Lessons Learned for Identifying and Annotating Permissions in Clinical Consent Forms - Umberfield, Elizabeth E.; Jiang, Yun; Fenton, Susan H.; Stansbury, Cooper; Ford, Kathleen; Crist, Kaycee; Kardia, Sharon L. R.; Thomer, Andrea K.; Harris, Marcelline R. (Applied Clinical Informatics, 2021)
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 (Ao), 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 (Ao = 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.
Keywords: informed consent; consent forms; natural language processing