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Lessons Learned for Identifying and Annotating Permissions in Clinical Consent Forms.

Elizabeth E Umberfield1,2, Yun Jiang3, Susan H Fenton4

  • 1Health Policy & Management, Indiana University Richard M Fairbanks School of Public Health, Indianapolis, Indiana, United States.

Applied Clinical Informatics
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Summary
This summary is machine-generated.

Annotating clinical consent forms for permissions is complex due to varied language. This study developed an annotation scheme and found moderate to substantial interannotator agreement, highlighting challenges for automated extraction tools.

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Area of Science:

  • Health Informatics
  • Natural Language Processing
  • Clinical Data Management

Background:

  • Machine-interpretable consent permissions are lacking, hindering scalable tools for managing data access across ecosystems.
  • Current clinical consent forms present challenges for automated analysis due to their complex language.

Purpose of the Study:

  • To detail the process, outcomes, and lessons learned from annotating permission statements within clinical consent forms.
  • To establish a foundation for developing automated tools for extracting consent permissions.

Main Methods:

  • Retrospective analysis of 134 clinical consent forms, totaling 6,399 sentences.
  • Development of an annotation scheme using the Model-Annotate-Model-Annotate (MAMA) cycle.
  • Evaluation of interannotator agreement (IAA) using observed agreement (A_o), weighted kappa (κw), and Krippendorff's alpha (α).

Main Results:

  • A final dataset of 6,399 sentences was annotated.
  • Complete agreement was reached for 5,871 sentences (91.8%).
  • Interannotator agreement metrics (A_o = 0.944, Krippendorff's α = 0.599) indicated moderate to substantial agreement, despite lexical variability and ambiguity causing disagreements.

Conclusions:

  • Identifying permission sentences in clinical consent forms is complex.
  • Lexical variability and sentence ambiguity are key challenges in automated permission extraction.
  • Lessons learned provide a basis for developing tools to automate the extraction of consent permissions from clinical documents.