Performance of Automatic De-identification Across Different Note Types
This work addresses privacy concerns in clinical research by assessing de-identification tools, but it is incremental as it focuses on performance evaluation without introducing new methods.
The study evaluated the performance of the NeuroNER de-identification system on diverse clinical notes, comparing models trained on external versus same-institution data, with results presented at the PHI and note type levels.
Free-text clinical notes detail all aspects of patient care and have great potential to facilitate quality improvement and assurance initiatives as well as advance clinical research. However, concerns about patient privacy and confidentiality limit the use of clinical notes for research. As a result, the information documented in these notes remains unavailable for most researchers. De-identification (de-id), i.e., locating and removing personally identifying protected health information (PHI), is one way of improving access to clinical narratives. However, there are limited off-the-shelf de-identification systems able to consistently detect PHI across different data sources and medical specialties. In this abstract, we present the performance of a state-of-the art de-id system called NeuroNER1 on a diverse set of notes from University of Washington (UW) when the models are trained on data from an external institution (Partners Healthcare) vs. from the same institution (UW). We present results at the level of PHI and note types.