Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes
Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes
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DOI：
10.48550/arxiv.2308.08494
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Sharon Jiang;Zejiang Shen;Monica Agrawal;Barbara Lam;N. Kurtzman;S. Horng;David R Karger;D. Sontag
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文献类型：
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作者：
Sharon Jiang;Zejiang Shen;Monica Agrawal;Barbara Lam;N. Kurtzman;S. Horng;David R Karger;D. Sontag
临床医生花费大量时间筛选患者笔记并在电子健康记录（EHRs）中进行记录，这是导致临床医生职业倦怠的主要原因。通过在记录过程中主动和动态地检索相关笔记，我们可以减少查找相关患者病史所需的工作量。在这项工作中，我们概念化了EHR审计日志在机器学习中的使用，作为特定临床环境中特定时间点笔记相关性监督的来源。我们的评估侧重于急诊科的动态检索，这是一个具有独特的信息检索和笔记撰写模式的高敏锐度环境。我们表明，我们的方法可以达到0.963的AUC，用于预测哪些音符将在单个音符书写会话中被读取。我们还与几位临床医生进行了用户研究，发现我们的框架可以帮助临床医生更有效地检索相关信息。证明我们的框架和方法可以在这种苛刻的环境中表现良好，这是一个有希望的概念证明，它们将转化为其他临床环境和数据模式（例如，实验室，药物，成像）。
The large amount of time clinicians spend sifting through patient notes and documenting in electronic health records (EHRs) is a leading cause of clinician burnout. By proactively and dynamically retrieving relevant notes during the documentation process, we can reduce the effort required to find relevant patient history. In this work, we conceptualize the use of EHR audit logs for machine learning as a source of supervision of note relevance in a specific clinical context, at a particular point in time. Our evaluation focuses on the dynamic retrieval in the emergency department, a high acuity setting with unique patterns of information retrieval and note writing. We show that our methods can achieve an AUC of 0.963 for predicting which notes will be read in an individual note writing session. We additionally conduct a user study with several clinicians and find that our framework can help clinicians retrieve relevant information more efficiently. Demonstrating that our framework and methods can perform well in this demanding setting is a promising proof of concept that they will translate to other clinical settings and data modalities (e.g., labs, medications, imaging).