There are several studies that suggest that MTurk workers are as good as ‘experts’ in different circumstances, and this one looks at how well Turkers can understand drug label information. And Turkers understand this information as well as experts.
“Each HIT only involves a worker making a binary judgment of whether a highlighted disease, in context of a given drug label, is an indication. In addition, this study is novel in the crowdsourcing interface design where the annotation guidelines are encoded into user options. For evaluation, we assess the ability of our proposed method to achieve high-quality annotations in a time-efficient and cost-effective manner. We posted over 3000 HITs drawn from 706 drug labels on MTurk. Within 8 h of posting, we collected 18 775 judgments from 74 workers, and achieved an aggregated accuracy of 96% on 450 control HITs (where gold-standard answers are known), at a cost of $1.75 per drug label. On the basis of these results, we conclude that our crowdsourcing approach not only results in significant cost and time saving, but also leads to accuracy comparable to that of domain experts.”
Citation: Khare R, Burger JD, Aberdeen JS, Tresner-Kirsch DW, Corrales TJ, Hirchman L, et al. Scaling drug indication curation through crowdsourcing. Database (Oxford) 2015