Helping Doctors Doctor: Using AI to Automate Documentation and "De-Autonomate" Health Care

帮助医生医生：使用人工智能实现文档自动化和医疗保健“去自动化”

• 批准号: 10701364
• 负责人: KEVIN B. JOHNSON
• 金额: $ 113.75万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10701364
• 关键词: Algorithms; Area; Characteristics; Clinical; Communication; Computer Vision Systems; Computers; Consumption; Country; Data; Data Analytics; Decision Making; Diagnosis; Documentation; Electronic Health Record; Engineering; Generations; Genetic Transcription; Goals; Healthcare; Hour; Image; Investigation; Label; Learning; Length; Machine Learning; Manuals; Medical; Methods; Names; Natural Language Processing; Occupations; Patients; Pattern; Physicians; Process; Quality of Care; Scientist; Source; System; Techniques; Technology; Testing; Time; User-Computer Interface; Work; burnout; clinical encounter; cognitive load; computer science; cost; data streams; deep learning; design; implicit bias; improved; learning strategy; multidisciplinary; novel; pediatrician; skills; supervised learning; unsupervised learning

Johnson, Kevin DP1 Details Project Name: Helping Doctors Doctor: Using AI to Automate Documentation and “De-Autonomate” Health Care Project Summary Clinical encounter documentation is one of the most time-consuming tasks of the ambulatory encounter, taking approximately two hours for every hour spent with patients. Clinical note lengths in the US are longer than those in other countries due to requirements to justify billing and complete quality care metrics. Not surprisingly, clinical encounter documentation has become a major source of clinician burnout over the past two decades. Although companies are selling technologies to transcribe what was discussed during the encounter, these costly solutions reproduce what is already suboptimal about how we create and use EHR information. There is a desperate need to reimagine the process of documenting as well as the content of documenting a clinical encounter. In this application I propose to develop a new generation of automated documentation algorithms—algorithms that can listen to the dialog between a patient and clinician, collect quantitative data about these observations, combine those with existing electronic health record (EHR) data and create relevant encounter summary information. These documentation algorithms will leverage the remarkable progress we have made in computer vision, natural language processing, machine learning to support image labeling, and other advances using EHR data. These novel computational approaches have yet to be explored as alternative approaches to summarizing medical data collected in real time. As a pediatrician and biomedical informatician who has acquired considerable expertise in real-world systems design, implementation and medical data analytics, this project leverages many of my skills, though it is a departure from my previous human-computer interface work. Rather, the goal of this project is to remove the burden of documentation from clinicians to the extent possible. To achieve this goal, I will work with a multidisciplinary group of collaborators, including computer scientists, technology engineers, and clinical domain experts. Specifically, I will: (1) collect and analyze exam room video and annotations of the encounters to identify salient characteristics of patients and their interaction with the clinician that led to specific diagnoses; (2) apply natural language processing, deep learning, and computer vision methods to learn and characterize patterns from vast streams of data using supervised and unsupervised learning methods. Combining these techniques to directly impact what is documented and how it is generated is a new area of investigation for me and an approach that promises to support innumerable other projects including identifying implicit bias in clinical encounters, enabling a new class of real-time decision making and improving the usefulness of encounter summaries.

约翰逊，凯文DP1详细信息 项目名称：帮助医生医生：使用AI自动化文档和“自动降低”健康 关心 项目摘要 临床遭遇文件是卧床遭遇中最耗时的任务之一， 每小时与患者一起度过大约两个小时。美国的临床音符长度更长 比其他国家 /地区的那些要求证明计费和完成质量护理指标的要求。不是 令人惊讶的是，临床遭遇文件已成为过去的主要临床倦怠来源 尽管公司正在出售技术来转录在此期间讨论的内容 相遇，这些代价高昂的解决方案重现了我们如何创建和使用EHR已经次优的内容 信息。迫切需要重新构想记录过程和内容 记录临床相遇。在此应用程序中，我建议开发新一代的自动化 文档算法 - 可以收听患者与临床之间对话的算法，收集 有关这些观察结果的定量数据，将这些数据与现有电子健康记录（EHR）数据相结合 并创建相关的相遇摘要信息。这些文档算法将利用 我们在计算机视觉，自然语言处理，机器学习中取得的显着进步 支持图像标签以及使用EHR数据的其他进步。这些新颖的计算方法尚未 作为总结实时收集的医疗数据的替代方法。作为儿科医生 和生物医学信息，他们在现实世界系统设计中获得了考虑的专业知识， 实施和医疗数据分析，该项目利用了我的许多技能，尽管这是一个离开 从我以前的人类计算机界面工作。相反，该项目的目的是消除燃烧 从临床医生到可能的范围。为了实现这一目标，我将与多学科合作 一组合作者，包括计算机科学家，技术工程师和临床领域专家。 具体来说，我将：（1）收集和分析检查室视频和相遇的注释以识别 患者的显着特征及其与导致特定诊断的临床相互作用； （2） 应用自然语言处理，深度学习和计算机视觉方法来学习和表征 使用监督和无监督的学习方法来自广泛数据流的模式。结合这些 直接影响所记载的内容以及如何生成的技术是一个新的投资领域 我和一种有望支持无数其他项目的方法，包括确定中隐性偏见 临床相遇，实现新的实时决策和提高的实用性 遇到摘要。