Development of Methods to Assess Readability in Health and Medical Information: A Scoping Review.

Area of Science:

• Health Informatics
• Medical Communication
• Natural Language Processing

Background:

• Readability of health and medical information (HMI) is crucial for patient understanding and adherence.
• Existing readability measures for HMI require further development and validation.
• A systematic review is needed to identify common strategies in HMI readability measure development.

Purpose of the Study:

• To extract characteristics of studies developing readability measures for HMI.
• To explore common themes and strategies employed in HMI readability measure development.
• To identify gaps and opportunities for future research in this field.

Main Methods:

• Systematic literature search across four databases following PRISMA guidelines.
• Data analysis incorporating statistical summary, thematic analysis, and idea webbing.
• Inclusion of 14 articles from an initial search of 1,129 articles.

Main Results:

• Four primary development strategies emerged: Language Features (10 studies), Machine Learning (ML, 6 studies), Natural Language Processing (NLP, 5 studies), and Human Annotations (5 studies).
• ML and NLP techniques were frequently utilized and validated by both professional and layperson judgment.
• A decline in recent developments was observed, with most studies published between 2006 and 2015.

Conclusions:

• Current readability measures for HMI, despite utilizing advanced techniques like ML and NLP, have not achieved widespread adoption.
• Opportunities exist for developing more usable and reliable HMI readability measures through advancements in Artificial Intelligence and Large Language Models.
• Future research should focus on addressing the adoption gap and leveraging emerging AI technologies.