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.

翻译：研究显示,三分之一的美国成年人使用互联网来诊断或了解健康关切,然而,这种在线获取健康信息的机会可能加剧卫生信息提供和使用方面的差异。健康信息寻求行为(HISB)指的是个人如何从YouTube社会媒体平台寻求有关其健康、风险、疾病和健康保护行为的信息。对于在数字媒体平台上寻找健康信息的病人来说,由于他们本身缺乏知识和算法建议,健康知识差距可能加剧,其结果对处境不利的人口、少数民族和低健康知识使用者产生不成比例的影响。这项研究报告,通过研究是否可以利用高级分析方法,在YouTube社会媒体平台上应用大量长期状况(直线)的视频和元数据来生成负责任和有代表性的建议,对上述挑战进行探索性调查。本文侧重于与使用糖尿病视频的行为者人口特征相关的偏见,这些视频被检索和整理为多种标准,如编码医疗内容及其可理解性满足病人教育和人口健康知识普及需求。这一方法为在人类内部创新、强化和负责任的病人健康视角方面提供了一个巨大的机会,通过可扩展的智能和负责任的健康分析框架,通过将病人和成熟的学习结果纳入可理解的实验室结果框架。