  De-identification of clinical records is an extremely important process which
enables the use of the wealth of information present in them. There are a lot
of techniques available for this but none of the method implementation has
evaluated the scalability, which is an important benchmark. We evaluated
numerous deep learning techniques such as BiLSTM-CNN, IDCNN, CRF, BiLSTM-CRF,
SpaCy, etc. on both the performance and efficiency. We propose that the SpaCy
model implementation for scrubbing sensitive PHI data from medical records is
both well performing and extremely efficient compared to other published
models.
