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Recent research advances achieve human-level accuracy for de-identifyingfree-text clinical notes on research datasets, but gaps remain in reproducingthis in large real-world settings. This paper summarizes lessons learned frombuilding a system used to de-identify over one billion real clinical notes, ina fully automated way, that was independently certified by multipleorganizations for production use. A fully automated solution requires a veryhigh level of accuracy that does not require manual review. A hybridcontext-based model architecture is described, which outperforms a Named EntityRecogniton (NER) - only model by 10% on the i2b2-2014 benchmark. The proposedsystem makes 50%, 475%, and 575% fewer errors than the comparable AWS, Azure,and GCP services respectively while also outperforming ChatGPT by 33%. Itexceeds 98% coverage of sensitive data across 7 European languages, without aneed 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 thesolution architecture that provides for reliable & linked anonymized documentsare described.

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