  Studies suggest that one in three US adults use the Internet to diagnose or
learn about a health concern. However, such access to health information online
could exacerbate the disparities in health information availability and use.
Health information seeking behavior (HISB) refers to the ways in which
individuals seek information about their health, risks, illnesses, and
health-protective behaviors. For patients engaging in searches for health
information on digital media platforms, health literacy divides can be
exacerbated both by their own lack of knowledge and by algorithmic
recommendations, with results that disproportionately impact disadvantaged
populations, minorities, and low health literacy users. This study reports on
an exploratory investigation of the above challenges by examining whether
responsible and representative recommendations can be generated using advanced
analytic methods applied to a large corpus of videos and their metadata on a
chronic condition (diabetes) from the YouTube social media platform. The paper
focusses on biases associated with demographic characters of actors using
videos on diabetes that were retrieved and curated for multiple criteria such
as encoded medical content and their understandability to address patient
education and population health literacy needs. This approach offers an immense
opportunity for innovation in human-in-the-loop, augmented-intelligence,
bias-aware and responsible algorithmic recommendations by combining the
perspectives of health professionals and patients into a scalable and
generalizable machine learning framework for patient empowerment and improved
health outcomes.
