Evaluating the comparative efficiency of medical centers in Taiwan: a dynamic data envelopment analysis application

Cheng Ming Chiu, Ming Shu Chen, Chung Shun Lin, Wei Yu Lin, Hui Chu Lang*

*此作品的通信作者

研究成果: Article同行評審

4 引文 斯高帕斯(Scopus)

摘要

Background: People in Taiwan enjoy comprehensive National Health Insurance coverage. However, under the global budget constraint, hospitals encounter enormous challenges. This study was designed to examine Taiwan medical centers' efficiency and factors that influence it. Methods: We obtained data from open sources of government routine publications and hospitals disclosed by law to the National Health Insurance Administration, Ministry of Health and Welfare, Taiwan. The dynamic data envelopment analysis (DDEA) model was adopted to estimate all medical centers' efficiencies during 2015–2018. Beta regression models were used to model the efficiency level obtained from the DDEA model. We applied an input-oriented approach under both the constant returns-to-scale (CRS) and variable returns-to-scale (VRS) assumptions to estimate efficiency. Results: The findings indicated that 68.4% (13 of 19) of medical centers were inefficient according to scale efficiency. The mean efficiency scores of all medical centers during 2015–2018 under the CRS, VRS, and Scale were 0.85, 0.930, and 0.95,respectively. Regression results showed that an increase in the population less than 14 years of age, assets, nurse-patient ratio and bed occupancy rate could increase medical centers' efficiency. The rate of emergency return within 3-day and patient self-pay revenues were associated significantly with reduced hospital efficiency (p < 0.05). The result also showed that the foundation owns medical center has the highest efficiency than other ownership hospitals. Conclusions: The study results provide information for hospital managers to consider ways they could adjust available resources to achieve high efficiency.

原文English
文章編號435
期刊BMC Health Services Research
22
發行號1
DOIs
出版狀態Published - 12月 2022

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