The effect of triage team on time-in-department in a pediatric emergency department

Objective. To determine whether the use of a triage team would reduce the average time-in-department in a pediatric emergency department by 25%.^ Methods. A triage team consisting of a physician, a nurse, and a nurse's assistant initiated work-ups and saw patients who required minimal lab work-up and were likely to be discharged. Study days were randomized. Our inclusion criteria were all children seen in the emergency center between 6p and 2a Monday-Friday. Our exclusion criteria included resuscitations, inpatient-inpatient transfers, left without being seen, leaving against medical advice, any child seen outside of 6p-2am Monday-Friday and on the weekends. A Pearson-Chi square was used for comparison of the two groups for heterogeneity. For the time-in-department analysis, we performed a 2 sided t-test with a set alpha of 0.05 using Mann Whitney U looking for differences in time-in-department based on acuity level, disposition, and acuity level stratified by disposition. ^ Results. Among urgent and non-urgent patients, we found a statistically significant decrease in time-in-department in a pediatric emergency department. Urgent patients had a time-in-department that was 51 minutes shorter than patients seen on non-triage team days (p=0.007), which represents a 14% decrease in time-in-department. Non-urgent patients seen on triage team days had a time-in-department that was 24 minutes shorter than non-urgent patients seen on non-triage team days (p=0.009). From the disposition perspective, discharged patients seen on triage team days had a shorter time-in-department of 28 minutes as compared to those seen on non-triage team days (p=0.012). ^ Conclusion. Overall, there was a trend towards decreased time-in-department of 19 minutes (5.9% decrease) during triage team times. There was a statistically significant decrease in the time-in-department among urgent patients of 51 minutes (13.9% decrease) and among discharged patients of 28 minutes (8.4% decrease). Urgent care patients make up nearly a quarter of the emergency patient population and decreasing their time-in-department would likely make a significant impact on overall emergency flow.^

Future pandemic influenza control (FPIC) related strategic management plan based on the 2009 H1N1 pandemic influenza review of health policy decisions, their practice and implications

