Nimptsch Ulrike, Spoden Melissa, Mansky Thomas
Fachgebiet Strukturentwicklung und Qualitätsmanagement im Gesundheitswesen, Technische Universität Berlin, Berlin.
Fachgebiet Management im Gesundheitswesen, Technische Universität Berlin, Berlin.
Gesundheitswesen. 2020 Mar;82(S 01):S29-S40. doi: 10.1055/a-0977-3332. Epub 2019 Oct 7.
In Germany, the Diagnosis-Related Group Statistics (DRG Statistics) supply full coverage of inpatient episodes in acute care hospitals. The Research Data Centres of the Federal Statistical Office and the Statistical Offices of the Federal States provide the microdata of the DRG Statistics, namely hospital discharge files of each inpatient case, for scientific research. Hospital discharge data are generated for administrative purposes. As well as other data sources, they have specific features and characteristics, which should be considered in planning and designing research studies. A key challenge is the appropriate and sophisticated operationalization of units of analysis, targets variables, and other study variables. The methodological approach should consider, among other factors, differing coding behaviour between hospitals in order to minimize the risk of bias. This contribution shows by practical examples what should be incorporated in variable definition to ensure that the risk of bias by coding behaviour or other factors is minimized to the greatest possible degree. First of all, the features and characteristics of the German hospital discharge data are outlined. Based on the authors' experiences, basic steps and challenges in observational health services research studies are described. Examples are illustrated by our own calculations, derived from previous studies based on the microdata of the DRG Statistics. The reliability and validity of analyses based on hospital discharge data are crucially dependent on the appropriateness of variable definition. To minimize the risk of bias and misinterpretation, extensive preliminary considerations are required which involve clinical aspects, as well as the context of data collection and technical classification opportunities. Hopefully, there will be greater acceptance of research based on hospital discharge data, so that these valuable data will be used more frequently for research purposes in the future.
在德国，诊断相关分组统计（DRG统计）涵盖了急性护理医院的全部住院病例。联邦统计局和联邦各州统计局的研究数据中心提供DRG统计的微观数据，即每个住院病例的医院出院文件，用于科学研究。医院出院数据是为行政目的而生成的。与其他数据源一样，它们具有特定的特征，在规划和设计研究时应予以考虑。一个关键挑战是对分析单位、目标变量和其他研究变量进行适当且精细的操作化。方法学方法应考虑诸多因素，其中包括医院之间不同的编码行为，以尽量减少偏差风险。本文通过实际例子展示了在变量定义中应纳入哪些内容，以确保将编码行为或其他因素导致的偏差风险降至最低程度。首先，概述了德国医院出院数据的特征。基于作者的经验，描述了观察性卫生服务研究中的基本步骤和挑战。通过我们自己的计算举例说明，这些计算源自先前基于DRG统计微观数据的研究。基于医院出院数据的分析的可靠性和有效性关键取决于变量定义的适当性。为了尽量减少偏差和误解的风险，需要进行广泛的前期考虑，这涉及临床方面以及数据收集背景和技术分类机会。希望基于医院出院数据的研究能得到更多认可，以便这些宝贵的数据在未来能更频繁地用于研究目的。
