退伍军人事务部国家药品档案参考术语：一项跨机构内容覆盖研究。

VA National Drug File Reference Terminology: a cross-institutional content coverage study.

作者信息

Brown Steven H, Elkin Peter L, Rosenbloom S Trent, Husser Casey, Bauer Brent A, Lincoln Michael J, Carter John, Erlbaum Mark, Tuttle Mark S

机构信息

U.S. Department of Veterans Affairs, 1310 24th Avenue South, Nashville, TN 37212, USA.

出版信息

Stud Health Technol Inform. 2004;107(Pt 1):477-81.

PMID:15360858

Abstract

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 (SAVS) 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 SAVS engine properly mapped 773 of 810 phrases 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

摘要

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。我们详细阐述了美国退伍军人事务部国家药品档案参考术语（NDF-RT）表示梅奥诊所口述用药清单短语的能力。NDF-RT是一种基于描述逻辑的资源，旨在支持美国最大的医疗服务提供商之一的临床操作。

方法

从梅奥诊所的口述患者记录中提取用药清单短语。使用智能访问词汇服务器（SAVS）对NDF-RT进行算法映射，并展示给两位非退伍军人事务部的医生。医生们使用术语浏览器来确定算法映射的准确性以及NDF-RT的内容覆盖范围。

结果

300名患者的509份提取文档在用药清单中包含847个用药概念。NDF-RT覆盖了97.8%的概念。在NDF-RT未表示的18个短语中，10个是关于非处方药和食品补充剂的，5个是关于处方药的，3个缺少同义词。SAVS引擎正确映射了810个短语中的773个，总体敏感度（精确率）为95.4%，阳性预测值（召回率）为99.9%。