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Health literacy is crucial to supporting good health and is a major national goal. Audio delivery of information is becoming more popular for informing oneself. In this study, we evaluate the effect of audio enhancements in the form of information emphasis and pauses with health texts of varying difficulty and we measure health information comprehension and retention. We produced audio snippets from difficult and easy text and conducted the study on Amazon Mechanical Turk (AMT). Our findings suggest that emphasis matters for both information comprehension and retention. When there is no added pause, emphasizing significant information can lower the perceived difficulty for difficult and easy texts. Comprehension is higher (54%) with correctly placed emphasis for the difficult texts compared to not adding emphasis (50%). Adding a pause lowers perceived difficulty and can improve retention but adversely affects information comprehension., (©2024 AMIA - All rights reserved.)

Critical to producing accessible content is an understanding of what characteristics affect understanding and comprehension. To answer this question, we are producing a large corpus of health-related texts with associated questions that can be read or listened to by study participants to measure the difficulty of the underlying content, which can later be used to better understand text difficulty and user comprehension. In this paper, we examine methods for automatically generating multiple-choice questions using Google's related questions and ChatGPT. Overall, we find both algorithms generate reasonable questions that are complementary; ChatGPT questions are more similar to the snippet while Google related-search questions have more lexical variation., (©2024 AMIA - All rights reserved.)

Text and audio simplification to increase information comprehension are important in healthcare. With the introduction of ChatGPT, evaluation of its simplification performance is needed. We provide a systematic comparison of human and ChatGPT simplified texts using fourteen metrics indicative of text difficulty. We briefly introduce our online editor where these simplification tools, including ChatGPT, are available. We scored twelve corpora using our metrics: six text, one audio, and five ChatGPT simplified corpora (using five different prompts). We then compare these corpora with texts simplified and verified in a prior user study. Finally, a medical domain expert evaluated the user study texts and five, new ChatGPT simplified versions. We found that simple corpora show higher similarity with the human simplified texts. ChatGPT simplification moves metrics in the right direction. The medical domain expert's evaluation showed a preference for the ChatGPT style, but the text itself was rated lower for content retention., (©2024 AMIA - All rights reserved.)

Text continues to be an important medium for communicating health-related information. We have built a text simplification tool that gives concrete suggestions on how to simplify health and medical texts. An important component of the tool identifies difficult words and suggests simpler synonyms based on pre-existing resources (WordNet and UMLS). These candidate substitutions are not always appropriate in all contexts. In this paper, we introduce a filtering algorithm that utilizes semantic similarity based on word embeddings to determine if the candidate substitution is appropriate in the context of the text. We provide an analysis of our approach on a new dataset of 788 labeled substitution examples. The filtering algorithm is particularly helpful at removing obvious examples and can improve the precision by 3% at a recall level of 95%., (©2022 AMIA - All rights reserved.)

There is often a discontinuity between patients' literacy level and educational materials. In response, we are developing an online medical text simplification editor. In this paper, we describe generating grammar simplification rules from a large parallel corpus (N=141,500) containing original sentences and their simplified variants. We algorithmically identified grammatical transformations between sentences (N=26,600) and used distributional characteristics in two corpora to select transformations with the broadest application and the least ambiguity. This resulted in a top set of 146 rules. Two experts evaluated 20 representative rules reflecting 4 characteristics (long/short and weak/strong) each with 5 example sentences. Generally, we found that the rules are helpful for guiding simplification. Using a 5-point Likert scale (5=best), stronger rules scored higher for ease of applying (4.11), overall helpfulness (4.40) and usefulness of examples (4.05). Rule length did not affect the expert scores. The grammar simplification rules are being integrated in our text editor.