This cross-sectional study is based on the qualitative and quantitative research design to review health policy decisions, their practice and implications during 2009 H1N1 influenza pandemic in the United States and globally. The “Future Pandemic Influenza Control (FPIC) related Strategic Management Plan” was developed based on the incorporation of the “National Strategy for Pandemic Influenza (2005)” for the United States from the U.S. Homeland Security Council and “The Canadian Pandemic Influenza Plan for the Health Sector (2006)” from the Canadian Pandemic Influenza Committee for use by the public health agencies in the United States as well as globally. The “global influenza experts’ survey” was primarily designed and administered via email through the “Survey Monkey” system to the 2009 H1N1 influenza pandemic experts as the study respondents. The effectiveness of this plan was confirmed and the approach of the study questionnaire was validated to be convenient and the excellent quality of the questions provided an efficient opportunity to the study respondents to evaluate the effectiveness of predefined strategies/interventions for future pandemic influenza control.^ The quantitative analysis of the responses to the Likert-scale based questions in the survey about predefined strategies/interventions, addressing five strategic issues to control future pandemic influenza. The effectiveness of strategies defined as pertinent interventions in this plan was evaluated by targeting five strategic issues regarding pandemic influenza control. For the first strategic issue pertaining influenza prevention and pre pandemic planning; the confirmed effectiveness (agreement) for strategy (1a) 87.5%, strategy (1b) 91.7% and strategy (1c) 83.3%. The assessment of the priority level for strategies to address the strategic issue no. (1); (1b (High Priority) > 1a (Medium Priority) > 1c (Low Priority) based on the available resources of the developing and developed countries. For the second Strategic Issue encompassing the preparedness and communication regarding pandemic influenza control; the confirmed effectiveness (agreement) for the strategy (2a) 95.6%, strategy (2b) 82.6%, strategy (2c) 91.3% and Strategy (2d) 87.0%. The assessment of the priority level for these strategies to address the strategic issue no. (2); (2a (highest priority) > 2c (high priority) >2d (medium priority) > 2b (low priority). For the third strategic issue encompassing the surveillance and detection of pandemic influenza; the confirmed effectiveness (agreement) for the strategy (3a) 90.9% and strategy (3b) 77.3%. The assessment of the priority level for theses strategies to address the strategic Issue No. (3) (3a (high priority) > 3b (medium/low priority). For the fourth strategic issue pertaining the response and containment of pandemic influenza; the confirmed effectiveness (agreement) for the strategy (4a) 63.6%, strategy (4b) 81.8%, strategy (4c) 86.3%, and strategy (4d) 86.4%. The assessment of the priority level for these strategies to address the strategic issue no. (4); (4d (highest priority) > 4c (high priority) > 4b (medium priority) > 4a (low priority). The fifth strategic issue about recovery from influenza and post pandemic planning; the confirmed effectiveness (agreement) for the strategy (5a) 68.2%, strategy (5b) 36.3% and strategy (5c) 40.9%. The assessment of the priority level for strategies to address the strategic issue no. (5); (5a (high priority) > 5c (medium priority) > 5b (low priority).^ The qualitative analysis of responses to the open-ended questions in the study questionnaire was performed by means of thematic content analysis. The following recurrent or common “themes” were determined for the future implementation of various predefined strategies to address five strategic issues from the “FPIC related Strategic Management Plan” to control future influenza pandemics. (1) Pre Pandemic Influenza Prevention, (2) Seasonal Influenza Control, (3) Cost Effectiveness of Non Pharmaceutical Interventions (NPI), (4) Raising Global Public Awareness, (5) Global Influenza Vaccination Campaigns, (6)Priority for High Risk Population, (7) Prompt Accessibility and Distribution of Influenza Vaccines and Antiviral Drugs, (8) The Vital Role of Private Sector, (9) School Based Influenza Containment, (10) Efficient Global Risk Communication, (11) Global Research Collaboration, (12) The Critical Role of Global Public Health Organizations, (13) Global Syndromic Surveillance and Surge Capacity and (14) Post Pandemic Recovery and Lessons Learned. The future implementation of these strategies with confirmed effectiveness to primarily “reduce the overall response time’ in the process of ‘early detection’, ‘strategies (interventions) formulation’ and their ‘implementation’ to eventually ensure the following health outcomes: (a) reduced influenza transmission, (b) prompt and effective influenza treatment and control, (c) reduced influenza related morbidity and mortality.^

Advanced Protein Modeling Method: Benchmarking and Applications in Computer-Aided Drug Discovery

Development of homology modeling methods will remain an area of active research. These methods aim to develop and model increasingly accurate three-dimensional structures of yet uncrystallized therapeutically relevant proteins e.g. Class A G-Protein Coupled Receptors. Incorporating protein flexibility is one way to achieve this goal. Here, I will discuss the enhancement and validation of the ligand-steered modeling, originally developed by Dr. Claudio Cavasotto, via cross modeling of the newly crystallized GPCR structures. This method uses known ligands and known experimental information to optimize relevant protein binding sites by incorporating protein flexibility. The ligand-steered models were able to model, reasonably reproduce binding sites and the co-crystallized native ligand poses of the β2 adrenergic and Adenosine 2A receptors using a single template structure. They also performed better than the choice of template, and crude models in a small scale high-throughput docking experiments and compound selectivity studies. Next, the application of this method to develop high-quality homology models of Cannabinoid Receptor 2, an emerging non-psychotic pain management target, is discussed. These models were validated by their ability to rationalize structure activity relationship data of two, inverse agonist and agonist, series of compounds. The method was also applied to improve the virtual screening performance of the β2 adrenergic crystal structure by optimizing the binding site using β2 specific compounds. These results show the feasibility of optimizing only the pharmacologically relevant protein binding sites and applicability to structure-based drug design projects.