开发一个基于病例的系统用于全科医疗中的诊断分组。
Development of a case-based system for grouping diagnoses in general practice.
作者信息
Biermans Marion C J, de Bakker Dinny H, Verheij Robert A, Gravestein Jan V, van der Linden Michiel W, Robbé Pieter F de Vries
机构信息
Department of Medical Informatics, Radboud University Nijmegen Medical Centre, 152 MI, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands.
出版信息
Int J Med Inform. 2008 Jul;77(7):431-9. doi: 10.1016/j.ijmedinf.2007.08.002. Epub 2007 Sep 17.
INTRODUCTION
This article describes the development of EPICON; an application to group ICPC-coded diagnoses from electronic medical records in general practice into episodes of care. These episodes can be used to estimate prevalence and incidence rates.
METHODS
We used data from 89 practices that participated in the Dutch National Survey of General Practice. Additionally, we held interviews with seven experts, and studied documentation to establish the requirements of the application and to develop the design. We then performed a formative evaluation by assessing incorrectly grouped diagnoses.
RESULTS
EPICON is based on a combination of logical expressions, a decision table, and information extracted from individual cases by case-based reasoning. EPICON is able to group all diagnoses in the selected 89 practices, and groups 95% correctly.
CONCLUSION
The results cautiously indicate that EPICONs performance will probably be adequate for the purpose of estimating morbidity rates in general practice.
引言
本文介绍了EPICON的开发过程；这是一个用于将全科医疗电子病历中按国际初级保健分类（ICPC）编码的诊断归为照护事件的应用程序。这些事件可用于估计患病率和发病率。
方法
我们使用了来自参与荷兰全国全科医疗调查的89家诊所的数据。此外，我们采访了七位专家，并研究了相关文档，以确定该应用程序的要求并进行设计。然后，我们通过评估分组错误的诊断进行了形成性评价。
结果
EPICON基于逻辑表达式、决策表以及通过基于案例推理从个别病例中提取的信息的组合。EPICON能够对所选89家诊所中的所有诊断进行分组，且分组正确率达95%。
结论
结果谨慎表明，EPICON的性能可能足以用于估计全科医疗中的发病率。
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