Steitz Bryan D, Levy Mia A
Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37201, USA.
Department of Medicine, Division of Hematology and Oncology, Vanderbilt University Medical Center, Nashville, TN 37201, USA.
Informatics (MDPI). 2018 Sep;5(3). doi: 10.3390/informatics5030034. Epub 2018 Aug 6.
Social network analysis (SNA) is a quantitative approach to study relationships between individuals. Current SNA methods use static models of organizations, which simplify network dynamics. To better represent the dynamic nature of clinical care, we developed a temporal social network analysis model to better represent care temporality. We applied our model to appointment data from a single institution for early stage breast cancer patients. Our cohort of 4082 patients were treated by 2190 providers. Providers had 54,695 unique relationships when calculated using our temporal method, compared to 249,075 when calculated using the atemporal method. We found that traditional atemporal approaches to network modeling overestimate the number of provider-provider relationships and underestimate common network measures such as care density within a network. Social network analysis, when modeled accurately, is a powerful tool for organizational research within the healthcare domain.
社会网络分析(SNA)是一种研究个体之间关系的定量方法。当前的SNA方法使用组织的静态模型,这简化了网络动态。为了更好地体现临床护理的动态性质,我们开发了一种时间社会网络分析模型,以更好地呈现护理的时间性。我们将我们的模型应用于来自单个机构的早期乳腺癌患者的预约数据。我们的4082名患者队列由2190名医护人员治疗。使用我们的时间方法计算时,医护人员有54,695种独特的关系,而使用非时间方法计算时为249,075种。我们发现,传统的非时间网络建模方法高估了医护人员之间的关系数量,并低估了诸如网络内护理密度等常见网络指标。当准确建模时,社会网络分析是医疗领域内组织研究的有力工具。