King Andrew J, Cooper Gregory F, Hochheiser Harry, Clermont Gilles, Hauskrecht Milos, Visweswaran Shyam
University of Pittsburgh, Pittsburgh, PA, USA.
AMIA Annu Symp Proc. 2018 Dec 5;2018:673-682. eCollection 2018.
Poor electronic medical record (EMR) usability is detrimental to both clinicians and patients. A better EMR would provide concise, context sensitive patient data, but doing so entails the difficult task of knowing which data are relevant. To determine the relevance of patient data in different contexts, we collect and model the information seeking behavior of clinicians using a learning EMR (LEMR) system. Sufficient data were collected to train predictive models for 80 different targets (e.g., glucose level, heparin administration) and 27 of them had AUROC values of greater than 0.7. These results are encouraging considering the high variation in information seeking behavior (intraclass correlation 0.40). We plan to apply these models to a new set of patient cases and adapt the LEMR interface to highlight relevant patient data, and thus provide concise, context sensitive data.
电子病历（EMR）可用性不佳对临床医生和患者都有害。更好的电子病历会提供简洁、上下文相关的患者数据，但要做到这一点需要了解哪些数据相关这一艰巨任务。为了确定不同背景下患者数据的相关性，我们使用学习型电子病历（LEMR）系统收集并模拟临床医生的信息检索行为。收集了足够的数据来训练针对80个不同目标（如血糖水平、肝素给药）的预测模型，其中27个模型的曲线下面积（AUROC）值大于0.7。考虑到信息检索行为的高度变异性（组内相关系数为0.40），这些结果令人鼓舞。我们计划将这些模型应用于一组新的患者病例，并调整LEMR界面以突出显示相关的患者数据，从而提供简洁、上下文相关的数据。
