传统数据仓库由ODS(Operational Data Stone)、数据仓库、数据集市和BI工具组成。实时数据仓库扩展了传统数据仓库的适用范围, 能给企业提供关于日常战术操作的技术支持。该文讨论了实时数据仓库的几种体系实现,并同传统数据仓库体系进...传统数据仓库由ODS(Operational Data Stone)、数据仓库、数据集市和BI工具组成。实时数据仓库扩展了传统数据仓库的适用范围, 能给企业提供关于日常战术操作的技术支持。该文讨论了实时数据仓库的几种体系实现,并同传统数据仓库体系进行了比较和分析。通过对 需求、技术、性能等方面的分析。提出了比较可行的实时数据仓库体系结构。展开更多
AIM: To examine the feasibility and validity of electronic generation of quality metrics in the intensive care unit(ICU).METHODS: This minimal risk observational study was performed at an academic tertiary hospital. T...AIM: To examine the feasibility and validity of electronic generation of quality metrics in the intensive care unit(ICU).METHODS: This minimal risk observational study was performed at an academic tertiary hospital. The Critical Care Independent Multidisciplinary Program at Mayo Clinic identified and defined 11 key quality metrics. These metrics were automatically calculated using ICU Data Mart, a near-real time copy of all ICU electronic medical record(EMR) data. The automatic report was compared with data from a comprehensive EMR review by a trained investigator. Data was collected for 93 randomly selected patients admitted to the ICU during April 2012(10% of admitted adult population). This study was approved by the Mayo Clinic Institution Review Board.RESULTS: All types of variables needed for metric calculations were found to be available for manual and electronic abstraction, except information for availability of free beds for patient-specific time-frames. There was 100% agreement between electronic and manual data abstraction for ICU admission source, admission service, and discharge disposition. The agreement between electronic and manual data abstraction of the time of ICU admission and discharge were 99% and 89%. The time of hospital admission and discharge were similar for both the electronically and manually abstracted datasets. The specificity of the electronically-generated report was 93% and 94% for invasive and non-invasive ventilation use in the ICU. One false-positive result for each type of ventilation was present. The specificity for ICU and in-hospital mortality was 100%. Sensitivity was 100% for all metrics.CONCLUSION: Our study demonstrates excellent accuracy of electronically-generated key ICU quality metrics. This validates the feasibility of automatic metric generation.展开更多
文摘传统数据仓库由ODS(Operational Data Stone)、数据仓库、数据集市和BI工具组成。实时数据仓库扩展了传统数据仓库的适用范围, 能给企业提供关于日常战术操作的技术支持。该文讨论了实时数据仓库的几种体系实现,并同传统数据仓库体系进行了比较和分析。通过对 需求、技术、性能等方面的分析。提出了比较可行的实时数据仓库体系结构。
文摘AIM: To examine the feasibility and validity of electronic generation of quality metrics in the intensive care unit(ICU).METHODS: This minimal risk observational study was performed at an academic tertiary hospital. The Critical Care Independent Multidisciplinary Program at Mayo Clinic identified and defined 11 key quality metrics. These metrics were automatically calculated using ICU Data Mart, a near-real time copy of all ICU electronic medical record(EMR) data. The automatic report was compared with data from a comprehensive EMR review by a trained investigator. Data was collected for 93 randomly selected patients admitted to the ICU during April 2012(10% of admitted adult population). This study was approved by the Mayo Clinic Institution Review Board.RESULTS: All types of variables needed for metric calculations were found to be available for manual and electronic abstraction, except information for availability of free beds for patient-specific time-frames. There was 100% agreement between electronic and manual data abstraction for ICU admission source, admission service, and discharge disposition. The agreement between electronic and manual data abstraction of the time of ICU admission and discharge were 99% and 89%. The time of hospital admission and discharge were similar for both the electronically and manually abstracted datasets. The specificity of the electronically-generated report was 93% and 94% for invasive and non-invasive ventilation use in the ICU. One false-positive result for each type of ventilation was present. The specificity for ICU and in-hospital mortality was 100%. Sensitivity was 100% for all metrics.CONCLUSION: Our study demonstrates excellent accuracy of electronically-generated key ICU quality metrics. This validates the feasibility of automatic metric generation.