Calvitti Alan, Hochheiser Harry, Ashfaq Shazia, Bell Kristin, Chen Yunan, El Kareh Robert, Gabuzda Mark T, Liu Lin, Mortensen Sara, Pandey Braj, Rick Steven, Street Richard L, Weibel Nadir, Weir Charlene, Agha Zia
Veterans Medical Research Foundation, 3350 La Jolla Village Dr, San Diego, CA 92161, United States.
Department of Biomedical Informatics and Intelligent Systems Program, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA 15206, United States.
J Biomed Inform. 2017 May;69:135-149. doi: 10.1016/j.jbi.2017.03.011. Epub 2017 Mar 18.
We describe methods for capturing and analyzing EHR use and clinical workflow of physicians during outpatient encounters and relating activity to physicians' self-reported workload. We collected temporally-resolved activity data including audio, video, EHR activity, and eye-gaze along with post-visit assessments of workload. These data are then analyzed through a combination of manual content analysis and computational techniques to temporally align streams, providing a range of process measures of EHR usage, clinical workflow, and physician-patient communication. Data was collected from primary care and specialty clinics at the Veterans Administration San Diego Healthcare System and UCSD Health, who use Electronic Health Record (EHR) platforms, CPRS and Epic, respectively. Grouping visit activity by physician, site, specialty, and patient status enables rank-ordering activity factors by their correlation to physicians' subjective work-load as captured by NASA Task Load Index survey. We developed a coding scheme that enabled us to compare timing studies between CPRS and Epic and extract patient and visit complexity profiles. We identified similar patterns of EHR use and navigation at the 2 sites despite differences in functions, user interfaces and consequent coded representations. Both sites displayed similar proportions of EHR function use and navigation, and distribution of visit length, proportion of time physicians attended to EHRs (gaze), and subjective work-load as measured by the task load survey. We found that visit activity was highly variable across individual physicians, and the observed activity metrics ranged widely as correlates to subjective workload. We discuss implications of our study for methodology, clinical workflow and EHR redesign.
我们描述了在门诊会诊期间捕获和分析医生电子健康记录（EHR）使用情况及临床工作流程，并将活动与医生自我报告的工作量相关联的方法。我们收集了具有时间分辨率的活动数据，包括音频、视频、EHR活动和眼动数据，以及会诊后的工作量评估。然后通过人工内容分析和计算技术相结合的方式对这些数据进行分析，以便在时间上对齐数据流，从而提供一系列关于EHR使用、临床工作流程和医患沟通的过程指标。数据分别从圣地亚哥退伍军人事务医疗保健系统和加州大学圣地亚哥分校健康中心的初级保健和专科诊所收集，这两个机构分别使用电子健康记录（EHR）平台CPRS和Epic。按医生、地点、专科和患者状态对会诊活动进行分组，能够根据与美国国家航空航天局任务负荷指数调查所捕获的医生主观工作量的相关性，对活动因素进行排序。我们开发了一种编码方案，使我们能够比较CPRS和Epic之间的时间研究，并提取患者和会诊复杂性概况。尽管在功能、用户界面以及由此产生的编码表示方面存在差异，但我们在这两个地点发现了类似的EHR使用和导航模式。两个地点在EHR功能使用和导航的比例、会诊时长分布、医生关注EHR的时间比例（注视）以及任务负荷调查所测量的主观工作量方面都显示出相似性。我们发现，不同医生之间的会诊活动差异很大，观察到的活动指标作为主观工作量的相关因素，范围也很广。我们讨论了我们的研究对方法学、临床工作流程和EHR重新设计的意义。
