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How much does automatic text de-identification impact clinical problems, tests, and treatments?

Stéphane M Meystre1, Oscar Ferrández, Brett R South

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Clinical text de-identification may obscure medical information. This study analyzed overlaps between NLP challenge concepts and protected health information (PHI) annotations, finding minimal data loss from de-identification applications.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing

Background:

  • Clinical text de-identification is crucial for patient privacy.
  • De-identification processes risk obscuring valuable clinical information, such as medical problems and treatments.

Purpose of the Study:

  • To quantify the overlap between clinical concept annotations and protected health information (PHI) annotations.
  • To assess the potential information loss caused by automated de-identification in clinical text.

Main Methods:

  • Utilized annotations from the 2010 i2b2 Natural Language Processing (NLP) challenge.
  • Compared i2b2 concept annotations with PHI annotations generated by a state-of-the-art de-identification application.
  • Analyzed exact and partial overlaps between the two sets of annotations.

Main Results:

  • A small percentage of annotations showed overlap between clinical concepts and PHI.
  • Exactly 0.81% of annotations exhibited exact overlap.
  • Partially overlapping annotations accounted for 1.78% of the total.

Conclusions:

  • The study indicates a low but present risk of clinical information loss due to de-identification.
  • Findings suggest that while de-identification is necessary, its impact on clinical data utility needs careful consideration.
  • Further research may refine de-identification techniques to minimize the loss of critical clinical insights.