De-identification of Address, Date, and Alphanumeric Identifiers in Narrative Clinical Reports.

Introduction: The Privacy Rule of Health Insurance Portability and Accountability Act requires that clinical documents be stripped of personally identifying information before they can be released to researchers and others. We have been developing a software application, NLM Scrubber, to de-identify narrative clinical reports.

Methods: We compared NLM Scrubber with MIT's and MITRE's de-identification systems on 3,093 clinical reports about 1,636 patients.

Piloting a deceased subject integrated data repository and protecting privacy of relatives.

Use of deceased subject Electronic Health Records can be an important piloting platform for informatics or biomedical research. Existing legal framework allows such research under less strict de-identification criteria; however, privacy of non-decedent must be protected. We report on creation of the decease subject Integrated Data Repository (dsIDR) at National Institutes of Health, Clinical Center and a pilot methodology to remove secondary protected health information or identifiable information (secondary PxI; information about persons other than the primary patient).

The Challenges of Creating a Gold Standard for De-identification Research.

We created a Gold Standard corpus comprised over 20,000 records of annotated narrative clinical reports for use in the training and evaluation of NLM Scrubber, a de-identification software system for medical records. Our experience with designing the corpus demonstrated the conceptual complexity of the task.

The pattern of name tokens in narrative clinical text and a comparison of five systems for redacting them.

Objective: To understand the factors that influence success in scrubbing personal names from narrative text.

Materials And Methods: We developed a scrubber, the NLM Name Scrubber (NLM-NS), to redact personal names from narrative clinical reports, hand tagged words in a set of gold standard narrative reports as personal names or not, and measured the scrubbing success of NLM-NS and that of four other scrubbing/name recognition tools (MIST, MITdeid, LingPipe, and ANNIE/GATE) against the gold standard reports. We ran three comparisons which used increasingly larger name lists.