SBIR Phase I: A tool to automate a narrative patient summary of the medical chart for outpatient physicians

SBIR 第一阶段：为门诊医生自动生成病历的叙述性患者摘要的工具

• 批准号: 2324507
• 负责人: Vince Hartman
• 金额: $ 27.5万
• 依托单位: ABSTRACTIVE HEALTH, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2324507&HistoricalAwards=false
• 关键词: SBIR; Phase; tool; automate; narrative

The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the development of a machine learning-enabled medical record summarization tool designed to provide a narrative summary that can aid physicians in patient care. On average, physicians spend just 3 minutes reviewing a patient’s medical record, and during this time they must interpret unstructured Electronic Health Records (EHR) that can make it difficult for physicians to identify information essential to patient care and diagnosis. By targeting the rich clinical data embedded in unstructured clinical notes, the proposed tool could provide clinically relevant information and a contextual understanding of a patient’s medical history. If successful, the proposed solution will reduce the data burden placed on doctors, mitigate the risk of missing valuable information that could affect patient diagnosis or lead to costly medical errors, and maximize downstream effects on patient outcomes. This Small Business Innovation Research (SBIR) Phase I project aims to leverage advances in natural language processing (NLP) to assist doctors by automating the process of electronic health record review. The underlying innovation is an extractive-abstractive pipeline that determines what content in the medical record is the most salient and should be summarized through a transformer (a machine learning model). This project aims to advance this summarization tool to more challenging use cases, primarily summarizing the outpatient record, a task made challenging by the large scope of the data, clinical redundancies, different data structures, and sources inherent to outpatient data, all of which need to be accounted for in model training and validation. Objectives include to 1) developing an outpatient summarization model and demonstrating the ability to produce summaries that semantically match reference text with a high level of fluency, 2) validating the utility of artificial intelligence (AI)-generated outpatient summaries to provide significant value to physicians, 3) evaluating the ability of AI-generated summaries that provide information relevant to future patient visit through ablation study, and 4) incorporating checks for bias in the existing model.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.

该小型企业创新研究 (SBIR) 第一阶段项目的更广泛影响/商业潜力是开发一种支持机器学习的医疗记录摘要工具，旨在提供可以帮助医生进行患者护理的叙述性摘要。只需 3 分钟即可查看患者的医疗记录，在此期间，他们必须解释非结构化电子健康记录 (EHR)，这可能使医生难以通过针对非结构化临床中嵌入的丰富临床数据来识别对患者护理和诊断至关重要的信息。笔记，所提出的工具可以提供相关信息以及对患者病史的背景理解。如果成功，所提出的解决方案将减轻医生的数据临床负担，降低丢失可能影响患者诊断或导致昂贵医疗费用的有价值信息的风险。该小型企业创新研究 (SBIR) 第一阶段项目旨在利用自然语言处理 (NLP) 的进步，通过自动化电子健康记录审核流程来帮助医生。提取-抽象管道决定什么病历中的内容是最重要的，应该通过转换器（机器学习模型）进行总结，该项目旨在将这种总结工具推进到更具挑战性的用例，主要是总结门诊记录，这是一项艰巨的任务。数据范围、临床冗余、不同的数据结构以及门诊数据固有的来源，所有这些都需要在模型训练和验证中加以考虑。目标包括 1) 开发门诊汇总模型并展示生成的能力。与参考文本在语义上高度流畅地匹配的摘要，2) 验证人工智能 (AI) 生成的门诊摘要的实用性，为医生提供重要价值，3) 评估人工智能生成的摘要提供相关信息的能力通过消融研究未来患者就诊，4) 在现有模型中纳入偏差检查。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。