An intelligent security systems engineering approach is used to analyze fire and explosive critical incidents, a growing concern in urban communities. A feed-forward back-propagation neural network models the damages ...An intelligent security systems engineering approach is used to analyze fire and explosive critical incidents, a growing concern in urban communities. A feed-forward back-propagation neural network models the damages arising from these critical incidents. The overall goal is to promote fire safety and sustainable security. The intelligent security systems engineering prediction model uses a fully connected multilayer neural network, and considers a number of factors related to the fire or explosive incident including the type of property affected, the time of day, and the ignition source. The network was trained on a large number of critical incident records reported in Toronto, Canada between 2000 and 2006. Our intelligent security systems engineering approach can help emergency responders by improving cr^tical incident analysis, sustainable security, and fire risk management.展开更多
Introduction: Critical incident monitoring is important in quality improvement as it identifies potential risks to patients by analyzing adverse events or near-misses. Methods: This study analyses the reported inciden...Introduction: Critical incident monitoring is important in quality improvement as it identifies potential risks to patients by analyzing adverse events or near-misses. Methods: This study analyses the reported incidents in a tertiary hospital over a 4-year period. Results: A total of 441 incidents were reported out of 98,502 anesthetics performed during the study period. Of these incidents, 67 resulted in no harm caused, 116 with unanticipated ICU admissions and 20 mortalities. The odds of having a critical incident increased with ASA status: from an odds ratio of 2.08 (95% CI: 1.58 to 2.74) for ASA 2 patients compared to ASA1, to OR of 13.70 (5.91 to 31.74) in ASA 5 compared to ASA 1. Critical incidents also have higher odds occurring out of hours (OR 1.7 (1.45 to 2.23) compared to daytime hours (08:00-17:00). They occurred most commonly in maintenance phase (142, 32.7%), followed by induction (120, 27.6%). The most common types of incidents include airway and respiratory (110, 24.9%) followed by drug related incidents (67, 15.2%). Human error was attributed as a significant contributing factor in 276 incidents (61.5%) followed by patient factors in 112 incidents (25.4%). Mitigating factors such as vigilance by staff involved were significant in 136 incidents (30.3%). Conclusion: Higher ASA status appears to be the most important factor associated with actual or potential patient harm in our study. Also significant, was time of incident, with incidents more likely out of hours. Critical incident reporting is a valuable part of quality assurance. We should continue to invest in incident reporting, incident analysis and improvement plans.展开更多
文摘An intelligent security systems engineering approach is used to analyze fire and explosive critical incidents, a growing concern in urban communities. A feed-forward back-propagation neural network models the damages arising from these critical incidents. The overall goal is to promote fire safety and sustainable security. The intelligent security systems engineering prediction model uses a fully connected multilayer neural network, and considers a number of factors related to the fire or explosive incident including the type of property affected, the time of day, and the ignition source. The network was trained on a large number of critical incident records reported in Toronto, Canada between 2000 and 2006. Our intelligent security systems engineering approach can help emergency responders by improving cr^tical incident analysis, sustainable security, and fire risk management.
文摘Introduction: Critical incident monitoring is important in quality improvement as it identifies potential risks to patients by analyzing adverse events or near-misses. Methods: This study analyses the reported incidents in a tertiary hospital over a 4-year period. Results: A total of 441 incidents were reported out of 98,502 anesthetics performed during the study period. Of these incidents, 67 resulted in no harm caused, 116 with unanticipated ICU admissions and 20 mortalities. The odds of having a critical incident increased with ASA status: from an odds ratio of 2.08 (95% CI: 1.58 to 2.74) for ASA 2 patients compared to ASA1, to OR of 13.70 (5.91 to 31.74) in ASA 5 compared to ASA 1. Critical incidents also have higher odds occurring out of hours (OR 1.7 (1.45 to 2.23) compared to daytime hours (08:00-17:00). They occurred most commonly in maintenance phase (142, 32.7%), followed by induction (120, 27.6%). The most common types of incidents include airway and respiratory (110, 24.9%) followed by drug related incidents (67, 15.2%). Human error was attributed as a significant contributing factor in 276 incidents (61.5%) followed by patient factors in 112 incidents (25.4%). Mitigating factors such as vigilance by staff involved were significant in 136 incidents (30.3%). Conclusion: Higher ASA status appears to be the most important factor associated with actual or potential patient harm in our study. Also significant, was time of incident, with incidents more likely out of hours. Critical incident reporting is a valuable part of quality assurance. We should continue to invest in incident reporting, incident analysis and improvement plans.
