目的 以山东省57家三级综合医院为研究样本,评价其医疗质量与运营效率状况,为加强医院内涵建设、提升精细化管理水平提供参考依据。方法 通过构建评价指标体系,运用熵权逼近理想解排序法(technique for order preference by similarity ...目的 以山东省57家三级综合医院为研究样本,评价其医疗质量与运营效率状况,为加强医院内涵建设、提升精细化管理水平提供参考依据。方法 通过构建评价指标体系,运用熵权逼近理想解排序法(technique for order preference by similarity to ideal solution, TOPSIS)与数据包络分析法(data envelopmentanalysis, DEA)分别对样本医院的医疗质量及运营效率水平进行评价分析。结果 医疗质量方面,样本医院间四级手术占比和CMI差异较大,且权重最大;质量水平排名靠前的医院为A1、A2、A3、A4、A29,均为三级甲等医院,且多为省属医院。运营效率方面,57家样本医院整体运营效率较高,但医院间发展不均衡,其中22家医院DEA有效,12家医院DEA弱有效,23家医院DEA无效。医疗质量与纯技术效率之间存在正相关关系。结论 部分样本医院医疗质量与运营效率有较大改进空间,应聚焦功能定位,在医疗技术水平、资源配置和内部管理能力等方面持续改进和提升,从规模扩张转向提质增效,助力医院高质量发展。展开更多
Using the classification results by the fuzzy clustering models as the basis for choosing the choosing patterns, a feed forward networks model for classification is given. Remarkable success was achieved in training t...Using the classification results by the fuzzy clustering models as the basis for choosing the choosing patterns, a feed forward networks model for classification is given. Remarkable success was achieved in training the networks to learn the patterns and in classifying the coal reserve assets. The results show that the neural network approach for classification has some advantages such as stability and reliability.展开更多
文摘目的 以山东省57家三级综合医院为研究样本,评价其医疗质量与运营效率状况,为加强医院内涵建设、提升精细化管理水平提供参考依据。方法 通过构建评价指标体系,运用熵权逼近理想解排序法(technique for order preference by similarity to ideal solution, TOPSIS)与数据包络分析法(data envelopmentanalysis, DEA)分别对样本医院的医疗质量及运营效率水平进行评价分析。结果 医疗质量方面,样本医院间四级手术占比和CMI差异较大,且权重最大;质量水平排名靠前的医院为A1、A2、A3、A4、A29,均为三级甲等医院,且多为省属医院。运营效率方面,57家样本医院整体运营效率较高,但医院间发展不均衡,其中22家医院DEA有效,12家医院DEA弱有效,23家医院DEA无效。医疗质量与纯技术效率之间存在正相关关系。结论 部分样本医院医疗质量与运营效率有较大改进空间,应聚焦功能定位,在医疗技术水平、资源配置和内部管理能力等方面持续改进和提升,从规模扩张转向提质增效,助力医院高质量发展。
文摘Using the classification results by the fuzzy clustering models as the basis for choosing the choosing patterns, a feed forward networks model for classification is given. Remarkable success was achieved in training the networks to learn the patterns and in classifying the coal reserve assets. The results show that the neural network approach for classification has some advantages such as stability and reliability.
