CAAC: Co-attentive Actionability Classification for Assessing Patient Education Videos

The use of video-sharing platforms for disseminating healthcare information is rapidly increasing worldwide. However, professionally produced videos from healthcare organizations cover only a small fraction of known health conditions. In contrast, user-generated videos often address a broader range of health topics and offer personal insights into the daily management of complex diseases, although their educational value remains uncertain. With the growing popularity of self-care videos, there is a critical need to automate manual evaluation methods to ensure these videos meet established health education guidelines. In this study, we propose a hierarchical co-attention mechanism within a transformer-based approach to assess the actionability of health-related videos, using criteria from the Patient Education Materials Assessment Tool (PEMAT). Our model leverages co-attention to dynamically align textual and visual features, enabling a more nuanced understanding of multimodal content. Extensive experiments on 1,795 YouTube videos demonstrate the superior performance of our approach, achieving 82.2% AUC and outperforming state-of-the-art video classifiers. This research contributes to the expanding literature on transformer architectures for video classification and extends the application of PEMAT guidelines to a larger data set of health-related content. By automating the evaluation process, our model overcomes the limitations of manual assessments, offering a scalable and efficient solution for ensuring the quality and reliability of healthcare information on digital platforms. History: This paper has been accepted by Kaushik Dutta for the Special Issue on Responsible AI and Data Science for Social Good. Funding: This research was supported in part by the National Library of Medicine [Grant R01LM013443] of the National Institutes of Health. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0493 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0493 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .