Network analysis approach to study hospitals' prescription patterns focused on the impact of new healthcare policy

Abstract

Understanding hospitals' relationships is critical to the analysis of public healthcare environment. There have been many attempts to analyze medical environment at a personal level. Recently, at an organizational level, there has been some advance in research into examining a relationship between hospitals. However, the formation of linkages is restricted to explicit and direct interactions. In contrast, we focused on implicit information flows between hospitals. This study also analyzes large scale hospital networks based on prescribing similarity. The sample dataset we used is the trustworthy representative of actual population in Korea. We assessed the impact of Drug Utilization Review (DUR) on hospital network characteristics. We examined National Inpatient Sample (NIS) dataset for before-DUR year (2010) and after-DUR year (2011). Various network metrics and performance measures are calculated for the two years. Generated hospital networks of the two years were significantly different in terms of both network metrics and performance measures, except for a riskiness measure. In network clustering result, Spearman's correlation coefficients indicated that network metrics can be used to evaluate hospitals having extreme prescription patterns. We anticipate our novel approach allows us to better understand public healthcare environment.