title: BoB, a best-of-breed automated text de-identification system for VHA clinical documents.
abstract: OBJECTIVE
  De-identification allows faster and more collaborative clinical research while protecting patient confidentiality. Clinical narrative de-identification is a tedious process that can be alleviated by automated natural language processing methods. The goal of this research is the development of an automated text de-identification system for Veterans Health Administration (VHA) clinical documents.
  
  
  METHODS
  We devised a novel stepwise hybrid approach designed to improve the current strategies used for text de-identification. The proposed system is based on a previous study on the best de-identification methods for VHA documents. This best-of-breed automated clinical text de-identification system (aka BoB) tackles the problem as two separate tasks: (1) maximize patient confidentiality by redacting as much protected health information (PHI) as possible; and (2) leave de-identified documents in a usable state preserving as much clinical information as possible.
  
  
  RESULTS
  We evaluated BoB with a manually annotated corpus of a variety of VHA clinical notes, as well as with the 2006 i2b2 de-identification challenge corpus. We present evaluations at the instance- and token-level, with detailed results for BoB's main components. Moreover, an existing text de-identification system was also included in our evaluation.
  
  
  CONCLUSIONS
  BoB's design efficiently takes advantage of the methods implemented in its pipeline, resulting in high sensitivity values (especially for sensitive PHI categories) and a limited number of false positives.
  
  
  CONCLUSIONS
  Our system successfully addressed VHA clinical document de-identification, and its hybrid stepwise design demonstrates robustness and efficiency, prioritizing patient confidentiality while leaving most clinical information intact.
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112, USA. oscar.ferrandez@utah.edu
authors.forename: Oscar
authors.initials: O
authors.lastname: Ferrández
authors.forename: Brett R
authors.initials: BR
authors.lastname: South
authors.forename: Shuying
authors.initials: S
authors.lastname: Shen
authors.forename: F Jeffrey
authors.initials: FJ
authors.lastname: Friedlin
authors.forename: Matthew H
authors.initials: MH
authors.lastname: Samore
authors.forename: Stéphane M
authors.initials: SM
authors.lastname: Meystre
mesh_terms: D001185:Artificial Intelligence; D003219:Confidentiality; D057225:Data Mining; D057286:Electronic Health Records; D006801:Humans; D033181:Information Dissemination; D009323:Natural Language Processing; D013673:Technology Assessment, Biomedical; D014481:United States; D014493:United States Department of Veterans Affairs