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To identify the factors affecting Emergency Department Length of Stay for transferred critically ill patients.The Length of Stay of the transferred patients is an important indicator of Emergency Department service quality; thus, understanding the factors affecting the Emergency Department Length of Stay of transferred critically ill patients is essential.Using the electronic medical records of 968 transferred critically ill Emergency Department patients of a tertiary hospital in Korea, prediction models for Emergency Department Length of Stay were built using various machine learning algorithms.The logistic regression (AUROC 0.85) models showed the best performance, followed by random forest (AUROC 0.83) and Naive Bayes (AUROC 0.83). The logistic regression model indicated that fewer consultations, the highest acuity level, need for an emergency operation or angiography, need for ICU admission, severe emergency disease and fewer diagnoses were the statistically significant predictors for Emergency Department Length of Stay of 6 h or less.The transferred critically ill patients analysed in this study who required immediate or specialized care tended to receive needed care on time at the study site.Understanding the factors affecting the Emergency Department Length of Stay of transferred critically ill patients is crucial for developing strategies to manage the nursing resource of Emergency Department successfully.

BackgroundTransferring an emergency patient to another emergency department (ED) is necessary when she/he is unable to receive necessary treatment from the first visited ED, although the transfer poses potential risks for adverse clinical outcomes and lowering the quality of emergency medical services by overcrowding the transferred ED. This study aimed to understand the factors affecting the ED length of stay (LOS) of critically ill patients and to investigate whether they are receiving prompt treatment through Interhospital Transfer (IHT).MethodsThis study analyzed 968 critically ill patients transferred to the ED of the study site in 2019. Machine learning based prediction models were built to predict the ED LOS dichotomized as greater than 6 hours or less. Explanatory variables in patient characteristics, clinical characteristics, transfer-related characteristics, and ED characteristics were selected through univariate analyses.ResultsAmong the prediction models, the Logistic Regression (AUC 0.85) model showed the highest prediction performance, followed by Random Forest (AUC 0.83) and Naïve Bayes (AUC 0.83). The Logistic Regression model suggested that the need for emergency operation or angiography (OR 3.91, 95% CI=1.65–9.21), the need for Intensive Care Unit (ICU) admission (OR 3.84, 95% CI=2.53–5.83), fewer consultations (OR 3.57, 95% CI=2.84–4.49), a high triage level (OR 2.27, 95% CI=1.43–3.59), and fewer diagnoses (OR 1.32, 95% CI=1.09–1.61) coincided with a higher likelihood of 6-hour-or-less stays in the ED. Furthermore, an interhospital transfer handoff led to significantly shorter ED LOS among the patients who needed emergency operation or angiography, or ICU admission, or had a high triage level.ConclusionsThe results of this study suggest that patients prioritized in emergency treatment receive prompt intervention and leave the ED in time. Also, having a proper interhospital transfer handoff before IHT is crucial to provide efficient care and avoid unnecessarily longer stay in ED.

Crowding in the emergency department is a problem worldwide that can affect patient safety and clinical outcomes. The aim of this project was to evaluate a multimodal quality improvement intervention with a new patient flow manager to reduce ED length of stay and ED bed occupancy.This single-site interrupted time-series analysis study was conducted in a tertiary hospital emergency department in South Korea. Interventions for a novel system load-balancing approach included a data-driven patient flow tracking informatics system, adding medical specialists, point-of-care creatinine testing (when required before diagnostic imaging) with dedicated imaging test slots for emergency patients, and introducing patient flow managers. Records of adult patients visiting the emergency department from January 2016 to March 2020 were included. Outcomes were ED length of stay and ED bed occupancy. Regression discontinuity analysis of an interrupted time series was used adjusting for seasonality and the number of patients per staff.A total of 46,494 patients in the preintervention period and 151,802 patients in the postintervention period were included. After the intervention, ED length of stay decreased by 4.07 hours, whereas the slope indicated a return to preintervention levels over time. Monthly average ED bed occupancy decreased by 34.6%, and the slope remained consistent over time.The multimodal quality improvement intervention that included a patient flow manager was an effective intervention to reduce the ED length of stay and the ED bed occupancy at the study site. The change for length of stay may not sustain over time without further intervention.

Despite years of excellent individual studies, the impact of nanoparticle (NP) cytotoxicity studies remains limited by inconsistent data collection and analysis. It is often unclear how exposure conditions can be used to determine cytotoxicity quantitatively. Discrepancies due to using different measurement conditions, readouts and controls to characterize NP interactions with cells lead to further challenges. To examine which parameters are critical in NP cytotoxicity studies, we have chosen to examine two NP types (liposomes and quantum dots) at different concentrations incubated with two primary vascular endothelial cells, HUVEC and HMVEC-C for a standard time of 24 h. We paid close attention to the effects of positive controls and cell association on interpretation of cytotoxicity data. Various cellular responses (ATP content, oxidative stress, mitochondrial toxicity, and phospholipidosis) were measured in parallel. Interestingly, cell association data varied significantly with the different image analyses. However, cytotoxicity responses could all be correlated with exposure concentration. Cell type did have an effect on cytotoxicity reports. Most significantly, NP cytotoxicity results varied with the inclusion or exclusion of positive controls. In the absence of positive controls, one tends to emphasize small changes in cell responses to NPs.

Li-air rechargeable batteries theoretically have advantages from both secondary batteries and fuel cells, which can be viewed as the best technological blends for automotive applications resolving the so called mileage anxiety problem due to the limited driving range of electrical vehicles based upon Li-ion batteries; this problem is rooted in the intrinsically small energy density of Li-ion batteries. This very scientific trait of Li-air batteries, which is apparently suited to the requirements of batteries for future electric vehicles, has induced quite a strong surge of research recently. This occurrence has motivated the authors to undertake a thorough review in an effort to understand the current status of Li-air battery related technologies. A comprehensive survey from a battery industry standpoint has been conducted on the fundamentals of chemistry, utilized Li-air cell configurations (or types) vs. performance, and major components comprising Li-air batteries using various sources of previously published peer-reviewed journal papers, book chapters, patents, and industrial reports. The survey results are presented here.

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