Replication Data for: Analysis of the U.S. Patient Referral Network

In this paper we analyze the US Patient Referral Network (also called the Shared Patient Network) and various subnetworks for the years 2009–2015. In these networks two physicians are linked if a patient encounters both of them within a specified time-interval, according to the data made available by the Centers for Medicare and Medicaid Services. We find power law distributions on most state-level data as well as a core-periphery structure. On a national and state level, we discover a so-called small-world structure as well as a “gravity law" of the type found in some large-scale economic networks. Some physicians play the role of hubs for interstate referral. Strong correlations between certain network statistics with healthcare system statistics at both the state and national levels are discovered. The patterns in the referral network evinced using several statistical analyses involving key metrics derived from the network illustrate the potential for using network analysis to provide new insights into the healthcare system and opportunities or mechanisms for catalyzing improvements.本研究针对2009至2015年间的美国患者转诊网络（US Patient Referral Network，又称共享患者网络）及其各类子网络展开分析。基于美国医疗保险与医疗补助服务中心（Centers for Medicare and Medicaid Services）公开的数据集，此类网络中，若患者在指定时间间隔内同时就诊于两位医师，则该两位医师间将建立连接。研究发现，多数州级数据集呈现幂律分布（power law distribution）特征，且具备核心-外围（core-periphery）结构。在国家与州级层面，本研究观测到典型的小世界（small-world）结构，以及一类与部分大规模经济网络中类似的“重力定律”（gravity law）特征。部分医师承担起跨州转诊枢纽的角色。此外，本研究发现，部分网络统计指标与州级及国家级医疗系统统计指标之间存在显著相关性。本研究通过基于该网络衍生关键指标的多项统计分析，揭示了转诊网络的内在模式，证实了利用网络分析为医疗系统提供全新认知、并探寻促进医疗体系优化的路径与机制的潜在价值。