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: 10295641
• 负责人: GONDY LEROY
• 金额: $ 25.79万
• 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/10295641
• 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.

项目概要/摘要 健康素养对于实现和保持健康至关重要。多项国家计划强调 这个目标及其重要性。文本通常对于呈现医疗保健更加有效且更具成本效益 比交互式工具和视频更大规模的信息。因此，在过去十年中，大多数医疗 信息以文本形式提供，例如通过印刷小册子或在网站上提供。 我们正在进入一个新时代，一种新的同样有效的信息传播模式正在成为 越来越可用：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 扬声器和虚拟助手越来越多地被患者和消费者用来收集信息 信息。医院还计划逐步将它们整合到他们的工具中。然而，存在的即使有也很少 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从现有文本创建最佳音频 消费者和患者教育的来源。为了实现这一目标，提出了四个目标。第一个目标是 识别影响信息理解和保留的音频特征。在这里，音频内容和 将测试基础信息的风格（例如词频或语法复杂性）的影响。在 此外，还将测试音频媒体特有的两组功能：传输功能（例如，速度） 和停顿）以及元特征（例如，说话者的特征，例如性别或口音以及偏见） 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 Amazon Mechanical Turk (MTurk) 收集的用户理解得分。统计与机器 将使用学习方法来梳理出最佳的功能和组合。第二个目标侧重于 探索如何增强音频文本并找到文本和音频信息的最佳组合 理解和保留。 MTurk 参与者将使用以下方式在线测试不同的组合 受控用户研究。第三个目标是更新、测试并提供现有的在线自由文本编辑器 生成优化的音频。我们还将开始向潜在用户传播该工具，包括 API 访问 成分。该项目将以在以下地点招募的代表性消费者进行总结性评估作为结束： 当地社区卫生中心，并进一步传播偏好、实际障碍和最佳方案 医学界通过这种新的、流行的音频媒体帮助提高健康素养的做法。 如果成功，该项目将为医学界提供使用音频作为附加手段的最佳实践 向公众提供医疗保健信息的方法；它将提供一个在线免费工具来生成 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 将工具组件集成到他们的研究和工具中；它将提供直接的实践经验教训 与临床实践相关的消费者合作。