Literature DB >> 27199298

Preserving medical correctness, readability and consistency in de-identified health records.

Kostas Pantazos1, Soren Lauesen1, Soren Lippert1.   

Abstract

A health record database contains structured data fields that identify the patient, such as patient ID, patient name, e-mail and phone number. These data are fairly easy to de-identify, that is, replace with other identifiers. However, these data also occur in fields with doctors' free-text notes written in an abbreviated style that cannot be analyzed grammatically. If we replace a word that looks like a name, but isn't, we degrade readability and medical correctness. If we fail to replace it when we should, we degrade confidentiality. We de-identified an existing Danish electronic health record database, ending up with 323,122 patient health records. We had to invent many methods for de-identifying potential identifiers in the free-text notes. The de-identified health records should be used with caution for statistical purposes because we removed health records that were so special that they couldn't be de-identified. Furthermore, we distorted geography by replacing zip codes with random zip codes.

Entities:  

Keywords:  anonymity; consistency; correctness; de-identification; electronic health records; readability

Mesh:

Year:  2016        PMID: 27199298     DOI: 10.1177/1460458216647760

Source DB:  PubMed          Journal:  Health Informatics J        ISSN: 1460-4582            Impact factor:   2.681


  3 in total

1.  Deep learning detects and visualizes bleeding events in electronic health records.

Authors:  Jannik S Pedersen; Martin S Laursen; Thiusius Rajeeth Savarimuthu; Rasmus Søgaard Hansen; Anne Bryde Alnor; Kristian Voss Bjerre; Ina Mathilde Kjær; Charlotte Gils; Anne-Sofie Faarvang Thorsen; Eline Sandvig Andersen; Cathrine Brødsgaard Nielsen; Lou-Ann Christensen Andersen; Søren Andreas Just; Pernille Just Vinholt
Journal:  Res Pract Thromb Haemost       Date:  2021-05-05

Review 2.  Use and Understanding of Anonymization and De-Identification in the Biomedical Literature: Scoping Review.

Authors:  Raphaël Chevrier; Vasiliki Foufi; Christophe Gaudet-Blavignac; Arnaud Robert; Christian Lovis
Journal:  J Med Internet Res       Date:  2019-05-31       Impact factor: 5.428

3.  Teaching a difficult topic using a problem-based concept resembling a computer game: development and evaluation of an e-learning application for medical molecular genetics.

Authors:  Kamila Prochazkova; Petr Novotny; Miroslava Hancarova; Darina Prchalova; Zdenek Sedlacek
Journal:  BMC Med Educ       Date:  2019-10-24       Impact factor: 2.463

  3 in total

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