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Lessons Learned for Identifying and Annotating Permissions in Clinical Consent FormsFunding E. Umberfield was supported in part by the Robert Wood Johnson Foundation Future of Nursing Scholar's Program predoctoral training program. She is presently funded as a Postdoctoral Research Fellow in Public and Population Health Informatics at Fairbanks School of Public Health and Regenstrief Institute, supported by the National Library of Medicine of the National Institutes of Health under award number 5T15LM012502-04. The study was further supported by the National Human Genome Research Institute of the National Institutes of Health under award number 5U01HG009454-03, the Rackham Graduate Student Research Grant, and the University of Michigan Institute for Data Science. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the funding entities.
Background The lack of machine-interpretable representations of consent permissions precludes development of tools that act upon permissions across information ecosystems, at scale.
Objectives To report the process, results, and lessons learned while annotating permissions in clinical consent forms.
Methods We conducted a retrospective analysis of clinical consent forms. We developed an annotation scheme following the MAMA (Model-Annotate-Model-Annotate) cycle and evaluated interannotator agreement (IAA) using observed agreement (A o), weighted kappa (κw ), and Krippendorff's α.
Results The final dataset included 6,399 sentences from 134 clinical consent forms. Complete agreement was achieved for 5,871 sentences, including 211 positively identified and 5,660 negatively identified as permission-sentences across all three annotators (A o = 0.944, Krippendorff's α = 0.599). These values reflect moderate to substantial IAA. Although permission-sentences contain a set of common words and structure, disagreements between annotators are largely explained by lexical variability and ambiguity in sentence meaning.
Conclusion Our findings point to the complexity of identifying permission-sentences within the clinical consent forms. We present our results in light of lessons learned, which may serve as a launching point for developing tools for automated permission extraction.
Protection of Human and Animal Subjects
Institutional Review Board review was not required because human subjects were not involved. Only blank consent forms were collected and analyzed.
Received: 29 December 2020
Accepted: 31 March 2021
23 June 2021 (online)
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