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.

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是开发一个支持机器学习的医疗记录摘要工具，旨在提供一个可以帮助医生进行患者护理的叙述性摘要。平均而言，医生只需花3分钟时间查看患者的病历，在此期间，他们必须解释非结构化的电子健康记录（EHR），这可能使医生难以识别对患者护理和诊断至关重要的信息。通过针对嵌入在非结构化临床笔记中的丰富临床数据，所提出的工具可以提供临床相关信息和对患者病史的上下文理解。如果成功，拟议的解决方案将减少医生的数据负担，降低可能影响患者诊断或导致代价高昂的医疗错误的有价值信息缺失的风险，并最大限度地提高对患者结果的下游影响。这个小型企业创新研究（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查过程来帮助医生。底层创新是一个提取-抽象管道，它确定医疗记录中的哪些内容是最突出的，应该通过Transformer（一种机器学习模型）进行总结。该项目旨在将此汇总工具推进到更具挑战性的用例中，主要是汇总门诊记录，这是一项具有挑战性的任务，因为数据范围大，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考虑。目标包括：1）开发一个门诊总结模型，并证明能够生成在语义上与参考文本相匹配且流畅度较高的总结，2）验证人工智能（AI）生成的门诊总结的效用，为医生提供重要价值，3）评价AI生成的总结的能力，通过消融研究提供与未来患者访视相关的信息，以及4）在现有模型中加入对偏差的检查。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。