TIDE 2.0 aims to remove patient identifiers without breaking links between notes

TIDE 2.0 takes a two-part approach to de-identifying clinical notes: find protected information, then replace it consistently so records remain useful for studying a patient’s care over time. Jose D. Posada, Somalee Datta and Priya Desai describe the engine in an arXiv preprint submitted on October 5, 2026.

The authors’ detection results show a gap between settings. Tested on gold-annotated corpora from two institutions, the default configuration achieved span-level recall of 0.88 in-domain and 0.77 on the second institution’s notes. Precision was 0.88 and 0.87, respectively. Those aggregate scores describe the authors’ tests, not an established level of performance across hospitals.

The system’s design addresses a different problem from detection accuracy: what remains after identifiers are found. As the authors explain, blanking dates removes the intervals needed for longitudinal research, while choosing a fresh random replacement at every occurrence severs connections between notes. TIDE separates an interchangeable recognizer from a keyed anonymizer, with both running on institution-owned hardware.

According to the abstract, the anonymizer generates replacements cryptographically without storing a linkage table. Under one key, a value receives the same replacement wherever it occurs. Dates move by a patient-specific offset that preserves the intervals between them. The authors also claim that a release generated with a new key cannot be linked to earlier releases—a privacy property described in the abstract, not assessed here.

Access differs between the two components. The authors describe the engine as MIT-licensed and open source, while TIDE2-Sentry, their recognizer distilled from a large language model, is available under a gated research-use agreement. The captured abstract supplies neither the agreement’s terms nor the category-level scores needed to see which kinds of identifiers were hardest to detect.