthetic surrogates. The authors conducted a pilot study to test this theory on clinical narrative, de-identified by an automated system. Test corpora included 31 oncology and 50 family practice progress notes read by two trained chart abstractors and an informaticist.
resultsExperimental results suggest approximately 90% of residual identifiers can be effectively concealed by the HIPS approach in text containing average and high densities of personal identifying information. DISCUSSION: This pilot test suggests HIPS is feasible, but requires further evaluation. The results need to be replicated on larger corpora of diverse origin under a range of detection scenarios. Error analyses also suggest areas where surrogate generation techniques can be refined to improve efficacy.
conclusionsIf these results generalize to existing high-performing de-identification systems with recall rates of 94-98%, HIPS could increase the effective de-identification rates of these systems to levels above 99% without further advancements in system recall. Additional and more rigorous assessment of the HIPS approach is warranted.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.