  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.
