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

约翰逊，凯文DP 1详情 项目名称：帮助医生医生：使用AI自动化文档和“去自主化”健康 护理 项目摘要 临床就诊记录是门诊就诊中最耗时的任务之一， 每花一个小时在病人身上大约花两个小时。美国的临床记录长度更长 由于需要证明计费和完整的质量护理指标，不 令人惊讶的是，在过去，临床诊疗记录已经成为临床医生倦怠的主要来源 二十年尽管公司正在出售技术，以转录会议期间讨论的内容， 这些昂贵的解决方案重现了我们如何创建和使用EHR的次优方案 信息.迫切需要重新设想记录的过程以及记录的内容。 记录一次临床遭遇在这个应用程序中，我建议开发新一代的自动化 文档算法-可以收听患者和临床医生之间的对话，收集 关于这些观察结果的定量数据，联合收割机将这些数据与现有的电子健康记录（EHR）数据相结合 并创建相关的遭遇摘要信息。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， 支持图像标记，以及使用EHR数据的其他进步。这些新的计算方法还没有 作为总结真实的时间收集的医疗数据的替代方法进行探索。作为一名儿科医生 和生物医学信息学家谁已经获得了相当多的专业知识，在现实世界的系统设计， 实施和医疗数据分析，这个项目利用了我的许多技能，虽然它是一个出发点， 从我以前的人机界面工作中。相反，该项目的目标是消除 尽可能从临床医生处获取文件。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）收集和分析考场录像和注释的遭遇，以确定 患者的显著特征及其与临床医生的互动导致特定诊断;（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 使用监督和无监督学习方法从大量数据流中提取模式。组合这些 直接影响记录内容及其生成方式的技术是一个新的研究领域， 我和一种承诺支持无数其他项目的方法，包括识别 临床遭遇，使一类新的实时决策和提高的有用性， 遭遇摘要

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.