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迈向更好的知识共享:评估HL7参考信息模型以支持医学逻辑模块查询。

Towards improved knowledge sharing: assessment of the HL7 Reference Information Model to support medical logic module queries.

作者信息

Jenders R A, Sujansky W, Broverman C A, Chadwick M

机构信息

Department of Medical Informatics, Columbia University, USA.

出版信息

Proc AMIA Annu Fall Symp. 1997:308-12.

Abstract

Because clinical databases vary in structure, access methods and vocabulary used to represent data, the Arden Syntax does not define a standard model for querying databases. Consequently, database queries are encoded in ad hoc ways and enclosed in "curly braces" in Medical Logic Modules (MLMs). However, the nonstandard representation of queries impairs sharing of MLMs, an impediment that has come to be known as the "curly braces problem." As a first step in solving this problem, we evaluated the proposed HL7 Reference Information Model (RIM) as a foundation for a standard query model for the Arden Syntax. Specifically, we analyzed the MLM knowledge base at the Columbia-Presbyterian Medical Center and compared the queries in these MLMs to the RIM. We studied 488 queries in 104 MLMs, identifying 674 total query data elements. Laboratory tests accounted for 45.8% of these elements, while demographic and ADT data accounted for 37.6%. Pharmacy orders accounted for 10.5%, medical problems for 4.3% and MLM output messages for 1.6%. We found that the RIM encompasses all but those data elements signifying MLM output (1.6% of the total). We conclude that the majority of queries in the CPMC knowledge base access a relatively small set of data elements and that the RIM encompasses these elements. We propose extensions of this analysis to continue construction of an Arden query model capable of solving the "curly braces problem."

摘要

由于临床数据库在结构、访问方法以及用于表示数据的词汇方面存在差异,Arden语法并未定义用于查询数据库的标准模型。因此,数据库查询以临时方式进行编码,并在医学逻辑模块(MLM)中用“花括号”括起来。然而,查询的非标准表示形式妨碍了MLM的共享,这一障碍后来被称为“花括号问题”。作为解决此问题的第一步,我们评估了提议的HL7参考信息模型(RIM),将其作为Arden语法标准查询模型的基础。具体而言,我们分析了哥伦比亚长老会医学中心的MLM知识库,并将这些MLM中的查询与RIM进行了比较。我们研究了104个MLM中的488个查询,共识别出674个查询数据元素。实验室检查占这些元素的45.8%,而人口统计学和ADT数据占37.6%。药房订单占10.5%,医疗问题占4.3%,MLM输出消息占1.6%。我们发现,RIM涵盖了除表示MLM输出的那些数据元素之外的所有元素(占总数的1.6%)。我们得出结论,CPMC知识库中的大多数查询访问的是相对较少的一组数据元素,并且RIM涵盖了这些元素。我们建议扩展此分析,以继续构建能够解决“花括号问题”的Arden查询模型。

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