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
Gestational Diabetes Mellitus (GDM) is a significant health concern affecting pregnant women worldwide. It is characterized by elevated blood sugar levels during pregnancy and poses risks to both maternal and fetal he...Gestational Diabetes Mellitus (GDM) is a significant health concern affecting pregnant women worldwide. It is characterized by elevated blood sugar levels during pregnancy and poses risks to both maternal and fetal health. Maternal complications of GDM include an increased risk of developing type 2 diabetes later in life, as well as hypertension and preeclampsia during pregnancy. Fetal complications may include macrosomia (large birth weight), birth injuries, and an increased risk of developing metabolic disorders later in life. Understanding the demographics, risk factors, and biomarkers associated with GDM is crucial for effective management and prevention strategies. This research aims to address these aspects comprehensively through the analysis of a dataset comprising 600 pregnant women. By exploring the demographics of the dataset and employing data modeling techniques, the study seeks to identify key risk factors associated with GDM. Moreover, by analyzing various biomarkers, the research aims to gain insights into the physiological mechanisms underlying GDM and its implications for maternal and fetal health. The significance of this research lies in its potential to inform clinical practice and public health policies related to GDM. By identifying demographic patterns and risk factors, healthcare providers can better tailor screening and intervention strategies for pregnant women at risk of GDM. Additionally, insights into biomarkers associated with GDM may contribute to the development of novel diagnostic tools and therapeutic approaches. Ultimately, by enhancing our understanding of GDM, this research aims to improve maternal and fetal outcomes and reduce the burden of this condition on healthcare systems and society. However, it’s important to acknowledge the limitations of the dataset used in this study. Further research utilizing larger and more diverse datasets, perhaps employing advanced data analysis techniques such as Power BI, is warranted to corroborate and expand upon the findings of this research. This underscores the ongoing need for continued investigation into GDM to refine our understanding and improve clinical management strategies.展开更多
目的:搭建放射科检查安排统计分析平台,为合理安排放射科检查资源提供数据支撑。方法:基于Power BI搭建放射科检查安排统计分析平台,该平台通过Power BI的数据网关从放射科信息系统(radiology information system,RIS)、影像数据交换平...目的:搭建放射科检查安排统计分析平台,为合理安排放射科检查资源提供数据支撑。方法:基于Power BI搭建放射科检查安排统计分析平台,该平台通过Power BI的数据网关从放射科信息系统(radiology information system,RIS)、影像数据交换平台、数据查找表中获取数据;使用Power BI Desktop进行数据处理和建模,并将数据处理的步骤和数据模型上传至Power BI报表服务器,Power BI报表服务器将可视化图表发布至客户端。放射科管理者利用该平台的分析结果,调整申请、预约、检查等环节的工作安排。结果:利用该平台对放射科检查安排工作进行优化后,每周可额外提供各类检查资源时长约9 h。结论:基于Power BI的放射科检查安排工作的改进,可有效减少检查资源的浪费,提高工作效率。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Gestational Diabetes Mellitus (GDM) is a significant health concern affecting pregnant women worldwide. It is characterized by elevated blood sugar levels during pregnancy and poses risks to both maternal and fetal health. Maternal complications of GDM include an increased risk of developing type 2 diabetes later in life, as well as hypertension and preeclampsia during pregnancy. Fetal complications may include macrosomia (large birth weight), birth injuries, and an increased risk of developing metabolic disorders later in life. Understanding the demographics, risk factors, and biomarkers associated with GDM is crucial for effective management and prevention strategies. This research aims to address these aspects comprehensively through the analysis of a dataset comprising 600 pregnant women. By exploring the demographics of the dataset and employing data modeling techniques, the study seeks to identify key risk factors associated with GDM. Moreover, by analyzing various biomarkers, the research aims to gain insights into the physiological mechanisms underlying GDM and its implications for maternal and fetal health. The significance of this research lies in its potential to inform clinical practice and public health policies related to GDM. By identifying demographic patterns and risk factors, healthcare providers can better tailor screening and intervention strategies for pregnant women at risk of GDM. Additionally, insights into biomarkers associated with GDM may contribute to the development of novel diagnostic tools and therapeutic approaches. Ultimately, by enhancing our understanding of GDM, this research aims to improve maternal and fetal outcomes and reduce the burden of this condition on healthcare systems and society. However, it’s important to acknowledge the limitations of the dataset used in this study. Further research utilizing larger and more diverse datasets, perhaps employing advanced data analysis techniques such as Power BI, is warranted to corroborate and expand upon the findings of this research. This underscores the ongoing need for continued investigation into GDM to refine our understanding and improve clinical management strategies.
