title: The pattern of name tokens in narrative clinical text and a comparison of five systems for redacting them.
abstract: OBJECTIVE
  To understand the factors that influence success in scrubbing personal names from narrative text.
  
  
  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.
  
  
  RESULTS
  The test reports contained more than 1 million words, of which 2388 were patient and 20,160 were provider name tokens. NLM-NS failed to scrub only 2 of the 2388 instances of patient name tokens. Its sensitivity was 0.999 on both patient and provider name tokens and missed fewer instances of patient name tokens in all comparisons with other scrubbers. MIST produced the best all token specificity and F-measure for name instances in our most relevant study (study 2), with values of 0.997 and 0.938, respectively. In that same comparison, NLM-NS was second best, with values of 0.986 and 0.748, respectively, and MITdeid was a close third, with values of 0.985 and 0.796 respectively. With the addition of the Clinical Center name list to their native name lists, Ling Pipe, MITdeid, MIST, and ANNIE/GATE all improved substantially. MITdeid and Ling Pipe gained the most--reaching patient name sensitivity of 0.995 (F-measure=0.705) and 0.989 (F-measure=0.386), respectively.
  
  
  CONCLUSIONS
  The privacy risk due to two name tokens missed by NLM-NS was statistically negligible, since neither individual could be distinguished among more than 150,000 people listed in the US Social Security Registry.
  
  
  CONCLUSIONS
  The nature and size of name lists have substantial influences on scrubbing success. The use of very large name lists with frequency statistics accounts for much of NLM-NS scrubbing success.
authors.affiliation: National Institutes of Health, National Library of Medicine, Lister Hill National Center for Biomedical Communications, Bethesda, Maryland, USA.
authors.forename: Mehmet
authors.initials: M
authors.lastname: Kayaalp
authors.forename: Allen C
authors.initials: AC
authors.lastname: Browne
authors.forename: Fiona M
authors.initials: FM
authors.lastname: Callaghan
authors.forename: Zeyno A
authors.initials: ZA
authors.lastname: Dodd
authors.forename: Guy
authors.initials: G
authors.lastname: Divita
authors.forename: Selcuk
authors.initials: S
authors.lastname: Ozturk
authors.forename: Clement J
authors.initials: CJ
authors.lastname: McDonald
mesh_terms: D003219:Confidentiality; D057286:Electronic Health Records; D006801:Humans; D009275:Names; D009317:National Library of Medicine (U.S.); D009323:Natural Language Processing; D014481:United States
keywords: Chart Research; De-Identification; Electronic Medical Records; PHI
references.citation: Am J Hosp Pharm. 1986 Dec;43(12):2991-7
references.pmid: 3544826
references.citation: Nat Rev Genet. 2011 Jun;12(6):417-28
references.pmid: 21587298
references.citation: BMC Med Inform Decis Mak. 2006 Mar 06;6:12
references.pmid: 16515714
references.citation: BMC Med Res Methodol. 2010 Aug 02;10:70
references.pmid: 20678228
references.citation: J Am Med Inform Assoc. 2013 Jan 1;20(1):84-94
references.pmid: 22859645
references.citation: J Am Med Inform Assoc. 2007 Sep-Oct;14(5):550-63
references.pmid: 17600094
references.citation: J Am Med Inform Assoc. 2007 Sep-Oct;14(5):564-73
references.pmid: 17600096
references.citation: J Am Med Inform Assoc. 2008 Sep-Oct;15(5):601-10
references.pmid: 18579831
references.citation: Ann Am Acad Pol Soc Sci. 1988 Jan;(495):117-26
references.pmid: 10318632
references.citation: BMC Med Inform Decis Mak. 2008 Jul 24;8:32
references.pmid: 18652655