Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, United States.
Mayo Clinic, Phoenix, AZ, United States.
J Biomed Inform. 2018 Mar;79:20-31. doi: 10.1016/j.jbi.2018.01.007. Epub 2018 Feb 2.
The analysis of clinical workflow offers many challenges, especially in settings characterized by rapid dynamic change. Typically, some combination of approaches drawn from ethnography and grounded theory-based qualitative methods are used to develop relevant metrics. Medical institutions have recently attempted to introduce technological interventions to develop quantifiable quality metrics to supplement existing purely qualitative analyses. These interventions range from automated location tracking to repositories of clinical data (e.g., electronics health record (EHR) data, medical equipment logs). Our goal in this paper is to present a cohesive framework that combines a set of analytic techniques that can potentially complement traditional human observations to derive a deeper understanding of clinical workflow and thereby to enhance the quality, safety, and efficiency of care offered in that environment. We present a series of theoretically-guided techniques to perform analysis and visualization of data developed using location tracking, with illustrations using the Emergency Department (ED) as an example. Our framework is divided into three modules: (i) transformation, (ii) analysis, and (iii) visualization. We describe the methods used in each of these modules, and provide a series of visualizations developed using location-tracking data collected at the Mayo Clinic ED (Phoenix, AZ). Our innovative analytics go beyond qualitative study, and includes user data collected from a relatively modern but increasingly ubiquitous technique of location tracking, with the goal of creating quantitative workflow metrics. Although we believe that the methods we have developed will generalize well to other settings, additional work will be required to demonstrate their broad utility beyond our single study environment.
临床工作流程分析面临诸多挑战，尤其是在快速动态变化的环境中。通常，会结合人种学和基于扎根理论的定性方法来开发相关指标。最近，医疗机构试图引入技术干预措施，以制定可量化的质量指标来补充现有的纯定性分析。这些干预措施的范围从自动位置跟踪到临床数据存储库（例如，电子健康记录 (EHR) 数据、医疗设备日志）。本文的目标是提出一个连贯的框架，该框架结合了一组分析技术，这些技术有可能补充传统的人工观察，从而更深入地了解临床工作流程，从而提高该环境下提供的护理质量、安全性和效率。我们提出了一系列理论指导的技术来对使用位置跟踪开发的数据进行分析和可视化，并用急诊科 (ED) 作为示例进行说明。我们的框架分为三个模块：（i）转换，（ii）分析，和（iii）可视化。我们描述了每个模块中使用的方法，并提供了使用在梅奥诊所 ED（凤凰城，AZ）收集的位置跟踪数据开发的一系列可视化。我们的创新分析超越了定性研究，包括从相对现代但越来越普及的位置跟踪技术收集的用户数据，旨在创建定量工作流程指标。尽管我们相信我们开发的方法将很好地推广到其他环境，但需要进一步的工作来证明它们除了我们的单一研究环境之外的广泛适用性。
