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

Hui Yan Ho; Sheuwen Chuang; Niann-Tzyy Dai; Chia-Hsin Cheng; Wei-Fong Kao

doi:10.1016/j.cmpb.2021.106166

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

Comput Methods Programs Biomed. 2021 Aug:207:106166. doi: 10.1016/j.cmpb.2021.106166. Epub 2021 May 19.

Authors

Hui Yan Ho¹, Sheuwen Chuang², Niann-Tzyy Dai³, Chia-Hsin Cheng⁴, Wei-Fong Kao⁵

Affiliations

¹ School of Health Care Administration, Taipei Medical University, Taipei, Taiwan.
² Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan; Health Policy and Care Research Center, Taipei Medical University, Taipei, Taiwan. Electronic address: sheuwen@tmu.edu.tw.
³ Division of Plastic and Reconstructive Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
⁴ Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan.
⁵ Department of Emergency Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.

PMID: 34077867
DOI: 10.1016/j.cmpb.2021.106166

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 ERP.

Methods: Multiple methods were adopted. An expert panel provided consultations on data selection and clustering validation. Data on 116 variables from 535 hospitals were collected via open data platforms under the Ministry of Health and Welfare. Data selection and streamlining was conducted to determine 42 variables for cluster analysis. SAS 9.4 was used to analyze the data set -via 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 which the refined level of emergency care responsibility hospital was found to be most influential on the clustering process. 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 to determine 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.

Keywords: Burn mass casualty incident; Cluster analysis; Formosa Fun Coast Dust Explosion; Hierarchical clustering; Mass casualty distribution.

MeSH terms

Burns* / therapy
Cluster Analysis
Disaster Planning*
Explosions
Government
Hospitals
Humans
Taiwan
Triage