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Asthma is a leading cause of emergency department (ED) visits and hospitalizations in children, though many could be prevented. Our study objective was to identify factors from the published literature that are associated with future hospitalization for asthma beyond 30 days following an initial asthma ED visit. We searched CINAHL, CENTRAL, MEDLINE, and Embase for all studies examining factors associated with asthma-related hospitalization in children from January 1, 1992 to February 7, 2022.Selecting Studies: All citations were reviewed independently by two reviewers and studies meeting inclusion criteria were assessed for risk of bias. Data on all reported variables were extracted from full text and categorized according to identified themes. Where possible, data were pooled for meta-analysis using random effects models. Of 2262 studies, 68 met inclusion criteria. We identified 28 risk factors and categorized these into six themes. Factors independently associated with future hospitalization in meta-analysis include: exposure to environmental tobacco smoke (OR = 1.94 95%CI 0.67–5.61), pets exposure (OR = 1.67 95%CI 1.17–2.37), and previous asthma hospitalizations (OR = 3.47 95% CI 2.95–4.07). Additional related factors included previous acute care visits, comorbid health conditions (including atopy), allergen exposure, severe-persistent asthma phenotype, inhaled steroid use prior to ED visit, poor asthma control, higher severity symptoms at ED presentation, warmer season at admission, longer length of stay or ICU admission, and African-American race/ethnicity. We identified multiple factors that are consistently associated with future asthma hospitalization in children and could be used to identify those who would benefit from targeted preventative interventions.

Synthesis of thienoindoles via easy and atom economical approaches followed by their potent applications.

Objective We assessed the impact of the transition from a primarily paper‐based electronic health record (EHR) to a comprehensive EHR on emergency physician work tasks and efficiency in an academic emergency department (ED). Methods We conducted a time motion study of emergency physicians on shift in our ED. Fifteen emergency physicians were directly observed for two 4‐hour sessions prior to EHR implementation, during go live, and then during post‐implementation. Observers performed continuous observation and measured times for the following tasks: chart review, direct patient care, documentation, physical movement, communication, teaching, handover, and other. We compared time spent on tasks during the 3 phases of transition and analyzed mean times for the tasks per patient and per shift using 2‐tailed t test for comparison. Results Physicians saw fewer patients per shift during go‐live (0.51 patient/hour, P < 0.01), patient efficiency increased in post‐implementation but did not recover to baseline (−0.31 patient/hour, P = 0.03). From pre‐implementation to post‐implementation, we observed a trend towards increased physician time spent charting (+54 seconds/patient, P = 0.05) and documenting (+36 seconds/patient, P = 0.36); time spent doing direct patient care trended towards decreasing (−0.43 seconds/patient, P = 0.23). A small percentage of shifts were spent receiving technical support and time spent on teaching activities remained relatively stable during EHR transition. Conclusion A new EHR impacts emergency physician task allocation and several changes are sustained post‐implementation. Physician efficiency decreased and did not recover to baseline. Understanding workflow changes during transition to EHR in the ED is necessary to develop strategies to maintain quality of care.

Introduction: Adoption of a new Electronic Health Record (EHR) can introduce radical changes in task allocation, work processes, and efficiency for providers. In June 2019, The Ottawa Hospital transitioned from a primarily paper based EHR to a comprehensive EHR (Epic) using a “big bang” approach. The objective of this study was to assess the impact of the transition to Epic on Emergency Physician (EP) work activities in a tertiary care academic Emergency Department (ED). Methods: We conducted a time motion study of EPs on shift in low acuity areas of our ED (CTAS 3-5). Fifteen EPs representing a spectrum of pre-Epic baseline workflow efficiencies were directly observed in real-time during two 4-hour sessions prior to EHR implementation (May 2019) and again in go live (August 2019). Trained observers performed continuous observation and measured times for the following EP tasks: chart review, direct patient care, documentation, physical movement, communication, teaching, handover, and other (including breaks). We compared time spent on tasks pre Epic and during go live and report mean times for the EP tasks per patient and per shift using two tailed t-test for comparison. Results: All physicians had a 17% decrease in patients seen after Epic implementation (2.72/hr vs 2.24/hr, p Conclusion: Implementation of Epic did not affect EP time with individual patients - there was no change in direct patient care or chart review. Documentation time increased and EP efficiency (patients seen per hr on shift) decreased after go live. Patient volumes cannot be adjusted in the ED therefore anticipating the EHR impact on EP workflow is critical for successful implementation. EDs may consider up staffing 20% during go live. Findings from this study can inform how to best support EDs nationally through transition to EHR.

Finding bugs in a software is a cumbersome and tedious task. When a new bug is reported, the developers find it challenging to replicate the unexpected behavior of the software, in order to fix the original fault. In this paper, an automated model is presented to find and sort the classes present in the source code according to their proneness of containing a bug, depending upon the bug reports. The model uses a text mining approach and recommends a list of classes based upon the lexical similarity between bug reports and the API descriptions, and also the changes previously recommended during bug fixing. To maximize the similarity index and at the same time reduce the number of classes recommended, a Non-dominant Sorting Genetic Algorithm (NSGA-II) is employed. This model was evaluated on three java based open source applications and it is observed that the model created using multi-objective NSGA-II outperforms the traditional methods of bug localization.

Web is huge, abundant and heterogeneous and so are the challenges that arise due to this versatility. Web Applications as the new task-centric and action-oriented facilities have assumed a distinguished role in today's Web. At the same time, faults in these Web Applications are tedious and hard to test due to the labor and resource intensive nature of testing. Thus, making it essential to use automated strategies like fault based testing that can test the continuously advancing web. Fault based testing uses fault classification as a baseline to make testing cost-effective and efficient, hence making fault classification necessary. Fault classification in Web Applications is a course of action of developing models to segregate the various kinds of faults for real world fault based testing. Manual classification tends to be strenuous and therefore in this paper, we intend to predict an efficient automated model which will classify the faults. Our model uses Text Mining and Machine Learning technique to classify the faults of three open source Web Applications, namely, the qaManager, bitWeaver and WebCalender. We provide a comparative study among four models in which different Machine Learning techniques are used namely Support Vector Machines (SVM), Decision Tree, Bernoulli Naive Bayes and Multinomial Naive Bayes. To analyze the performance of these models, Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) and 10-fold validation are used. Results show that all considered Machine Learning techniques prove to be efficient in the classification of faults. Apart from this, analysis also shows that the performance of Multinomial Naive Bayes Classifier is better than the other three classifiers.