Department of Computer Science, Dartmouth College, Hanover, NH, USA.
Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
Stat Med. 2018 Feb 28;37(5):847-866. doi: 10.1002/sim.7565. Epub 2017 Dec 4.
In this paper, we analyze the US Patient Referral Network (also called the Shared Patient Network) and various subnetworks for the years 2009 to 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 health care 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 health care system and opportunities or mechanisms for catalyzing improvements.
本文分析了美国患者转诊网络(也称为共享患者网络)及其在 2009 年至 2015 年期间的各个子网。根据医疗保险和医疗补助服务中心提供的数据,如果患者在指定时间间隔内遇到两名医生,则认为这两名医生之间存在联系。我们在大多数州级数据以及核心-外围结构中发现了幂律分布。在全国和州一级,我们发现了一种所谓的小世界结构,以及一种在某些大规模经济网络中发现的“引力定律”。一些医生在州际转诊中扮演枢纽的角色。在州和国家两级的医疗保健系统统计数据与某些网络统计数据之间发现了强烈的相关性。通过涉及从网络中得出的关键指标的几项统计分析所证明的转诊网络模式,为使用网络分析来深入了解医疗保健系统以及为促进改进提供机会或机制提供了新的见解。