Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing
This addresses the problem of healthcare consumers struggling to access complex medical literature, though it is incremental as it builds on existing NLP and interface design approaches.
The authors tackled the challenge of making medical research papers more accessible to healthcare consumers by introducing Paper Plain, an interactive interface with NLP-powered features like definitions, plain language summaries, and guided questions. They found that participants using Paper Plain had an easier time reading and understanding papers without loss in comprehension compared to a typical PDF reader.
When seeking information not covered in patient-friendly documents, like medical pamphlets, healthcare consumers may turn to the research literature. Reading medical papers, however, can be a challenging experience. To improve access to medical papers, we introduce a novel interactive interface-Paper Plain-with four features powered by natural language processing: definitions of unfamiliar terms, in-situ plain language section summaries, a collection of key questions that guide readers to answering passages, and plain language summaries of the answering passages. We evaluate Paper Plain, finding that participants who use Paper Plain have an easier time reading and understanding research papers without a loss in paper comprehension compared to those who use a typical PDF reader. Altogether, the study results suggest that guiding readers to relevant passages and providing plain language summaries, or "gists," alongside the original paper content can make reading medical papers easier and give readers more confidence to approach these papers.