YouTube Video Analytics for Patient Health Literacy: Evidence from Colonoscopy Preparation Videos

Videos can be an effective way to deliver contextualized, just-in-time medical information for patient education. However, video analysis, from topic identification and retrieval to extraction and analysis of medical information and understandability from a patient perspective are extremely challenging tasks. This study utilizes data analysis methods to retrieve medical information from YouTube videos concerning colonoscopy to manage health conditions. We first use the YouTube Data API to collect metadata of desired videos on select search keywords and use Google Video Intelligence API to analyze texts, frames and objects data. Then we annotate the YouTube video materials on medical information, video understandability annotation and recommendation. We develop a bidirectional long short-term memory (BLSTM) model to identify medical terms in videos and build three classifiers to group videos based on the level of encoded medical information, video understandability level and whether the videos are recommended. Our study provides healthcare practitioners and patients with guidelines for generating new educational video content and enabling management of health conditions.