Crowdsourcing Information Extraction for Biomedical Systematic Reviews
This work-in-progress addresses the expense of systematic reviews for biomedical researchers, but it is incremental as it builds on existing crowdsourcing approaches without yet proving feasibility.
The study tackled the high cost of biomedical systematic reviews by designing a crowdsourcing task for information extraction, finding that concrete examples improved worker understanding and that payment levels attracted workers from low-income countries.
Information extraction is a critical step in the practice of conducting biomedical systematic literature reviews. Extracted structured data can be aggregated via methods such as statistical meta-analysis. Typically highly trained domain experts extract data for systematic reviews. The high expense of conducting biomedical systematic reviews has motivated researchers to explore lower cost methods that achieve similar rigor without compromising quality. Crowdsourcing represents one such promising approach. In this work-in-progress study, we designed a crowdsourcing task for biomedical information extraction. We briefly report the iterative design process and the results of two pilot testings. We found that giving more concrete examples in the task instruction can help workers better understand the task, especially for concepts that are abstract and confusing. We found a few workers completed most of the work, and our payment level appeared more attractive to workers from low-income countries. In the future, we will further evaluate our results with reference to gold standard extractions, thus assessing the feasibility of tasking crowd workers with extracting biomedical intervention information for systematic reviews.