A Smart Mobile App to Simplify Medical Documents and Improve Health Literacy: System Design and Feasibility Validation

Abstract

Background People with low health literacy experience more challenges in understanding instructions given by their health providers, following prescriptions, and understanding their health care system sufficiently to obtain the maximum benefits. People with insufficient health literacy have high risk of making medical mistakes, more chances of experiencing adverse drug effects, and inferior control of chronic diseases. Objective This study aims to design, develop, and evaluate a mobile health app, MediReader, to help individuals better understand complex medical materials and improve their health literacy. Methods MediReader is designed and implemented through several steps, which are as follows: measure and understand an individual’s health literacy level; identify medical terminologies that the individual may not understand based on their health literacy; annotate and interpret the identified medical terminologies tailored to the individual’s reading skill levels, with meanings defined in the appropriate external knowledge sources; evaluate MediReader using task-based user study and satisfaction surveys. Results On the basis of the comparison with a control group, user study results demonstrate that MediReader can improve users’ understanding of medical documents. This improvement is particularly significant for users with low health literacy levels. The satisfaction survey showed that users are satisfied with the tool in general. Conclusions MediReader provides an easy-to-use interface for users to read and understand medical documents. It can effectively identify medical terms that a user may not understand, and then, annotate and interpret them with appropriate meanings using languages that the user can understand. Experimental results demonstrate the feasibility of using this tool to improve an individual’s understanding of medical materials.

Transforming unstructured digital clinical notes for improved health literacy

“…Although the transformed clinical notes might lose some information compared to the original text using medical jargon and specialized words, the proposed transformation pipeline helps improve health literacy substantially, resulting in information gain for individual patients when compared with the acquired information by reading and comprehending the original clinical records. Promisingly, the proposed pipeline can be further improved by incorporating knowledge graphs (Hendawi et al, 2022) and biomedical vocabularies and ontologies repository, e.g. Unified Medical Language System (Bodenreider, 2004) for target vocabulary enrichment and candidate substitutions generation, and also by training the simplification model with larger data sets in medical domain.…”

“…The solutions for both these requirements are long-drawn and complicated for both the care providers and the consumers. Promisingly, automated solutions using natural language processing (NLP) techniques and machine learning (ML) methods can help bridge the gap between both sides and hence provide more opportunities for better care (Hendawi, Alian, & Li, 2022). It is well known that clinical notes represent a huge collection of information on patients, including the whole process of caregiving ranging from patients' diagnosis and admission to discharge.…”

A Mobile App That Addresses Interpretability Challenges in Machine Learning–Based Diabetes Predictions: Survey-Based User Study

“…The knowledge matching module assumes a pivotal role in our platform, serving as the vital link between data items, such as features extracted from a training data set, and the entities present within the KG. This module’s functionality is rooted in a sophisticated semantic matching process [ 37 ] that assesses the semantic distances between data items and the semantic entities within the KG.…”

Bridging Culture and Care: A Mobile App for Diabetes Self-Care Honoring Native American Cultural Practices

“…It allows users to select their preferred language and adjust the complexity of the language to match their literacy level, ensuring accessibility and comprehension for all. Insights into this aspect of our design process are detailed in publication [18].…”