Audio Generation and Optimization from Existing Resources for Patient Education

利用现有资源生成和优化患者教育音频

• 批准号: 10580849
• 负责人: GONDY LEROY
• 金额: $ 41.26万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10580849
• 关键词: Accent; Adoption; Affect; Affordable Care Act; Age; Arizona; Characteristics; Collaborations; Communication; Communities; Comprehension; Computer software; Computers; Data; Development; Educational Materials; Effectiveness; Evaluation; Frequencies; Gender; General Population; Generations; Goals; Government; Growth; Guidelines; Health; Healthcare; Hospitals; Household; Information Dissemination; Machine Learning; Measures; Mechanics; Medical; Medical Education; Methods; Modeling; Natural Language Processing; Neighborhood Health Center; Outcome; Pamphlets; Participant; Patient Education; Patients; Pilot Projects; Population; Printing; Research; Research Personnel; Resources; Software Tools; Source; Specific qualifier value; Speed; Technology; Testing; Text; Update; Voice; Work; Work Simplification; Writing; application programming interface; clinical encounter; clinical practice; clinically relevant; cost effective; design; digital; experience; handheld mobile device; health literacy; improved; information gathering; information processing; innovation; intelligent personal assistant; interactive tool; large scale data; novel; open source tool; patient oriented; preference; programs; real world application; recruit; skills; smart speaker; statistical and machine learning; symposium; tool; web based software; web site

Project Summary/Abstract Health literacy is vital to achieving and maintaining good health. Several national programs have emphasized this goal and its importance. Text is generally much more efficient and cost-effective for presenting healthcare information on a large scale than interactive tools and videos. Over the past decade, therefore, most medical information has been provided as text, e.g., via printed pamphlets or on websites. We are entering a new era where a new similarly effective mode of information dissemination is becoming increasingly available: audio accessed with mobile devices. Millions of households have and use smart speakers and virtual assistants and they are increasingly used by patients and consumers to gather information. Hospitals also plan to gradually integrate them among their tools. However, there exist few if any guidelines on optimal generation and use of audio. The overall goal of this project is to discover how to support the creation of optimal audio from existing text sources for consumer and patient education. To accomplish this, four aims are proposed. The first aim is to identify audio features that affect information comprehension and retention. Here, features in audio content and style (e.g., word frequency or grammatical complexity) of the underlying information will be tested for impact. In addition, two groups of features specific to the audio medium will be tested: the delivery features (e.g., speed and pauses) as well as meta-features (e.g., speaker characteristics such as gender or accent and bias in listeners). This first aim will rely on large-scale datasets, semi-automatically generated and augmented with user scores for comprehension gathered using Amazon Mechanical Turk (MTurk). Statistical and machine learning approaches will be used to tease out the best features and combinations. The second aim focuses on discovering how to augment text for audio and finding the optimal combination of text and audio for information comprehension and retention. Different combinations will be tested online with MTurk participants using controlled user studies. The third aim is to update, test and provide the existing online free text editor to generate optimized audio. We will also start dissemination of the tool to potential users including API access to components. The project will conclude with a summative evaluation with representative consumers recruited at a local community health center and further dissemination of preferences, practical obstacles, and best practices for the medical community to help increase health literacy through this new, popular audio medium. If successful, this project will generate best practices for the medical community in using audio as an additional method for bringing healthcare information to the general public; it will provide an online, free tool to generate audio leveraging these best practices and will include API access so that other researchers can easily integrate tool components into their research and tools; and it will provide immediate practical lessons from working with consumers relevant for clinical practice.

项目总结/摘要 健康素养对于实现和保持良好健康至关重要。一些国家计划强调， 这一目标及其重要性。文本通常在呈现医疗保健方面效率更高，成本效益更高 比互动工具和视频更大规模的信息。在过去的十年里，大多数医学 信息以文本形式提供，例如，通过印刷的小册子或网站。 我们正在进入一个新的时代，一种新的同样有效的信息传播模式正在成为 越来越多地可用：通过移动的设备访问音频。数以百万计的家庭拥有并使用智能 扬声器和虚拟助手，它们越来越多地被患者和消费者用来收集 信息.医院也计划逐步将它们整合到他们的工具中。然而，如果有的话， 关于最佳音频生成和使用的准则。 该项目的总体目标是发现如何支持从现有文本创建最佳音频 消费者和患者教育的来源。为此，提出了四个目标。第一个目标是 识别影响信息理解和记忆的音频特征。这里，音频内容中的特征和 风格（例如，词频或语法复杂性）的影响进行测试。在 另外将测试音频媒体特有的两组特征：传送特征（例如，速度 和暂停）以及元特征（例如，说话者的性别或口音等特征， 听众）。第一个目标将依赖于大规模的数据集，半自动生成和增强， 使用Amazon Mechanical Turk（MTurk）收集的用户理解分数。统计和机器 将使用学习方法来梳理出最佳特征和组合。第二个目标是 发现如何为音频增加文本，并找到文本和音频信息的最佳组合 理解和记忆。不同的组合将与MTurk参与者在线测试， 对照用户研究。第三个目标是更新、测试和提供现有的在线免费文本编辑器， 生成优化的音频。我们还将开始向潜在用户分发该工具，包括API访问 件.该项目将结束与代表性的消费者招募的总结性评估， 当地社区卫生中心和进一步传播的偏好，实际障碍， 通过这种新的流行的音频媒体，医学界帮助提高健康素养的做法。 如果成功，该项目将为医疗界提供使用音频作为附加功能的最佳实践。 一种将医疗保健信息带给公众的方法;它将提供一个在线的免费工具来生成 音频利用这些最佳实践，并将包括API访问，以便其他研究人员可以轻松 将工具组成部分纳入其研究和工具;它将提供直接的实际经验教训， 与临床实践相关的消费者合作。