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VA National Drug File Reference Terminology: A Cross-Institutional Content Coverage Study

机译:VA国家药物文件参考术语:跨机构内容覆盖研究

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Background: Content coverage studies provide valuable information to potential users of terminologies. We detail the VA National Drug File Reference Terminology's (NDF-RT) ability to represent dictated medication list phrases from the Mayo Clinic. NDF-RT is a description logic-based resource created to support clinical operations at one of the largest healthcare providers in the US. Methods: Medication list phrases were extracted from dictated patient notes from the Mayo Clinic. Algorithmic mappings to NDF-RT using the SmartAccess Vocabulary Server (SA VS) were presented to two non-VA physicians. The physicians used a terminology browser to determine the accuracy of the algorithmic mapping and the content coverage of NDF-RT. Results: The 509 extracted documents on 300 patients contained 847 medication concepts in medication lists. NDF-RT covered 97.8% of concepts. Of the 18 phrases that NDF-RT did not represent, 10 were for OTC's and food supplements, 5 were for prescription medications, and 3 were missing synonyms. The SA VS engine properly mapped 773 of 810phrases with an overall sensitivity (precision) was 95.4% and positive predictive value (recall) of 99.9%.. Conclusions: This study demonstrates that NDF-RT has more general utility than its initial design parameters dictated.
机译:背景:内容覆盖研究为术语的潜在用户提供了有价值的信息。我们详细介绍了VA国家药物文件参考术语(NDF-RT)能力代表Mayo诊所的决定药物清单短语。 NDF-RT是一种基于逻辑的资源,以支持美国最大的医疗保健提供商之一的临床操作。方法:从Mayo诊所的决定患者笔记中提取药物清单短语。使用SmartAccess词汇服务器(SA VS)向NDF-RT进行算法映射被呈现给两个非VA医生。医生使用术语浏览器来确定算法映射的准确性和NDF-RT的内容覆盖。结果:300例患者的509份提取的文件含有847种药物清单中的药物概念。 NDF-RT涵盖了97.8%的概念。在NDF-RT没有代表的18个短语的中,10对于OTC和食品补充剂,5个用于处方药,3名缺少同义词。 SA VS发动机正确地映射了773的810phrass,整体敏感性(精度)为95.4%,阳性预测值(召回)为99.9%。结论:本研究表明NDF-RT具有比其初始设计参数更通用的效用。

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