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).

翻译：临床医生在查阅患者笔记和记录电子健康记录（EHR）上花费的大量时间，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且动态地检索相关笔记，我们可以减少寻找相关患者病史所需的工作量。在这项工作中，我们将EHR审计日志用于机器学习的概念化，将其作为在特定临床情境和特定时间点监督笔记相关性的来源。我们的评估聚焦于急诊科的动态检索——一个具有独特信息检索和笔记编写模式的高敏锐度环境。我们证明，我们的方法在预测单个笔记编写会话中将读取哪些笔记时，AUC可达0.963。此外，我们与数位临床医生进行了用户研究，发现我们的框架能帮助临床医生更高效地检索相关信息。证明我们的框架和方法在此高要求环境中表现良好，是一个有前景的概念验证，表明它们将能推广至其他临床情境和数据模态（例如实验室检查、药物、影像学）。