School of Industrial Engineering, College of Engineering, Purdue University, West Lafayette, Indiana, United States.
Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, Indiana, United States.
Appl Clin Inform. 2022 Aug;13(4):891-900. doi: 10.1055/s-0042-1756428. Epub 2022 Sep 21.
Infusion start time, completion time, and interruptions are the key data points needed in both area under the concentration-time curve (AUC)- and trough-based vancomycin therapeutic drug monitoring (TDM). However, little is known about the accuracy of documented times of drug infusions compared with automated recorded events in the infusion pump system. A traditional approach of direct observations of infusion practice is resource intensive and impractical to scale. We need a new methodology to leverage the infusion pump event logs to understand the prevalence of timestamp discrepancies as documented in the electronic health records (EHRs).
We aimed to analyze timestamp discrepancies between EHR documentation (the information used for clinical decision making) and pump event logs (actual administration process) for vancomycin treatment as it may lead to suboptimal data used for therapeutic decisions.
We used process mining to study the conformance between pump event logs and EHR data for a single hospital in the United States from July to December 2016. An algorithm was developed to link records belonging to the same infusions. We analyzed discrepancies in infusion start time, completion time, and interruptions.
Of the 1,858 infusions, 19.1% had infusion start time discrepancy more than ± 10 minutes. Of the 487 infusion interruptions, 2.5% lasted for more than 20 minutes before the infusion resumed. 24.2% (312 of 1,287) of 1-hour infusions and 32% (114 of 359) of 2-hour infusions had over 10-minute completion time discrepancy. We believe those discrepancies are inherent part of the current EHR documentation process commonly found in hospitals, not unique to the care facility under study.
We demonstrated pump event logs and EHR data can be utilized to study time discrepancies in infusion administration at scale. Such discrepancy should be further investigated at different hospitals to address the prevalence of the problem and improvement effort.
在基于浓度-时间曲线（AUC）和谷值的万古霉素治疗药物监测（TDM）中，输注开始时间、完成时间和中断时间都是关键数据点。然而，与输注泵系统中自动记录的事件相比，记录的药物输注时间的准确性知之甚少。传统的直接观察输注实践的方法需要大量资源，并且不切实际地难以扩展。我们需要一种新的方法来利用输注泵事件日志来了解电子病历（用于临床决策的信息）中记录的时间戳差异的普遍性。
我们旨在分析万古霉素治疗中电子病历记录（用于临床决策的信息）与泵事件日志（实际给药过程）之间的时间戳差异，因为这可能导致治疗决策中使用的数据不理想。
我们使用流程挖掘技术，从 2016 年 7 月至 12 月，在美国的一家医院研究泵事件日志和电子病历数据之间的一致性。开发了一种算法来链接属于同一输注的记录。我们分析了输注开始时间、完成时间和中断时间的差异。
在 1858 次输注中，有 19.1%的输注开始时间差异超过±10 分钟。在 487 次输注中断中，有 2.5%的中断持续时间超过 20 分钟，然后才恢复输注。在 1 小时的输注中，有 24.2%（312/1287）和在 2 小时的输注中，有 32%（114/359）的输注完成时间差异超过 10 分钟。我们认为这些差异是当前电子病历记录过程中固有的一部分，而不是研究中护理机构所特有的。
我们证明可以利用输注泵事件日志和电子病历数据来大规模研究输注给药的时间差异。应在不同的医院进一步研究这种差异，以解决问题的普遍性和改进工作。
