目的利用Power BI Desktop对医院DIP分组明细进行多维度对比分析及数据可视化,寻找医院DIP亏损原因。方法提取某三甲肿瘤专科医院2022年度部分出院结算DIP入组明细数据,通过数据导入、数据类型转换、建立数据关系、数据建模、数据可视...目的利用Power BI Desktop对医院DIP分组明细进行多维度对比分析及数据可视化,寻找医院DIP亏损原因。方法提取某三甲肿瘤专科医院2022年度部分出院结算DIP入组明细数据,通过数据导入、数据类型转换、建立数据关系、数据建模、数据可视化等方法,建立全院、科室、病种等多个维度的可视化动态分析报表。结果Power BI Desktop能有效提升DIP精细化管理水平,提升工作效率,精准分析亏损原因,指导科室进行改进,有效减少医院DIP医保资金亏损。结论Power BI Desktop具有成本低廉,定制化、可视化、自动化程度高,用户界面友好等特点,值得在各医院尤其是信息化程度低、专科运营人员不足、资金预算不足的医院DIP精细化管理中进行推广。展开更多
The aim of present research is to study the dispersion of air pollutants using the air quality model, AERMOD and to predict the impact of pollutants (PM<sub>10</sub>, NO<sub>2</sub> and CO) at ...The aim of present research is to study the dispersion of air pollutants using the air quality model, AERMOD and to predict the impact of pollutants (PM<sub>10</sub>, NO<sub>2</sub> and CO) at the receptor level released from Gas Based Power Plant (GBPP). The net-concentrations including monitored data plus predicted values of PM<sub>10</sub>, NO<sub>2</sub> and CO would be increased from base value 75 to 77.61 μg/m<sup>3</sup> with an increase of 3.48%, 22 to 26.66 μg/m<sup>3</sup> with an increase of 21.18% and 428 to 538.37 μg/m<sup>3</sup> with an increase of 25.79% respectively. The study of hill effect showed that it had profound impact upon the dispersion of pollutants and the ratio (with hill and without hill) of each pollutant was 3.89 for PM<sub>10</sub> (24 hr), 2.40 for NO<sub>2</sub> (24 hr) and 13.98 for CO (1 hr). The natural gas based plant not only decreases the pollution level but also reduces the hospital treatment cost and protects the public health. The modeling results suggest that the GBPP could be a clean technology as replacement of coal power plants located in the city which pollute the environment considerably in spite of control measures installed.展开更多
文摘目的利用Power BI Desktop对医院DIP分组明细进行多维度对比分析及数据可视化,寻找医院DIP亏损原因。方法提取某三甲肿瘤专科医院2022年度部分出院结算DIP入组明细数据,通过数据导入、数据类型转换、建立数据关系、数据建模、数据可视化等方法,建立全院、科室、病种等多个维度的可视化动态分析报表。结果Power BI Desktop能有效提升DIP精细化管理水平,提升工作效率,精准分析亏损原因,指导科室进行改进,有效减少医院DIP医保资金亏损。结论Power BI Desktop具有成本低廉,定制化、可视化、自动化程度高,用户界面友好等特点,值得在各医院尤其是信息化程度低、专科运营人员不足、资金预算不足的医院DIP精细化管理中进行推广。
文摘The aim of present research is to study the dispersion of air pollutants using the air quality model, AERMOD and to predict the impact of pollutants (PM<sub>10</sub>, NO<sub>2</sub> and CO) at the receptor level released from Gas Based Power Plant (GBPP). The net-concentrations including monitored data plus predicted values of PM<sub>10</sub>, NO<sub>2</sub> and CO would be increased from base value 75 to 77.61 μg/m<sup>3</sup> with an increase of 3.48%, 22 to 26.66 μg/m<sup>3</sup> with an increase of 21.18% and 428 to 538.37 μg/m<sup>3</sup> with an increase of 25.79% respectively. The study of hill effect showed that it had profound impact upon the dispersion of pollutants and the ratio (with hill and without hill) of each pollutant was 3.89 for PM<sub>10</sub> (24 hr), 2.40 for NO<sub>2</sub> (24 hr) and 13.98 for CO (1 hr). The natural gas based plant not only decreases the pollution level but also reduces the hospital treatment cost and protects the public health. The modeling results suggest that the GBPP could be a clean technology as replacement of coal power plants located in the city which pollute the environment considerably in spite of control measures installed.