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Monitoring quality requires knowing similarity: the NICLTS experience.

机译:监视质量需要了解相似之处:NICLTS经验。

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摘要

Laboratory tests can appear similar from the test names but may be vastly different in the way a result is achieved. Currently, for example, cervical cancer evaluation is moving from the traditional Papanicolaou smear to new smear preparation technologies and testing for human papillomavirus. Monitoring the quality of these three tests, and of all tests, requires that computers "understand" how these tests are similar and different. The National Inventory of Clinical Laboratory Testing Services (NICLTS) found that the approximately 20,000 most commonly performed tests used combinations of 635 analytes and 1,699 methods. These analytes and methods provide the base data for a semantic model that makes the requisite similarities and differences explicit. The semantic relationships, e.g. the method principle enabling a test and the nature of the substance tested, were evaluated against empirically derived, uni-dimensional relations. The resulting multi-dimensional semantic model expands our ability to monitor the quality of laboratory testing in the face of rapid change. Use of common terminology tools and representations enable the creation, expansion and reuse of this model beyond the needs of NICLTS.
机译:实验室测试在测试名称上可能看起来相似,但在获得结果的方式上可能有很大的不同。例如,目前,宫颈癌评估正在从传统的Papanicolaou涂片转向新的涂片制备技术和人类乳头瘤病毒测试。监视这三个测试以及所有测试的质量,要求计算机“了解”这些测试的相似性和差异性。国家临床实验室检测服务目录(NICLTS)发现,大约20,000个最常执行的测试使用了635种分析物和1,699种方法的组合。这些分析物和方法为语义模型提供了基础数据,该语义模型使必要的相似性和差异变得很明显。语义关系,例如根据经验得出的一维关系评估了实现测试的方法原理和所测试物质的性质。由此产生的多维语义模型扩展了我们面对快速变化时监控实验室测试质量的能力。使用通用术语工具和表示形式可以创建,扩展和重用超出NICLTS需求的该模型。

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