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

arXiv: https://arxiv.org/abs/2312.08495 · HTML: https://mlsanity.com/paper/2312.08495/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 13 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [Marian: Fast Neural Machine Translation in C++](https://mlsanity.com/paper/1804.00344/) (2018)
- [Biomedical Named Entity Recognition at Scale](https://mlsanity.com/paper/2011.06315/) (2020)
- [Spark NLP: Natural Language Understanding at Scale](https://mlsanity.com/paper/2101.10848/) (2021)
- [A Deep Learning Architecture for De-identification of Patient Notes: Implementation and Evaluation](https://mlsanity.com/paper/1810.01570/) (2018)
- [Automatic end-to-end De-identification: Is high accuracy the only metric?](https://mlsanity.com/paper/1901.10583/) (2019)

## Built on by (most cited first, 3 of 3)

- [LLMs-in-the-Loop Part 2: Expert Small AI Models for Anonymization and De-identification of PHI Across Multiple Languages](https://mlsanity.com/paper/2412.10918/) (2024)
- [Improving the Performance of Radiology Report De-identification with Large-Scale Training and Benchmarking Against Cloud Vendor Methods](https://mlsanity.com/paper/2511.04079/) (2025)
- [SHIELD: A Diverse Clinical Note Dataset and Distilled Small Language Models for Enterprise-Scale De-identification](https://mlsanity.com/paper/2605.03301/) (2026)

## Abstract (arXiv)

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.