Evaluation of quality and readability of over-the-counter medication package inserts.

BACKGROUND: Over-the-counter (OTC) medication package inserts are vital references for healthcare professionals to make drug recommendations and influential education materials that patients commonly refer to when self-caring. However, little is known about the quality and readability of the OTC medication package inserts in China. OBJECTIVES: This study aimed at evaluating the quality and readability of OTC medication package inserts. METHODS: Package inserts of OTC medication were selected based on the market share and pharmacological category. The quality of the package inserts was evaluated based on standards adapted separately from the Chinese National Medical Products Administration (NMPA) and European Medicines Agency's (EMA) Working Group. The readability was assessed using the Patient Education Materials Assessment Tool (PEMAT) in conjunction with the Chinese Readability Index Explorer (CRIE). RESULTS: A total of 29 OTC medication package inserts consisting of 12 Western Medicine (WM) and 17 Chinese Tradition Patent Medicine (CTPM) package inserts were included. Overall, the OTC package inserts met 92% of the NMPA standards and 54% of the EMA standards. In terms of readability assessment using PEMAT, the overall median (interquartile range) understandability score was 38% (38-45%) and for actionability score was 40% (40-55%). The overall text reading level of package inserts measured by the CRIE, after removing some medical jargon, is equivalent to the median reading level for the 12th (9.5-12th) grade reading level. CONCLUSIONS: The quality of OTC medication package inserts was satisfactory under internal standards but poor under international standards. Some OTC pharmacological information is not provided due to lack of research, especially for CTPM. A more informative and comprehensive package insert may be needed to guide drug use decisions. OTC medication package inserts are not appropriate patient education materials in terms of readability. Additional materials may be developed to supplement package inserts for patient education for OTC medications.

Constructing and validating readability models: the method of integrating multilevel linguistic features with machine learning.

Multilevel linguistic features have been proposed for discourse analysis, but there have been few applications of multilevel linguistic features to readability models and also few validations of such models. Most traditional readability formulae are based on generalized linear models (GLMs; e.g., discriminant analysis and multiple regression), but these models have to comply with certain statistical assumptions about data properties and include all of the data in formulae construction without pruning the outliers in advance. The use of such readability formulae tends to produce a low text classification accuracy, while using a support vector machine (SVM) in machine learning can enhance the classification outcome. The present study constructed readability models by integrating multilevel linguistic features with SVM, which is more appropriate for text classification. Taking the Chinese language as an example, this study developed 31 linguistic features as the predicting variables at the word, semantic, syntax, and cohesion levels, with grade levels of texts as the criterion variable. The study compared four types of readability models by integrating unilevel and multilevel linguistic features with GLMs and an SVM. The results indicate that adopting a multilevel approach in readability analysis provides a better representation of the complexities of both texts and the reading comprehension process.