Wonsung Lee, Gene Yi, Dain Jung, Minki Kim, and Il-Chul Moon, Network Analysis Approach to Study Hospitals’ Prescription Patterns focused on the Impact of New Healthcare Policy, IEEE International Conference on Systems, Man and Cybernetics (SMC 2014), San Diego, USA, Oct 5-8, 2014

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). Variousnetwork metrics and performance measures are calculated for the two years. Generated hospitalnetworks 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'scorrelation 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.

@INPROCEEDINGS{6974326, 
author={W. Lee and G. Yi and D. Jung and M. Kim and I. C. Moon}, 
booktitle={2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)}, 
title={Network analysis approach to study hospitals' prescription patterns focused on the impact of new healthcare policy}, 
year={2014}, 
pages={2643-2650}, 
keywords={health care;hospitals;medical information systems;network theory (graphs);DUR;NIS dataset;Spearman correlation coefficients;drug utilization review;health care policy;hospital network characteristics;hospital prescription pattern;hospital relationship;implicit information flow;medical environment;national inpatient sample dataset;network analysis approach;network metrics;performance measures;public health care environment;Correlation;Couplings;Hospitals;Informatics;Measurement;Social network services;healthcare informatics;medical informatics;social network analysis}, 
doi={10.1109/SMC.2014.6974326}, 
ISSN={1062-922X}, 
month={Oct}

}

Source Website : 

 http://ieeexplore.ieee.org/document/6974326