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Studies suggest that one in three US adults use the Internet to diagnose orlearn about a health concern. However, such access to health information onlinecould exacerbate the disparities in health information availability and use.Health information seeking behavior (HISB) refers to the ways in whichindividuals seek information about their health, risks, illnesses, andhealth-protective behaviors. For patients engaging in searches for healthinformation on digital media platforms, health literacy divides can beexacerbated both by their own lack of knowledge and by algorithmicrecommendations, with results that disproportionately impact disadvantagedpopulations, minorities, and low health literacy users. This study reports onan exploratory investigation of the above challenges by examining whetherresponsible and representative recommendations can be generated using advancedanalytic methods applied to a large corpus of videos and their metadata on achronic condition (diabetes) from the YouTube social media platform. The paperfocusses on biases associated with demographic characters of actors usingvideos on diabetes that were retrieved and curated for multiple criteria suchas encoded medical content and their understandability to address patienteducation and population health literacy needs. This approach offers an immenseopportunity for innovation in human-in-the-loop, augmented-intelligence,bias-aware and responsible algorithmic recommendations by combining theperspectives of health professionals and patients into a scalable andgeneralizable machine learning framework for patient empowerment and improvedhealth outcomes.

On Curating Responsible and Representative Healthcare Video  Recommendations for Patient Education and Health Literacy: An Augmented  Intelligence Approach