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Medical records must often be stripped of patient identifiers, or de-identified, before being shared. De-identification by humans is time-consuming, and existing software is limited in its generality. The open source MITRE Identification Scrubber Toolkit (MIST) provides an environment to support rapid tailoring of automated de-identification to different document types, using automatically learned classifiers to de-identify and protect sensitive information.

MIST was evaluated with four classes of patient records from the Vanderbilt University Medical Center: discharge summaries, laboratory reports, letters, and order summaries. We trained and tested MIST on each class of record separately, as well as on pooled sets of records. We measured precision, recall, F-measure and accuracy at the word level for the detection of patient identifiers as designated by the HIPAA Safe Harbor Rule.