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: 10439893
• 负责人: GONDY LEROY
• 金额: $ 34.93万
• 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/10439893
• 关键词: Accent; Adoption; Affect; Affordable Care Act; Age; Arizona; Characteristics; Collaborations; Communication; Communities; Comprehension; Computer software; Computers; Data; Development; Effectiveness; Evaluation; Frequencies; Gender; General Population; Generations; Goals; Government; Growth; Guidelines; Health; Healthcare; Hospitals; Household; Information Dissemination; Machine Learning; Measures; Mechanics; Medical; Methods; Modeling; Natural Language Processing; Neighborhood Health Center; Operative Surgical Procedures; Outcome; Pamphlets; Participant; Patient Education; Patients; Pilot Projects; Population; 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 processing; innovation; intelligent personal assistant; interactive tool; large scale data; novel; open source tool; preference; programs; real world application; recruit; skills; statistical and machine learning; symposium; tool; 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.

项目摘要/摘要 健康素养对于实现和维持良好的健康至关重要。几个国家计划强调 这个目标及其重要性。文本通常在呈现医疗保健方面更有效和成本效益 大规模的信息比交互式工具和视频。因此，在过去的十年中，大多数医学 信息已作为文本（例如，通过印刷的小册子或网站提供）提供。 我们正在进入一个新时代，新时代，新的类似有效的信息传播方式正在成为 越来越多的可用：通过移动设备访问音频。数以百万计的家庭拥有并使用智能 演讲者和虚拟助手，患者和消费者越来越多地使用他们 信息。医院还计划逐渐将它们整合在一起。但是，几乎没有 关于音频的最佳生成和使用指南。 该项目的总体目标是发现如何支持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。第一个目的是 确定影响信息理解和保留的音频功能。在这里，音频内容的功能以及 基础信息的样式（例如，单词频率或语法复杂性）将进行影响。在 此外，将测试两组特定于音频介质的功能：交付功能（例如，速度 和暂停）以及元功能（例如，诸如性别或口音和偏见之类的说话者特征 听众）。第一个目标将依赖于半自动生成和增强的大规模数据集 使用亚马逊机械土耳其人（MTURK）收集理解的用户分数。统计和机器 学习方法将用于逗弄最佳功能和组合。第二个目标重点 发现如何增强音频的文本并找到文本和音频的最佳组合以获取信息 理解和保留。不同的组合将与MTURK参与者在线测试 受控的用户研究。第三个目的是更新，测试并向现有的在线免费文本编辑器提供 生成优化的音频。我们还将开始将该工具传播给潜在用户，包括API访问 成分。该项目将以总结性评估结束，并在 当地社区卫生中心，进一步传播偏好，实用障碍和最佳 医学界通过这种新的流行音频媒体来帮助提高健康素养的实践。 如果成功，该项目将为医学界提供最佳实践，以使用音频作为额外 将医疗保健信息带给公众的方法；它将提供一个免费的，免费的工具来生成 利用这些最佳实践的音频，并将包括API访问，以便其他研究人员可以轻松 将工具组件集成到他们的研究和工具中；它将提供直接的实用课程 与与临床实践相关的消费者合作。