Mining tasks and task characteristics from electronic health record audit logs with unsupervised machine learning
Mining tasks and task characteristics from electronic health record audit logs with unsupervised machine learning
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DOI：
10.1093/jamia/ocaa338
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发表时间：
2021-02-12
影响因子：
6.4
通讯作者：
Chen, You
中科院分区：
文献类型：
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作者：
Chen, Bob;Alrifai, Wael;Chen, You
目的：临床医生在与电子健康记录（EHR）系统交互时的活动特征会影响在EHR中花费的时间和工作量。这项研究的目的是EHR活动的特点，并定义新的，数据驱动的metrics.Materials和Methods：我们利用无监督学习方法来学习EHR审计日志中的事件序列的任务。我们开发了表征独特事件和事件重复发生率的指标，并将其应用于将任务分类为4个复杂性配置文件。在这些特征之间，应用Mann-Whitney U检验来测量执行时间、事件类型和临床医生患病率的差异，或观察到执行这些任务的独特临床医生的数量。此外，我们应用过程挖掘框架与临床注释配对，以支持我们确定的任务样本的有效性。我们应用我们的方法来学习任务的护士在范德比尔特大学医学中心新生儿重症监护病房。结果：我们检查了EHR审计日志产生的33新生儿重症监护病房护士57 234次会议和81个任务。我们的研究结果表明，每个观察到的任务复杂性配置文件的性能时间显着差异。在不同复杂性的任务之间，临床医生患病率或查看和修改事件类型的频率没有显著差异。我们提出了一个样本的专家审查，注释的任务工作流支持解释其临床meaningfulness.Conclusions：使用的审计日志提供了一个机会，以帮助医院进一步调查临床活动，优化电子病历工作流程。
Objective: The characteristics of clinician activities while interacting with electronic health record (EHR) systems can influence the time spent in EHRs and workload. This study aims to characterize EHR activities as tasks and define novel, data-driven metrics.Materials and Methods: We leveraged unsupervised learning approaches to learn tasks from sequences of events in EHR audit logs. We developed metrics characterizing the prevalence of unique events and event repetition and applied them to categorize tasks into 4 complexity profiles. Between these profiles, Mann-Whitney U tests were applied to measure the differences in performance time, event type, and clinician prevalence, or the number of unique clinicians who were observed performing these tasks. In addition, we apply process mining frameworks paired with clinical annotations to support the validity of a sample of our identified tasks. We apply our approaches to learn tasks performed by nurses in the Vanderbilt University Medical Center neonatal intensive care unit.Results: We examined EHR audit logs generated by 33 neonatal intensive care unit nurses resulting in 57 234 sessions and 81 tasks. Our results indicated significant differences in performance time for each observed task complexity profile. There were no significant differences in clinician prevalence or in the frequency of viewing and modifying event types between tasks of different complexities. We presented a sample of expert-reviewed, annotated task workflows supporting the interpretation of their clinical meaningfulness.Conclusions: The use of the audit log provides an opportunity to assist hospitals in further investigating clinician activities to optimize EHR workflows.