Dynamic Generation of a Table of Contents with Consumer-Friendly Labels

Consumers increasingly look to the Internet for health information, but available resources are too difficult for the majority to understand. Interactive tables of contents (TOC) can help consumers access health information by providing an easy to understand structure. Using natural language processing and the Unified Medical Language System (UMLS), we have automatically generated TOCs for consumer health information. The TOC are categorized according to consumer-friendly labels for the UMLS semantic types and semantic groups. Categorizing phrases by semantic types is significantly more correct and relevant. Greater correctness and relevance was achieved with documents that are difficult to read than with those at an easier reading level. Pruning TOCs to use categories that consumers favor further increases relevancy and correctness while reducing structural complexity

Evaluating Online Health Information: Beyond Readability Formulas

Although understanding health information is important, the texts provided are often difficult to understand. There are formulas to measure readability levels, but there is little understanding of how linguistic structures contribute to these difficulties. We are developing a toolkit of linguistic metrics that are validated with representative users and can be measured automatically. In this study, we provide an overview of our corpus and how readability differs by topic and source. We compare two documents for three groups of linguistic metrics. We report on a user study evaluating one of the differentiating metrics: the percentage of function words in a sentence. Our results show that this percentage correlates significantly with ease of understanding as indicated by users but not with the readability formula levels commonly used. Our study is the first to propose a user validated metric, different from readability formulas

Using a Digital Library of Images for Communication: Comparison of a Card-Based System to PDA Software

Autism spectrum disorder has become one of the most prevalent developmental disorders and one of the main impairments is difficulty with communication. One method of augmentative and alternative communication is the use of the Picture Exchange Communication System (PECS) to create messages using a series of images printed on cards and organized in binders. We are developing a digital alternative based on an image library that is displayed on a personal digital assistant (PDA). We conducted an initial user acceptance study that compared the effectiveness and usability of both systems. The study showed that the PDA system was able to communicate messages to adult recipients as effectively as PECS. However, the PDA was perceived to be more current, of higher quality, easier, and more normal looking than the PECS binder

A Classifier to Evaluate Language Specificity in Medical Documents

Consumer health information written by health care professionals is often inaccessible to the consumers it is written for. Traditional readability formulas examine syntactic features like sentence length and number of syllables, ignoring the target audience\u27s grasp of the words themselves. The use of specialized vocabulary disrupts the understanding of patients with low reading skills, causing a decrease in comprehension. A naive Bayes classifier for three levels of increasing medical terminology specificity (consumer/patient, novice health learner, medical professional) was created with a lexicon generated from a representative medical corpus. Ninety-six percent accuracy in classification was attained. The classifier was then applied to existing consumer health web pages. We found that only 4% of pages were classified at a layperson level, regardless of the Flesch reading ease scores, while the remaining pages were at the level of medical professionals. This indicates that consumer health web pages are not using appropriate language for their target audience

Perils of Providing Visual Health Information Overviews for Consumers with Low Health Literacy or High Stress

This pilot study explores the impact of a health topics overview (HTO) on reading comprehension. The HTO is generated automatically based on the presence of Unified Medical Language System terms. In a controlled setting, we presented health texts and posed 15 questions for each. We compared performance with and without the HTO. The answers were available in the text, but not always in the HTO. Our study (n=48) showed that consumers with low health literacy or high stress performed poorly when the HTO was available without linking directly to the answer. They performed better with direct links in the HTO or when the HTO was not available at all. Consumers with high health literacy or low stress performed better regardless of the availability of the HTO. Our data suggests that vulnerable consumers relied solely on the HTO when it was available and were misled when it did not provide the answer

Dynamic Generation of a Health Topics Overview from Consumer Health Information Documents

Online health information use is increasing, but can be too difficult for consumers. We created a system that dynamically generates a health topics overview for consumer health web pages that organises the information into four consumer-preferred categories while displaying topic prevalence through visualisation. It accesses both a consumer health vocabulary and the Unified Medical Language System (UMLS). We evaluated its ability by calculating precision, recall, and F-score for phrase extraction and categorisation. We tested pages from three different consumer web sites. Overall, precision is 82&37;, recall is 75&37;, and F-score is 78&37;, and precision between sites did not significantly differ