Ranking hospitals’ burn care capacity using cluster analysis on open government data

Abstract Background and Objective : To deal with burn mass casualty incidents (BMCIs), various countries have established national or regional BMCI emergency response plans (ERPs). A burn care capacity ranking model for hospitals can play an integral role in ERPs by providing essential information to emergency medical services for distributing and handling mass-burn patients. Ranking models vary across countries and contexts. However, Taiwan has had no such model. The study aims to develop a ranking model for classifying hospitals’ burn care capacity in preparation for the development of a national BMCI's ERP. Methods : Multiple methods were adopted. An expert panel provided consultations to data selections and clustering validation. 535 hospitals data with 116 variables were collected via the open data platforms under the Ministry of Health and Welfare. Data selection and streamlining process was conducted to determine 42 variables for cluster analysis. SAS 9.4 was used to analyze the data set through a hierarchical cluster analysis using Ward's method, followed by a tree-based model analysis to identify the criteria for each cluster. Both internal and external cluster validation were performed. Results : Four clusters of burn care capacity were determined to be a suitable number of clusters. All hospitals were arranged into capacity levels accordingly. Results of the Kruskal-Wallis test showed that the difference between clusters were significant. Tree-based model analysis revealed four determining variables, among them, the refined level of emergency care responsible hospital was the most influential factor for the clustering. Responses from the questionnaire were used as an external validation tool to corroborate with the cluster analysis results. Conclusion : The use of open government data and cluster analysis was suitable for developing a ranking model for determining hospitals’ burn care capacity levels in Taiwan. The proposed ranking model can be used to develop a BMCI emergency response plan, and can also serve as a reference for using cluster analysis with open government data to rank care capacity or quality in other domains.