论文标题
通过模块化优化对医院服务区域和医院转诊区域的自动描述
Automated Delineation of Hospital Service Areas and Hospital Referral Regions by Modularity Optimization
论文作者
论文摘要
客观的。为了开发一种自动化，数据驱动和规模的方法来描述最新的HSA和HRR，代表了所有患者，并具有医院就诊的最佳定位。数据源。佛罗里达州的2011年州立住院数据库（SID）来自医疗保健成本和利用项目（HCUP）。研究设计。通过最大化每个HSA/HRR内的患者到医院流，同时最大程度地减少它们之间的流量，使用了一种网络优化方法来重新定义HSA和HRR。我们首先构建了与佛罗里达州现有的达特茅斯单元一样多的HSA/HRR，然后通过各种指标比较了这两种单元。接下来，我们试图得出最佳的HSA/HRR的最佳数字和配置，这些数字和配置最能反映佛罗里达州住院模式的模块化。主要发现。通过我们的方法，HSAS/HRR在区域规模和市场结构，形状以及最重要的是当地住院方面的平衡而受到达特茅斯单位的青睐。结论。新方法是自动化的，可扩展的，可有效捕获医疗系统的自然结构。它在描述其他医疗服务领域或更大的地理区域的应用中具有巨大的应用。
Objective. To develop an automated, data-driven, and scale-flexible method to delineate HSAs and HRRs that are up-to-date, representative of all patients, and have the optimal localization of hospital visits. Data Sources. The 2011 State Inpatient Database (SID) in Florida from the Healthcare Cost and Utilization Project (HCUP). Study Design. A network optimization method was used to redefine HSAs and HRRs by maximizing patient-to-hospital flows within each HSA/HRR while minimizing flows between them. We first constructed as many HSAs/HRRs as existing Dartmouth units in Florida, and then compared the two by various metrics. Next, we sought to derive the optimal numbers and configurations of HSAs/HRRs that best reflect the modularity of hospitalization patterns in Florida. Principal Findings. The HSAs/HRRs by our method are favored over the Dartmouth units in balance of region size and market structure, shape, and most importantly, local hospitalization. Conclusions. The new method is automated, scale-flexible, and effective in capturing the natural structure of healthcare system. It has great potential for applications in delineating other healthcare service areas or in larger geographic regions.
