Bioinformatics Research Group, University of Applied Sciences Upper Austria, Hagenberg, Austria.
Smile Digital Health, Toronto, Canada.
Stud Health Technol Inform. 2023 May 2;301:12-17. doi: 10.3233/SHTI230004.
Current monitoring and evaluation methods challenge the healthcare system. Specifically for the use case of immunization coverage calculation, person-level data retrieval is required instead of inaccurate aggregation methods. The Clinical Quality Language (CQL) by HL7®, has the potential to overcome current challenges by offering an automated generation of quality reports on top of an HL7® FHIR® repository.
This paper provides a method to author and evaluate an electronic health quality measure as demonstrated by a proof-of-concept on immunization coverage calculation.
Five artifact types were identified to transform unstructured input into CQL, to define the terminology, to create test data, and to evaluate the new quality measures.
CQL logic and FHIR® test data were created and evaluated by using the different approaches of manual evaluation, unit testing in the HAPI FHIR project, as well as showcasing the functionality with a developed user interface for immunization coverage analysis.
Simple, powerful, and transparent evaluations on a small population can be achieved with existing open-source tools, by applying CQL logic to FHIR®.
当前的监测和评估方法对医疗保健系统提出了挑战。特别是在免疫接种覆盖率计算的用例中，需要进行人员层面的数据检索，而不是使用不准确的汇总方法。HL7® 的临床质量语言（CQL）有可能通过在 HL7® FHIR® 存储库之上提供自动生成的质量报告来克服当前的挑战。
本文提供了一种方法，通过一个免疫接种覆盖率计算的概念验证，来编写和评估电子健康质量测量。
确定了五种类型的工件，将非结构化输入转换为 CQL，定义术语，创建测试数据，并评估新的质量测量。
使用手动评估、HAPI FHIR 项目中的单元测试以及使用开发的免疫接种覆盖率分析用户界面展示功能等不同方法，创建和评估了 CQL 逻辑和 FHIR®测试数据。
通过将 CQL 逻辑应用于 FHIR®，使用现有的开源工具可以对小人群进行简单、强大和透明的评估。
