De-identifying an EHR Database: Anonymity, Correctness and Readability of the Medical Record

Søren Lauesen, Kostas Pantazos, Søren Lippert

Research output: Journal Article or Conference Article in JournalConference articleResearchpeer-review


Abstract. Electronic health records (EHR) contain a large amount of structured data and free text. Exploring and sharing clinical data can improve healthcare and facilitate the development of medical software. However, revealing confidential information is against ethical principles and laws. We de-identified a Danish EHR database with 437,164 patients. The goal was to generate a version with real medical records, but related to artificial persons. We developed a de-identification algorithm that uses lists of named entities, simple language analysis, and special rules. Our algorithm consists of 3 steps: collect lists of identifiers from the database and external resources, define a replacement for each identifier, and replace identifiers in structured data and free text. Some patient records could not be safely de-identified, so the de-identified database has 323,122 patient records with an acceptable degree of anonymity, readability and correctness (F-measure of 95%). The algorithm has to be adjusted for each culture, language and database.
Original languageEnglish
Book seriesStudies in Health Technology and Informatics
Pages (from-to)862-866
Number of pages5
Publication statusPublished - 2011


  • Electronic health records
  • De-identification
  • Artificial persons
  • Named entity recognition
  • Healthcare data privacy


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