Improving Consumer Understanding of Medical Text: Development and Validation of a New SubSimplify Algorithm to Automatically Generate Term Explanations in English and Spanish

Abstract

BackgroundWhile health literacy is important for people to maintain good health and manage diseases, medical educational texts are often written beyond the reading level of the average individual. To mitigate this disconnect, text simplification research provides methods to increase readability and, therefore, comprehension. One method of text simplification is to isolate particularly difficult terms within a document and replace them with easier synonyms (lexical simplification) or an explanation in plain language (semantic simplification). Unfortunately, existing dictionaries are seldom complete, and consequently, resources for many difficult terms are unavailable. This is the case for English and Spanish resources.ObjectiveOur objective was to automatically generate explanations for difficult terms in both English and Spanish when they are not covered by existing resources. The system we present combines existing resources for explanation generation using a novel algorithm (SubSimplify) to create additional explanations.MethodsSubSimplify uses word-level parsing techniques and specialized medical affix dictionaries to identify the morphological units of a term and then source their definitions. While the underlying resources are different, SubSimplify applies the same principles in both languages. To evaluate our approach, we used term familiarity to identify difficult terms in English and Spanish and then generated explanations for them. For each language, we extracted 400 difficult terms from two different article types (General and Medical topics) balanced for frequency. For English terms, we compared SubSimplify’s explanation with the explanations from the Consumer Health Vocabulary, WordNet Synonyms and Summaries, as well as Word Embedding Vector (WEV) synonyms. For Spanish terms, we compared the explanation to WordNet Summaries and WEV Embedding synonyms. We evaluated quality, coverage, and usefulness for the simplification provided for each term. Quality is the average score from two subject experts on a 1-4 Likert scale (two per language) for the synonyms or explanations provided by the source. Coverage is the number of terms for which a source could provide an explanation. Usefulness is the same expert score, however, with a 0 assigned when no explanations or synonyms were available for a term.ResultsSubSimplify resulted in quality scores of 1.64 for English (P<.001) and 1.49 for Spanish (P<.001), which were lower than those of existing resources (Consumer Health Vocabulary [CHV]=2.81). However, in coverage, SubSimplify outperforms all existing written resources, increasing the coverage from 53.0% to 80.5% in English and from 20.8% to 90.8% in Spanish (P<.001). This result means that the usefulness score of SubSimplify (1.32; P<.001) is greater than that of most existing resources (eg, CHV=0.169).ConclusionsOur approach is intended as an additional resource to existing, manually created resources. It greatly increases the number of difficult terms for which an easier alternative can be made available,...

Tailored Retrieval of Health Information from the Web for Facilitating Communication and Empowerment of Elderly People

“…Notice that in (Alfano et al, 2019c) we have evaluated the language familiarity of Web pages targeted to different audience types. This has been done by computing the "term familiarity index" of a word (i.e., number of results provided by the Google search engine, Kloehn, et al, 2018;Leroy, et al, 2012) and then computing the language familiarity of a Web page as the average of the term familiarity indexes of its words. The results clearly show that, on average, the Web pages targeted to patients have a much higher language familiarity, and thus a simpler terminology than the Web pages targeted to clinicians or medical researchers.…”

A Year of Papers Using Biomedical Texts: Findings from the Section on Natural Language Processing of the IMIA Yearbook

“…Yet, medical educational texts are often written beyond the reading level of the average individual. One work proposes to isolate particularly difficult terms within documents and to replace them with easier synonyms or explanations in plain language 11 . The main accent is put on the automatic generation of explanations for difficult terms in English and Spanish.…”

“…Among the pre-selected publications, we can for instance mention: Chinese, addressed through the manually annotated dataset containing 540 breast radiology reports 5 ; Italian, addressed through 5,432 non-annotated medical reports belonging to patients with rare arrhythmias and the manually curated hospital database 6 ; French, addressed for detecting medical events as an epidemiological purpose 7 , or through the clinical data warehouse for thousands of patients 8 ; Japanese, addressed through the data set of 5,000 patient pharmacovigilance complaints 9 ; and Korean, addressed through 30 rheumatic patients discharge summaries annotated with temporal information 10 . Besides, non-clinical data have been used in Spanish and English for the building of vocabulary useful for patients to better understand the medical and health information in these languages 11 . In almost all these works, the corpora built and annotated are proprietary and can only be used by the teams that built them in collaboration with their clinical colleagues.…”

Language Complexity in On-line Health Information Retrieval

“…We have taken the three subsets presented in the previous section, related to Patient, Clinician, and MedicalResearcher audience types, and, for each quadruple, we have analysed the related Web page in order to estimate its language complexity. To this end, we have evaluated the 'term familiarity index', as described in [6], [24], [25] of the English and non-empty Web pages (around 50% of the total). In particular, for each Web page, we have computed the term familiarity of each word by using the number of results provided by the Google search engine and we have then computed the page familiarity index by averaging all the term familiarity indexes.…”