Both authors contributed equally.
Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA.
AMIA Annu Symp Proc. 2021 Jan 25;2020:953-962. eCollection 2020.
High quality patient care through timely, precise and efficacious management depends not only on the clinical presentation of a patient, but the context of the care environment to which they present. Understanding and improving factors that affect streamlined workflow, such as provider or department busyness or experience, are essential to improving these care processes, but have been difficult to measure with traditional approaches and clinical data sources. In this exploratory data analysis, we aim to determine whether such contextual factors can be captured for important clinical processes by taking advantage of non-traditional data sources like EHR audit logs which passively track the electronic behavior of clinical teams. Our results illustrate the potential of defining multiple measures of contextual factors and their correlation with key care processes. We illustrate this using thrombolytic (tPA) treatment for ischemic stroke as an example process, but the measurement approaches can be generalized to multiple scenarios.
通过及时、准确和有效的管理提供高质量的患者护理,不仅取决于患者的临床表现,还取决于他们就诊的医疗环境背景。了解和改善影响流程简化的因素,如提供者或科室的忙碌程度或经验,对于改善这些护理流程至关重要,但传统方法和临床数据源很难对此进行衡量。在这项探索性数据分析中,我们旨在通过利用非传统数据来源(如电子健康记录审计日志)来确定是否可以为重要的临床流程捕获此类上下文因素,这些数据来源可以被动地跟踪临床团队的电子行为。我们的结果说明了定义多种上下文因素衡量指标及其与关键护理流程相关性的潜力。我们使用溶栓(tPA)治疗缺血性中风作为示例流程来说明这一点,但可以将这些测量方法推广到多种情况。