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肿瘤学中的形似、音似药物。

Look-alike, sound-alike drugs in oncology.

作者信息

Kovacic Laurel, Chambers Carole

机构信息

University of Washington, Seattle, Washington, USA.

出版信息

J Oncol Pharm Pract. 2011 Jun;17(2):104-18. doi: 10.1177/1078155209354135. Epub 2010 Jan 29.

Abstract

BACKGROUND

Medication errors with oncology drugs can place cancer patients at risk for adverse events or death. Look-alike, sound-alike (LASA) drug names may increase the risk for errors. Published lists of LASA drug names are generally a result of voluntarily reported medication incidents. This study performed a proactive review of the oncology drug formulary from the Cancer Services of the Alberta Health Services for LASA drug pairs.

METHODS

The Levenshtein Distance and Bigram Similarity algorithms, same first and last letters, and Lexi-Comp(R) on-line alerts were used to review the outpatient oncology formulary to identify potential LASA generic drug name pairs.

RESULTS

indicate there are more potential LASA generic drug name pairs in the oncology formulary than are published in the literature. The risk detection methods used in this study identified unique and common LASA drug pairs. The Bigram Similarity algorithm identified 186 LASA drug pairs from 3320 possible pairs. The Levenshtein Distance algorithm, same first and last letters, and Lexi-Comp(R) methods identified 42, 75, and 38 LASA drug pairs, respectively. Five generic LASA drug pairs were identified in common by all four of the risk determination methods.

DISCUSSION

LASA drug pairs identified by three or four methods were considered to provide the highest risk for errors. A step-wise approach to risk reduction, dependent on the number of detection methods identifying a pair, is presented.

CONCLUSION

For specialty areas of practice, a proactive system of reviewing LASA drug name pairs may be warranted for increasing medication safety.

摘要

背景

肿瘤药物用药错误可能使癌症患者面临不良事件或死亡风险。外形相似、发音相似(LASA)的药物名称可能会增加错误风险。已公布的LASA药物名称列表通常是自愿报告的用药事件的结果。本研究对艾伯塔省卫生服务局癌症服务部的肿瘤药物处方集进行了主动审查,以查找LASA药物对。

方法

使用莱文斯坦距离算法、二元组相似度算法、首字母和尾字母相同的情况以及Lexi-Comp®在线警报来审查门诊肿瘤处方集,以识别潜在的LASA通用药物名称对。

结果

表明肿瘤处方集中潜在的LASA通用药物名称对比文献中公布的更多。本研究中使用的风险检测方法识别出了独特的和常见的LASA药物对。二元组相似度算法从3320个可能的药物对中识别出186个LASA药物对。莱文斯坦距离算法、首字母和尾字母相同的情况以及Lexi-Comp®方法分别识别出42个、75个和38个LASA药物对。所有四种风险判定方法共同识别出了5个通用LASA药物对。

讨论

通过三种或四种方法识别出的LASA药物对被认为错误风险最高。提出了一种根据识别出药物对的检测方法数量逐步降低风险的方法。

结论

对于专业实践领域,可能有必要建立一个主动审查LASA药物名称对的系统,以提高用药安全性。

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