National surveys of the healthcare system in the United States were conducted to characterize the structure of healthcare system and investigate the impact of evidence-based innovations in healthcare systems on healthcare services. Administrative data is additionally available to researchers raising the question of whether inferences about healthcare organizations based on the survey data can be enhanced by incorporating information from auxiliary data. Administrative data can provide information for dealing with under-coverage-bias and non-response in surveys and for capturing more sub-populations. In this study, we focus on the use of administrative claims data to improve estimates about means of survey items for the finite population. Auxiliary information from the claims data is incorporated using multiple imputation to impute values of non-responding or non-surveyed organizations. We derive multiple versions of imputation strategy, and the logical development of methodology is compared to two incumbent approaches: a na\"ive analysis that ignores the sampling probabilities and a traditional survey analysis weighting by the inverses of the sampling probabilities. , and illustrate the methods using data from The National Survey of Healthcare Organizations and Systems and The Centers for Medicare & Medicaid Services Medicare claims data to make inferences about relationships of characteristics of healthcare organizations and healthcare services they provide.

翻译：美国医疗体系全国性调查旨在刻画医疗体系结构，并探究基于循证创新的医疗体系改革对医疗服务的影响。行政数据可供研究者使用，由此引发问题：能否通过融合辅助数据信息增强基于调查数据对医疗机构所作的推断？行政数据可为处理调查中的覆盖不足偏差和无应答问题、捕获更多亚群体提供信息。本研究聚焦于利用行政索赔数据改进有限总体调查项目均值的估计。通过多重插补法将索赔数据的辅助信息纳入模型，对未应答或未调查机构的数值进行插补。我们推导了多种插补策略变体，并将方法的逻辑演进与两种现有方法进行比较：一种是忽略抽样概率的朴素分析，另一种是采用抽样概率倒数加权的传统调查分析。最后，利用美国医疗机构与系统全国调查及医疗保险与医疗补助服务中心的医疗保险索赔数据对方法进行实证，以推断医疗机构特征与其提供医疗服务之间的关系。