"Dr. Detective": Combining gamification techniques and crowdsourcing to create a gold standard in medical text

Abstract

This paper proposes a design for a gamified crowdsourcing workflow to extract annotation from medical text. Developed in the context of a general crowdsourcing platform, Dr. Detective is a game with a purpose that engages medical experts into solving annotation tasks on medical case reports, tailored to capture disagreement between annotators. It incorporates incentives such as learning features, to motivate a continuous involvement of the expert crowd. The game was designed to identify expressions valuable for training NLP tools, and interpret their relation in the context of medical diagnosing. In this way, we can resolve the main problem in gathering ground truth from experts - that the low inter-annotator agreement is typically caused by different interpretations of the text. We report on the results of a pilot study assessing the usefulness of this game. The results show that the quality of the annotations by the expert crowd are comparable to those of an NLP parser. Furthermore, we observed that allowing game users to access each others' answers increases agreement between annotators.