Objective： The objective of this study was to reduce or avoid the occurrence of the cases of osteofascial compartment syndrome induced by a radial artery puncture for arterial blood gas analysis. Methods： We analyze...Objective： The objective of this study was to reduce or avoid the occurrence of the cases of osteofascial compartment syndrome induced by a radial artery puncture for arterial blood gas analysis. Methods： We analyzed an adverse event using cheese model analysis, ＂fish bone＂ analysis, root cause analysis, and other methods. Results： There are three root causes leading to an adverse event： operation technique, assessment of the disease, and informing patient families. However, there are many reasons to promote the occurrence and development of the event. Conclusions： We should analyze and manage the adverse events in patients from the point of view of a system. Developing the measures of a system defense can enhance patient safety and create a good safety culture.展开更多
Several ensemble-based three-dimensional variational (3D-Var) filters are compared. These schemes replace the static background error covariance of the traditional 3D-Var with the ensemble forecast error covariance, b...Several ensemble-based three-dimensional variational (3D-Var) filters are compared. These schemes replace the static background error covariance of the traditional 3D-Var with the ensemble forecast error covariance, but generate analysis ensemble anomalies (perturbations) in different ways. However, it is demonstrated in this paper that they are all theoretically equivalent to the ensemble transformation Kalman filter (ETKF). Furthermore, a new method named EnPSAS is presented. The analysis shows that EnPSAS has a small condition number and can apply covariance localization more easily than other ensemble-based 3D-Var methods.展开更多
文摘Objective： The objective of this study was to reduce or avoid the occurrence of the cases of osteofascial compartment syndrome induced by a radial artery puncture for arterial blood gas analysis. Methods： We analyzed an adverse event using cheese model analysis, ＂fish bone＂ analysis, root cause analysis, and other methods. Results： There are three root causes leading to an adverse event： operation technique, assessment of the disease, and informing patient families. However, there are many reasons to promote the occurrence and development of the event. Conclusions： We should analyze and manage the adverse events in patients from the point of view of a system. Developing the measures of a system defense can enhance patient safety and create a good safety culture.
基金Project supported by the National Natural Science Foundation of China (No. 41105063)the Special Fund for Meteorological Scientific Research in the Public Interest (No. GYHY20100615)
文摘Several ensemble-based three-dimensional variational (3D-Var) filters are compared. These schemes replace the static background error covariance of the traditional 3D-Var with the ensemble forecast error covariance, but generate analysis ensemble anomalies (perturbations) in different ways. However, it is demonstrated in this paper that they are all theoretically equivalent to the ensemble transformation Kalman filter (ETKF). Furthermore, a new method named EnPSAS is presented. The analysis shows that EnPSAS has a small condition number and can apply covariance localization more easily than other ensemble-based 3D-Var methods.
