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J Am Med Inform Assoc 15:601-610 doi:10.1197/jamia.M2702
  • The Practice of Informatics
  • Application of Information Technology

A Software Tool for Removing Patient Identifying Information from Clinical Documents

  1. F Jeff Friedlin,
  2. Clement J McDonald1
  1. Regenstrief Institute, Indianapolis, IN
  1. Correspondence: Jeff Friedlin, DO, Regenstrief Institute, Inc., Medical Informatics, Health Information and Translational Sciences (HITS) Building, 410 West 10th Street, Suite 2000, Indianapolis, IN 46202; e-mail: <jfriedlin{at}regenstrief.org>
  • Received 20 December 2007
  • Accepted 30 May 2008

Abstract

We created a software tool that accurately removes all patient identifying information from various kinds of clinical data documents, including laboratory and narrative reports. We created the Medical De-identification System (MeDS), a software tool that de-identifies clinical documents, and performed 2 evaluations. Our first evaluation used 2,400 Health Level Seven (HL7) messages from 10 different HL7 message producers. After modifying the software based on the results of this first evaluation, we performed a second evaluation using 7,190 pathology report HL7 messages. We compared the results of MeDS de-identification process to a gold standard of human review to find identifying strings. For both evaluations, we calculated the number of successful scrubs, missed identifiers, and over-scrubs committed by MeDS and evaluated the readability and interpretability of the scrubbed messages. We categorized all missed identifiers into 3 groups: (1) complete HIPAA-specified identifiers, (2) HIPAA-specified identifier fragments, (3) non-HIPAA–specified identifiers (such as provider names and addresses). In the results of the first-pass evaluation, MeDS scrubbed 11,273 (99.06%) of the 11,380 HIPAA-specified identifiers and 38,095 (98.26%) of the 38,768 non-HIPAA–specified identifiers. In our second evaluation (status postmodification to the software), MeDS scrubbed 79,993 (99.47%) of the 80,418 HIPAA-specified identifiers and 12,689 (96.93%) of the 13,091 non-HIPAA–specified identifiers. Approximately 95% of scrubbed messages were both readable and interpretable. We conclude that MeDS successfully de-identified a wide range of medical documents from numerous sources and creates scrubbed reports that retain their interpretability, thereby maintaining their usefulness for research.

Footnotes

  • Dr. McDonald is currently at the Lister Hill Center, Bethesda, MD.

  • All patient names cited in this paper, although analogous in format to the original names, are fictitious.

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