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The OpenDeID corpus for patient de-identification.

Jitendra Jonnagaddala1, Aipeng Chen2, Sean Batongbacal2

  • 1School of Population Health, UNSW Sydney, Sydney, Australia. z3339253@unsw.edu.au.

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|October 8, 2021
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Summary

The OpenDeID corpus aids automatic redaction of protected health information from electronic health records. Pre-annotations reduce time but impact quality compared to serial methods.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Data Privacy

Background:

  • Electronic health records (EHRs) contain sensitive protected health information (PHI).
  • Research requires redaction of PHI from unstructured EHRs to ensure patient privacy.
  • Automatic methods for PHI redaction are needed to streamline this process.

Purpose of the Study:

  • To introduce the OpenDeID corpus for developing automatic PHI redaction tools.
  • To evaluate different annotation strategies for corpus creation.
  • To assess the quality and efficiency of annotation methods.

Main Methods:

  • Collected 4,548 unstructured surgical pathology reports from Australian hospitals.
  • Developed the OpenDeID corpus using serial, parallel, and pre-annotation methods with two annotators.
  • Evaluated annotation quality and inter-annotator agreement (0.9464) and deviation (0.9726).

Main Results:

  • The OpenDeID corpus contains 2,100 pathology reports with an average of 7.35 PHI entities per report.
  • Pre-annotation significantly reduced annotation time but yielded lower quality compared to serial annotation.
  • Generated realistic surrogates for corpus distribution.

Conclusions:

  • The OpenDeID corpus is a valuable resource for advancing automatic PHI redaction in EHRs.
  • Annotation strategy impacts both the quality and efficiency of corpus development.
  • The corpus, with surrogates, is suitable for broader research dissemination.