Leveraging YouTube Video Analytics for Patient Education: A Digital TherapyTool for Clinicians to Retrieve and Recommend Understandable Videos on Chronic Disease Management

利用 YouTube 视频分析进行患者教育：临床医生检索和推荐易于理解的慢性病管理视频的数字治疗工具

• 批准号: 10631959
• 负责人: REMA PADMAN
• 金额: $ 31.71万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10631959
• 关键词: Accounting; Adherence; Adult; Assessment tool; Automation; Caregivers; Caring; Chronic; Chronic Disease; Clinic Visits; Clinical; Clinical Trials; Communication; Complement; Computing Methodologies; Diabetes Mellitus; Diagnosis; Dimensions; Disease; Disease Management; Educational Materials; Elements; Evaluation; Future; Guidelines; Health; Health Care Costs; Health Promotion; Healthcare; Home; Hospitals; Human; Impact evaluation; Information Centers; Intervention; Interview; Knowledge; Libraries; Literature; Machine Learning; Managed Care; Medical; Mental Health; Metadata; Methodology; Methods; Natural Language Processing; Non-Insulin-Dependent Diabetes Mellitus; Outcome; Pamphlets; Pathogenesis; Pathway interactions; Patient Education; Patients; Performance; Personal Satisfaction; Physicians; Population; Prevention; Preventive; Printing; Protocols documentation; Recommendation; Research; Resources; Retrieval; Self Care; Self Management; Site; Social Network; Social Sciences; Symptoms; System; Technology; Time; Training; Twitter; augmented intelligence; care delivery; computer science; design; digital; digital treatment; empowerment; health communication; health literacy; improved; individual patient; innovation; literate; machine learning framework; machine learning method; novel; patient engagement; patient oriented; point of care; poor health outcome; programs; prototype; social media; success; technology platform; two-dimensional; verbal

Project Summary The easy availability of huge amount of user generated health information on social networks, blogs, YouTube, Twitter, and hospital review sites presents an unprecedented opportunity to investigate how social media can be a channel to inform and communicate healthcare information to patients and facilitate patient- centric health promotion and literacy improvement. YouTube hosts over 100 million healthcare related videos on a variety of medical conditions. This plethora of user-generated content can be leveraged by patients to improve adherence to clinical guidelines and self-care required for management of chronic diseases. In this project, we propose an augmented intelligence-based approach that effectively combines human input from domain experts and consumers with machine learning and natural language processing methods from computer science to winnow down and retrieve relevant, contextualized video materials that clinicians can recommend to patients. The problem of identifying the most relevant videos from a patient perspective is challenging, but provides an immense innovation space for this approach. We will leverage a co-training machine learning framework and incorporate inputs from patient education assessment tools and clinicians to assess diabetes- related videos on two dimensions: the amount of medical information encoded in the videos and video understandability. We will develop a user-centric patient education video recommender system by integrating these two dimensions with the YouTube video ranking results. Furthermore, we will apply a multi-dimensional evaluation strategy that combines computational evaluations, comparisons with YouTube baseline, and causal analysis methods to understand the performance of the automated methods and the relationship between video understandability and collective user engagement. Finally, we will integrate our computational approach in a modular research prototype technology platform that will accept health related YouTube videos as inputs (generated from patients' keyword searches on diabetes) and produce a ranked list of top 10 retrieved videos for further review by clinicians, and evaluated for barriers and facilitators of the technology usage. Recommending relevant educational materials in video format that leverage user-generated content is one way to deliver personalized and contextualized healthcare information, and resources for self-care management, to patients and consumers. As technology continues to advance and evolve, our methods can be refined further and evaluated via clinical trials to improve patient education, empower patients, caregivers and clinicians, and improve societal health and health literacy.

项目摘要 用户在社交网络、博客、 YouTube、Twitter和医院评论网站提供了一个前所未有的机会，可以调查社交媒体如何影响人们的生活。 媒体可以成为向患者告知和传达医疗保健信息的渠道，并促进患者- 以促进健康和提高识字率为中心。YouTube托管超过1亿个医疗保健相关视频 各种各样的医疗条件。患者可以利用这种过多的用户生成内容， 更好地遵守临床指南和管理慢性病所需的自我护理。在这 项目，我们提出了一种基于增强智能的方法，有效地结合了人类的输入， 领域专家和消费者使用计算机的机器学习和自然语言处理方法 科学筛选和检索相关的，背景化的视频材料，临床医生可以推荐， 患者从患者的角度识别最相关的视频的问题是具有挑战性的，但是 为这种方法提供了巨大的创新空间。我们将利用联合训练机器学习 制定框架并纳入患者教育评估工具和临床医生的输入，以评估糖尿病- 两个维度上的相关视频：视频中编码的医疗信息量和视频 可理解性我们将开发一个以用户为中心的病人教育视频推荐系统， 这两个维度与YouTube视频排名结果。此外，我们将采用多维 评估策略，结合计算评估，与YouTube基线的比较，以及因果关系 分析方法，了解自动化方法的性能和视频之间的关系 可理解性和集体用户参与。最后，我们将把我们的计算方法整合到一个 模块化研究原型技术平台，将接受与健康相关的YouTube视频作为输入 （从患者对糖尿病的关键词搜索中生成），并生成前10名检索视频的排名列表， 由临床医生进一步审查，并评估技术使用的障碍和促进因素。推荐 利用用户生成内容的视频格式的相关教育材料是提供 向患者提供个性化和情境化的医疗保健信息以及用于自我护理管理的资源 和消费者随着技术的不断进步和发展，我们的方法可以进一步改进， 通过临床试验进行评估，以改善患者教育，增强患者、护理人员和临床医生的能力， 改善社会卫生和卫生知识普及。