title: Using Nonexperts for Annotating Pharmacokinetic Drug-Drug Interaction Mentions in Product Labeling: A Feasibility Study.
abstract: BACKGROUND
  Because vital details of potential pharmacokinetic drug-drug interactions are often described in free-text structured product labels, manual curation is a necessary but expensive step in the development of electronic drug-drug interaction information resources. The use of nonexperts to annotate potential drug-drug interaction (PDDI) mentions in drug product label annotation may be a means of lessening the burden of manual curation.
  
  
  OBJECTIVE
  Our goal was to explore the practicality of using nonexpert participants to annotate drug-drug interaction descriptions from structured product labels. By presenting annotation tasks to both pharmacy experts and relatively naïve participants, we hoped to demonstrate the feasibility of using nonexpert annotators for drug-drug information annotation. We were also interested in exploring whether and to what extent natural language processing (NLP) preannotation helped improve task completion time, accuracy, and subjective satisfaction.
  
  
  METHODS
  Two experts and 4 nonexperts were asked to annotate 208 structured product label sections under 4 conditions completed sequentially: (1) no NLP assistance, (2) preannotation of drug mentions, (3) preannotation of drug mentions and PDDIs, and (4) a repeat of the no-annotation condition. Results were evaluated within the 2 groups and relative to an existing gold standard. Participants were asked to provide reports on the time required to complete tasks and their perceptions of task difficulty.
  
  
  RESULTS
  One of the experts and 3 of the nonexperts completed all tasks. Annotation results from the nonexpert group were relatively strong in every scenario and better than the performance of the NLP pipeline. The expert and 2 of the nonexperts were able to complete most tasks in less than 3 hours. Usability perceptions were generally positive (3.67 for expert, mean of 3.33 for nonexperts).
  
  
  CONCLUSIONS
  The results suggest that nonexpert annotation might be a feasible option for comprehensive labeling of annotated PDDIs across a broader range of drug product labels. Preannotation of drug mentions may ease the annotation task. However, preannotation of PDDIs, as operationalized in this study, presented the participants with difficulties. Future work should test if these issues can be addressed by the use of better performing NLP and a different approach to presenting the PDDI preannotations to users during the annotation workflow.
authors.affiliation: Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States. harryh@pitt.edu.
authors.forename: Harry
authors.identifier: http://orcid.org/0000-0001-8793-9982
authors.initials: H
authors.lastname: Hochheiser
authors.forename: Yifan
authors.identifier: http://orcid.org/0000-0002-0883-4742
authors.initials: Y
authors.lastname: Ning
authors.forename: Andres
authors.identifier: http://orcid.org/0000-0001-6037-5451
authors.initials: A
authors.lastname: Hernandez
authors.forename: John R
authors.identifier: http://orcid.org/0000-0003-4525-4250
authors.initials: JR
authors.lastname: Horn
authors.forename: Rebecca
authors.identifier: http://orcid.org/0000-0002-5719-8340
authors.initials: R
authors.lastname: Jacobson
authors.forename: Richard D
authors.identifier: http://orcid.org/0000-0002-2993-2085
authors.initials: RD
authors.lastname: Boyce
keywords: crowdsourcing; drug interactions, drug product labeling, structured product labels; natural language processing