Abraham Joanna, Galanter William L, Touchette Daniel, Xia Yinglin, Holzer Katherine J, Leung Vania, Kannampallil Thomas
Department of Anesthesiology, Washington University School of Medicine in St. Louis,St. Louis, Missouri, USA.
Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.
J Am Med Inform Assoc. 2021 Jan 15;28(1):86-94. doi: 10.1093/jamia/ocaa264.
We utilized a computerized order entry system-integrated function referred to as "void" to identify erroneous orders (ie, a "void" order). Using voided orders, we aimed to (1) identify the nature and characteristics of medication ordering errors, (2) investigate the risk factors associated with medication ordering errors, and (3) explore potential strategies to mitigate these risk factors.
We collected data on voided orders using clinician interviews and surveys within 24 hours of the voided order and using chart reviews. Interviews were informed by the human factors-based SEIPS (Systems Engineering Initiative for Patient Safety) model to characterize the work systems-based risk factors contributing to ordering errors; chart reviews were used to establish whether a voided order was a true medication ordering error and ascertain its impact on patient safety.
During the 16-month study period (August 25, 2017, to December 31, 2018), 1074 medication orders were voided; 842 voided orders were true medication errors (positive predictive value = 78.3 ± 1.2%). A total of 22% (n = 190) of the medication ordering errors reached the patient, with at least a single administration, without causing patient harm. Interviews were conducted on 355 voided orders (33% response). Errors were not uniquely associated with a single risk factor, but the causal contributors of medication ordering errors were multifactorial, arising from a combination of technological-, cognitive-, environmental-, social-, and organizational-level factors.
The void function offers a practical, standardized method to create a rich database of medication ordering errors. We highlight implications for utilizing the void function for future research, practice and learning opportunities.
我们利用一种称为“作废”的计算机化医嘱录入系统集成功能来识别错误医嘱(即“作废”医嘱)。通过使用作废医嘱,我们旨在:(1)确定用药医嘱错误的性质和特征;(2)调查与用药医嘱错误相关的风险因素;(3)探索减轻这些风险因素的潜在策略。
我们通过在作废医嘱后24小时内对临床医生进行访谈和调查以及查阅病历,收集有关作废医嘱的数据。访谈采用基于人为因素的SEIPS(患者安全系统工程倡议)模型,以描述导致医嘱错误的基于工作系统的风险因素;病历审查用于确定作废医嘱是否为真正的用药医嘱错误,并确定其对患者安全的影响。
在16个月的研究期间(2017年8月25日至2018年12月31日),有1074条用药医嘱被作废;842条作废医嘱为真正的用药错误(阳性预测值 = 78.3 ± 1.2%)。共有22%(n = 190)的用药医嘱错误在至少进行了一次给药的情况下传递给了患者,但未对患者造成伤害。对355条作废医嘱(回复率33%)进行了访谈。错误并非仅与单一风险因素相关,用药医嘱错误的因果因素是多方面的,源于技术、认知、环境、社会和组织层面因素的综合作用。
作废功能提供了一种实用、标准化的方法来创建丰富的用药医嘱错误数据库。我们强调了利用作废功能进行未来研究、实践和学习机会的意义。