Collaborative Research: SCH: Machine Learning Driven User Interfaces for Information Gathering and Synthesis from Medical Records

临床医生必须搜索患者过去的医疗记录，以了解患者的病情，并达成个性化的诊断和治疗计划。然而，医疗保健中的用户界面很难导航，主要是对传统临床工作流程中的静态纸质表单进行数字化，这一范例导致了可用性低下和临床医生的倦怠。该项目汇集了机器学习和人机交互方面的专家，开发了一种新型的动态上下文用户界面，将医疗笔记记录界面从一个简单的记录设备转变为一个可以帮助临床医生快速查找和合成信息的工具。该项目的创新之处在于推进了人类-人工智能交互的基础，并通过新的方法推动了医疗保健人工智能的尖端技术，这些方法可以在正确的时间为临床医生自动检索、总结和展示相关信息。该项目的影响是在国家优先领域--卫生信息技术，因为它的目标是使电子健康记录现代化。由此产生的系统将有助于防止忽视细微的发现，防止患者被误诊，防止错过关键干预措施，最终导致发病率、死亡率和医疗保健总成本的降低。此外，它还将减轻文档负担并减轻医生的职业倦怠。以前开发患者信息的上下文显示的尝试是手动的、劳动密集型的过程，依赖于领域专业知识，并且既不能扩展、可维护，也不能为单个用户定制。自动化上下文显示具有挑战性，因为哪些信息高度相关取决于用户、患者和特定的临床上下文。由于缺乏带标签的训练数据，传统的机器学习方法是不可行的。该项目开发了新的用户界面，使大规模收集基于隐式使用情况的培训数据成为常规用户工作流程的一部分。具体地说，该项目开发了一种新颖的信息搜寻界面--“语义剪贴板”，临床医生将在阅读患者过去的医疗记录和写笔记时使用它。使用通过这个新界面收集的数据，研究人员将开发新的机器学习方法，以预测应该出现在这些根据临床场景和用户定制的上下文显示中的相关信息片段。通过这个项目和研究人员的学术教学，将培养新一代跨学科研究人员：了解人机交互、机器学习和临床医学的基本挑战的研究生，以及了解机器学习和在医疗保健中部署机器学习的微妙之处的医学研究员。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Clinicians have to search through a patient’s past medical records to contextualize the patient’s condition and reach a personalized diagnosis and treatment plan. However, user interfaces in healthcare are unwieldy to navigate and are largely a digitization of static paper forms from legacy clinical workflows, a paradigm that has contributed to poor usability and clinician burnout. This project brings together experts in machine learning and human-computer interaction to develop a novel dynamic contextual user interface that transforms the medical note-taking interface from a simple recording device to a tool that can help clinicians quickly find and synthesize information. The project’s novelties are in advancing the foundations of human-AI interaction and in advancing the state-of-the-art in artificial intelligence for health care with novel methods that autonomously retrieve, summarize, and surface relevant information for clinicians, at the right time. The project’s impacts are in an area of national priority, health IT, as it aims to modernize electronic health records. The resulting system will help prevent subtle findings from being overlooked, patients from being misdiagnosed, and critical interventions from being missed, ultimately resulting in a decrease in morbidity, mortality, and overall cost of health care. It will additionally decrease documentation burden and mitigate physician burnout. Prior attempts at developing contextual displays of patient information were manual, labor-intensive processes that relied on domain expertise and were neither scalable, maintainable, nor customized to individual users. Automating contextual displays is challenging because what information is relevant highly depends on the user, patient, and specific clinical context. Traditional machine learning approaches are infeasible because of the lack of labeled training data. This project develops new user interfaces that enable the large-scale collection of implicit usage-based training data as part of routine user workflows. Specifically, this project develops a novel information foraging interface, the ‘semantic clipboard’, which clinicians will use while reading patients’ past medical records and while writing notes. Using the data collected through this new interface, the investigators will develop new machine learning methodologies to predict the relevant pieces of information that should appear in these contextual displays, customized to the clinical scenario as well as the user. Through this project and the investigators’ academic teaching, a new generation of cross-disciplinary researchers will be educated: graduate students who understand the fundamental challenges of human computer interaction, machine learning, and clinical medicine, and medical fellows who understand machine learning and the subtleties of deploying machine learning in health care.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.