Beyond Accuracy: Automated De-Identification of Large Real-World Clinical Text Datasets

• URL: http://arxiv.org/abs/2312.08495v1
• Date: Wed, 13 Dec 2023 20:15:29 GMT
• Title: Beyond Accuracy: Automated De-Identification of Large Real-World Clinical Text Datasets
• Authors: Veysel Kocaman, Hasham Ul Haq, David Talby
• Abstract summary: This paper summarizes lessons learned from building a system used to de-identify over one billion real clinical notes. A fully automated solution requires a very high level of accuracy that does not require manual review.
• Score: 7.6631083158336715
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent research advances achieve human-level accuracy for de-identifying free-text clinical notes on research datasets, but gaps remain in reproducing this in large real-world settings. This paper summarizes lessons learned from building a system used to de-identify over one billion real clinical notes, in a fully automated way, that was independently certified by multiple organizations for production use. A fully automated solution requires a very high level of accuracy that does not require manual review. A hybrid context-based model architecture is described, which outperforms a Named Entity Recogniton (NER) - only model by 10% on the i2b2-2014 benchmark. The proposed system makes 50%, 475%, and 575% fewer errors than the comparable AWS, Azure, and GCP services respectively while also outperforming ChatGPT by 33%. It exceeds 98% coverage of sensitive data across 7 European languages, without a need for fine tuning. A second set of described models enable data obfuscation -- replacing sensitive data with random surrogates -- while retaining name, date, gender, clinical, and format consistency. Both the practical need and the solution architecture that provides for reliable & linked anonymized documents are described.

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