An Informatics Framework to Assess Consumer Health Language Complexity Differences: Proof-of-Concept Study

Area of Science:

• Health Informatics
• Linguistics
• Consumer Health Communication

Background:

• The disparity between health professionals' and consumers' language hinders effective health information comprehension.
• While using consumer health language (CHL) is a recognized solution, individuals possess diverse language proficiencies.
• Quantifying CHL complexity across different consumer groups is crucial for tailoring health information.

Purpose of the Study:

• To introduce an informatics framework, Consumer Health Language Complexity (CHELC), for assessing CHL complexity.
• To develop unifying scores (CHELCS) using syntax-level, text-level, term-level, and semantic-level metrics.
• To compare CHL complexity across general public, deaf/hearing-impaired, and autism spectrum disorder (ASD) online health forum users.

Main Methods:

• Analyzed over 64,000 posts from three online health forums (general public, AllDeaf, Wrong Planet).
• Calculated CHELCS for each user based on 8 validated language complexity metrics.
• Employed statistical tests (Kolmogorov-Smirnov, ANCOVA) to compare CHL complexity scores among user groups.

Main Results:

• Users in the general public forum exhibited higher CHL complexity, particularly in semantics and health terminology.
• Individuals with ASD used more complex vocabulary, while deaf/hearing-impaired users employed more complex syntax.
• Significant differences in CHL complexity were observed across all facets among the three user groups.

Conclusions:

• The CHELC framework effectively quantifies CHL complexity variations among diverse health consumer groups.
• Findings underscore the necessity of customizing health content to match the specific language complexities of different audiences.
• Tailoring health information is essential for improving comprehension and engagement across heterogeneous populations.