Department of Clinical Pharmacology and Toxicology, St Vincent's Hospital, University of New South Wales, Sydney, New South Wales, Australia.
St Vincent's Clinical School, UNSW Medicine, University of New South Wales, Sydney, New South Wales, Australia.
Appl Clin Inform. 2018 Oct;9(4):849-855. doi: 10.1055/s-0038-1676039. Epub 2018 Nov 28.
Drug-drug interaction (DDI) alerts are often implemented in the hospital computerized provider order entry (CPOE) systems with limited evaluation. This increases the risk of prescribers experiencing too many irrelevant alerts, resulting in alert fatigue. In this study, we aimed to evaluate clinical relevance of alerts prior to implementation in CPOE using two common approaches: compendia and expert panel review.
After generating a list of hypothetical DDI alerts, that is, alerts that would have been triggered if DDI alerts were operational in the CPOE, we calculated the agreement between multiple drug interaction compendia with regards to the severity of these alerts. A subset of DDI alerts ( = 13), with associated patient information, were presented to an expert panel to reach a consensus on whether each alert should be included in the CPOE.
There was poor agreement between compendia in their classifications of DDI severity (Krippendorff's α: 0.03; 95% confidence interval: -0.07 to 0.14). Only 10% of DDI alerts were classed as severe by all compendia. On the other hand, the panel reached consensus on 12 of the 13 alerts that were presented to them regarding whether they should be included in the CPOE.
Using an expert panel and allowing them to discuss their views openly likely resulted in high agreement on what alerts should be included in a CPOE system. Presenting alerts in the context of patient cases allowed panelists to identify the conditions under which alerts were clinically relevant. The poor agreement between compendia suggests that this methodology may not be ideal for the evaluation of DDI alerts. Performing preimplementation review of DDI alerts before they are enabled provides an opportunity to minimize the risk of alert fatigue before prescribers are exposed to false-positive alerts.
药物-药物相互作用（DDI）警报通常在医院计算机化医嘱输入（CPOE）系统中实施，但评估有限。这增加了医嘱者遇到太多不相关警报的风险，导致警报疲劳。在这项研究中，我们旨在使用两种常见方法（药物相互作用手册和专家小组审查）在 CPOE 中实施之前评估警报的临床相关性。
在生成假设的 DDI 警报列表（即如果 CPOE 中启用了 DDI 警报，这些警报将被触发）之后，我们计算了多个药物相互作用手册对这些警报严重程度的分类之间的一致性。一组 DDI 警报（ = 13），以及相关的患者信息，被提交给一个专家小组，以就每个警报是否应包含在 CPOE 中达成共识。
手册在 DDI 严重程度的分类上存在较差的一致性（Krippendorff 的 α：0.03；95%置信区间：-0.07 至 0.14）。只有 10%的 DDI 警报被所有手册归类为严重。另一方面，小组就向他们提出的 13 个警报中的 12 个达成了共识，即它们是否应包含在 CPOE 中。
使用专家小组并允许他们公开讨论他们的观点，可能会就应该包含在 CPOE 系统中的警报达成高度一致。在患者病例的背景下呈现警报，使小组成员能够确定在哪些情况下警报具有临床相关性。手册之间的一致性较差表明，这种方法可能不适合评估 DDI 警报。在启用 DDI 警报之前对其进行预实施审查，为在医嘱者接触到假阳性警报之前降低警报疲劳的风险提供了机会。
