Center for Organization, Leadership and Management Research, Department of Veterans Affairs, Boston, MA, USA.
Health Econ. 2011 Feb;20(2):239-51. doi: 10.1002/hec.1588.
Measuring health services provided to patients can be difficult when patients see providers across multiple health systems and all visits are rarely captured in a single data source covering all systems where patients receive care. Studies that account for only one system will omit the out-of-system health-care use at the patient level. Combining data across systems and comparing utilization patterns across health systems creates complications for both aggregation and accuracy because data-generating processes (DGPs) tend to vary across systems. We develop a hybrid methodology for aggregation across systems, drawing on the strengths of the DGP in each system, and demonstrate its validity for answering research questions requiring cross-system assessments of health-care utilization. Positive and negative predictive probabilities can be useful to assess the impact of the hybrid methodology. We illustrate these issues comparing public sector (administrative records from the US Department of Veterans Affairs system) and private sector (billing records from the US Medicare system) patient level data to identify primary-care utilization. Understanding the context of a particular health system and its effect on the DGP is important in conducting effective valid evaluations.
当患者在多个医疗系统中就诊，且所有就诊记录很少在一个涵盖所有患者就诊系统的单一数据源中捕获时，评估患者接受的医疗服务可能会比较困难。仅针对一个系统进行的研究将忽略患者层面的系统外医疗保健使用情况。跨系统整合数据并比较医疗系统之间的使用模式会给聚合和准确性带来复杂问题，因为数据生成过程（DGP）在各系统之间往往存在差异。我们开发了一种跨系统聚合的混合方法，利用每个系统中的 DGP 优势，并证明其对于回答需要对医疗保健使用情况进行跨系统评估的研究问题具有有效性。阳性和阴性预测概率对于评估混合方法的影响可能很有用。我们通过比较公共部门（来自美国退伍军人事务部系统的行政记录）和私营部门（来自美国医疗保险系统的计费记录）的患者层面数据来识别初级保健利用情况，来说明这些问题。了解特定医疗系统的背景及其对 DGP 的影响对于进行有效的有效性评估很重要。
