英文摘要 |
Due to rapid environmental changes in the past few years, hospital managers have paid more attention to performance management so that they can compete in more hostile health service market. Therefore, how to improve operating efficiency in serving patients has become an important topic for hospital managers. Data envelopment analysis (DEA) is a popular tool used to calculate relative efficiency of hospital so that hospital managers can be aware of the advantage and disadvantages of their hospital operation and resources allocation. This study aims at accessing relative operating performance (efficiency) of twelve veteran hospitals by using DEA method. Three main objectives of this study include: 1. Establishing a DEA model to assess relative operating efficiency of 12 veteran hospitals. 2. Comparing relative efficiency of these 12 veteran hospitals to DOH hospitals. 3. Suggesting potential solutions for veteran hospitals to improve their relative operating efficiency. A three consecutive year operating dataset were used to prepare data required for DEA. Operating data of a hospital in different years were treated independently. Results of this study indicate that: 1. Twenty-eight out of thirty-six (63.1%) veteran hospitals had achieved complete relative efficiency (efficiency score=1.0), and five out of twelve veteran hospitals have achieved operating efficient every year. 2. Two veteran hospitals were found to be relative inefficiency for three consecutive years. 3. Production scale was not related to operating efficiency, and 4. When twelve veteran hospitals were compared to twenty-two DOH hospitals seven hospitals (or 58.3%) reached complete efficiency but only nine, out of twenty- two, DOH hospitals (or 40.9%) had reached complete efficiency. Because this study did not included quality and financial data, results of the study should be interpreted cautiously. Future studies in similar subject should financial or quality data so that issues related to operating efficiency of veteran hospitals can be recognized. |