用药过程中自动化系统用药错误的类型和严重程度:系统综述

M. Mustafa, Najlaa Al-Qahtani, K. Yusuff
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引用次数: 0

摘要

自动化系统对于减少用药错误和提高患者安全至关重要。然而,它们的使用增加了与其他因素相关的药物错误:社会技术相互作用,自动化偏差,变通方法和覆盖。本研究进行了全面的系统评价,以确定在用药过程的各个阶段与使用自动化系统相关的用药错误的类型和严重程度。这为研究领域的全球知识提供了新的视角。检索了三个数据库,包括2000-2019年的英语观察和实验研究,重点是药物错误的类型和严重程度。开发了数据提取表,并使用hoy等工具评估质量。经过重复数据删除后,搜索得到860篇文章,其中13篇符合条件。8项研究偏倚风险较低(62%),5项研究偏倚风险中等(38%)。用药错误的类型和发生率分别为遗漏信息(4 ~ 61%)、剂量错误(4 ~ 30%)、用药错误(1 ~ 18%)、给药时间错误(3 ~ 18%)、频率错误(0.6% ~ 21%),发生在处方和给药阶段(62%)。使用的错误严重程度评估是NCC-MERP-index(46%), other(23%),或不进行(31%)。在处方和给药阶段,遗漏信息和不正确剂量是与自动化系统相关的最常见错误。然而,由于研究设计和评估标准的差异,错误的严重程度和分类尚无定论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Types and Severity of Medication-Errors with Automated Systems within Medication-Use Process: Systematic-Review
Automated systems have been crucial to reducing medication errors and improving patient safety. However, their use has increased medication-errors associated with other factors:socio-technical interactions, automation bias, workarounds, and overrides. This comprehensive systematic review was conducted to identify types and severity of medication-errors associated with the use of automated system in all stages of the medication use process. This provides new perspectives that contribute significantly to global knowledge in the research area. Three databases were searched to include English-language observational and experimental studies(from 2000-2019) focused on types and severity of medication errors. A data-extraction form was developed, and quality was assessed using Hoy-et-al tool. The search yielded 860 articles after deduplication and thirteen were eligible. The bias risk was low for eight studies(62%) and moderate for five(38%). The medication-error types, and prevalence were omitted information(4-61%), wrong dose(4-30%), incorrect medication(1-18%), incorrect administration time(3-18%), and incorrect frequency(0.6%-21%) and occurred in the prescribing(62%) and administration(69%) stage. The error severity assessment used was NCC-MERP-index(46%), other(23%), or not conducted(31%). Omitted information and incorrect dose were the most common errors associated with automated systems in the prescribing and administration stages. However, the error severity and classification was inconclusive due to differences in study design and assessment criteria.
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