Failure to recognize and respond to early signs of deterioration in hospitalized patients can have significant implications associated with delays in treatment. This lack of recognition was the impetus for rapid response teams in the United States and the recommendation by the Institute of Healthcare Improvement for use of early warning scores. This project was designed to evaluate the pilot implementation of an early warning score on 2 units in a community hospital in the Northeast. The practice-focused question was used to explore how patient outcomes changed following implementation of an early warning score (EWS) compared to patient outcomes associated with a rapid response team alone. The translating evidence into practice model informed this project. Supporting evidence from existing hospital data was collected for rapid response, code blue, and mortality. Analysis using the chi-square test of homogeneity compared post-implementation with baseline data. The findings indicated the differences between the proportions were not statistically significant, indicating the metrics did not change appreciably following the implementation of the early warning score. While the evaluation analytics of this pilot did not demonstrate significant change in the outcome measures post-implementation, the results may be useful for the facility when performing a future evaluation of the EWS. It is possible that the results of the 2 units were not representative of the facility, and it is therefore recommended to repeat the evaluation using data from the entire facility for a longer period. Increasing the capacity for early recognition in decline has implications for social change through improvement in safety and quality of health care for all hospitalized patients.

The nation has placed a spotlight on improving graduation rates for all students. The current study analyzed retrospective, longitudinal student data from the fifth largest school district in Oregon to create an Early Warning Indicator System (EWS) to identify students on track to graduate and those who are not. This study creates an EWS system using the student demographics and the ABC’s of (a) attendance, (b) behavior, and (c) coursework to identify students who are on track and those who are not. I employed logistic regression model to build a prediction model using middle school data (N = 2,041) that examined predictors established in sixth through eighth grade with high school graduation. The dependent variable, four-year graduation was coded as graduate or non-graduate. The independent variables were (a) gender, (b) race, (c) ELL status, (d) SPED Status (e) attendance rate, (f) ODR’s, and (g) number of F’s in English Language Arts and Mathematics. Attendance rate was the strongest predictor of high school graduation. Overall the model predicted graduates with 89.7% accuracy and non-graduates with 33.6% accuracy with the total model predicting 69.5% of graduates and non-graduates.

Purpose: The aim of this project was to evaluate data from a survey sent to nurses in regards to the current practices and workflow of the Early Warning System (EWS) currently in use at Banner Health. Methods: A descriptive cross-sectional design was used to collect feedback from Registered Nurses. The survey was open between February and March of 2016. Likert style and open-ended questions demonstrate evidence supporting the following study questions: 1) What are the barriers to documentation that triggers the EWS? 2) To what extent is the EWS useful and usable? 3) What were the features of the EWS implementation? Results: Findings demonstrated three barriers associated with EWS protocol compliance to include increased workload (78%), previous negative responses from providers (62%), and alert fatigue (48%). Provider responsiveness to notification of the Early Warning Score was shown to be effective "most of the time" and "about half of the time" at 71%, with 12% indicating "sometimes" and "never". Deployment of the Rapid Response Team (RRT) when indicated by EWS algorithm showed only 9 (14%) of nurses always call the RRT, 7 (11%) call about half the time, and 16% indicated they never use the RRT. "Real time" charting occurred 50-75% or less than 50% of the time for 96% of respondents. Open ended questions support recommendations for future practice to include: implementation of a pop up alert for easy recognition of changes in EWS, tailoring parameters based on individual patient characteristics, automatic direct paging to medical providers, and elimination of the level of consciousness parameter. A validated usability survey provided data with a mean response rate (n=58). Nurses (84%) agree the EWS is useful and usable. Ease of use, efficiency, and comfort with EWS software showed 90% agree. System interface responses demonstrate 23% dislike using the interface, and 21% felt the system interface was unpleasant.Conclusions: Findings demonstrate EWS system usability and usefulness. Recommendations for improvement include implementation of a "pop up" alert for easy recognition of changes in the Early Warning Score and/or automatic direct paging to medical providers and nursing will increase effective use. Barriers to EWS protocol documentation include increased workload, previous negative response from providers, and alert fatigue. "Real time" documentation of physiological parameters is essential to successful triggering of an Early Warning Score.

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Nowadays, the trend towards globalization and internationalization of business has strong impact on companies’ strategy. When a company is thinking about strategy development, it needs to pay attention to variable aspects on micro- and macro-level. This study includes the overview of the factors which need to be considered while developing a strategy. Moreover, a significant emphasize is made on the contemporary deriving challenges at the market. The purpose of the study is to investigate a new approach to the strategic planning, named “Early warning system”. For that purpose, it was conducted qualitative research at the example of the Ukrainian telecommunication company – MTS Ukraine. I identified factors which influence the company and analyzed how company copes with deriving challenges. Moreover, it was found out the company’s strategic planning process is significantly influenced by specific market characteristics such as high level of bureaucracy, specific behavior of some competitors, growing market.

A new zone was added to the existing Livestock Early Warning System (LEWS), which is a subproject of the USAID Global Livestock Collaborative Research Support Program. LEWS uses the PHYGROW model and satellite imagery of weather and vegetation to estimate the availability of forage to livestock and wildlife. Drought advisories are then distributed to governments, development organizations, and pastoralists via the Internet, satellite radios, and written reports. The Laikipia zone was established in 2001 to provide drought early warning for the arid pastoral rangelands of the Ewaso Ngiro ecosystem in the Laikipia and southern Samburu Districts, Kenya. Field verification of PHYGROW estimates of standing crop was conducted in 2002. In addition, research was conducted to determine the ability of the warning system to provide significant advance notice of emerging drought conditions. Results of this study indicate that LEWS is capable of providing accurate estimates of forage availability on East African rangelands. There is also evidence that the use of LEWS advisories could accelerate drought response by pastoralists as much as three to seven weeks.

Due to the global competition, the survival of enterprises must face the major test. Since the poor management of the market will increase number of companies, so the crisis early warning model of business has the necessary to establish. The cause of financial crisis is the main source of financial situation of the deteriorating. Therefore, if we could analysis the facets and weights of potential affect factors through the financial and managerial situation of business to judge the crisis cause of a corporation and establish the early warning model is worth to discuss deeply. The precious year of companies¡¦ data that this study collecting are from the Taiwan Securities Exchange 2006/01/01-2008/12/31 which have been out of the open security market based on the analysis standards and omitting the less information and banking, have total 36 enterprises data for analysis. The application of total variables, this study pre-adapts the TEJ business credit risk indicators to integrate the documentation and analysis the fundamental variables. It can be seen that all the factors have the relationship with each other through this study. This highlights a very important message, and also to the crisis among the factors and normal company with a considerable fluctuations. The judging results of DEA-DA model show that most of the company might be affected with some important factors of interpretation in abnormal situation to let the company in crisis cluster. Through Logistic regression analysis results show that our study forecasting model has the great explanatory power to meet the behavior of interpretation with the crisis and normal companies. By the enterprises crisis model of this study building to assess and forecast the crisis situation have the same results with the simplified model constructing with key factors to affect the original model direction. This study shows a very important fact that the crisis forecasting models will not be simplified to change the outcome which also indicating to increase of variables won¡¦t change the results of the assessment. In accordance with this study proposed model, if value positive that would be show more and more vulnerable to crisis. By other words, if value negative that would be more small vulnerable to crisis.

Bakgrund: Att tidigt upptäcka symtom på allvarlig klinisk försämring hos en patient är av stor vikt för att minska lidande och förhindra allvarliga komplikationer. För detta har flera skattningsinstrument utvecklats, däribland olika early warning score-system. Dessa har implementerats på flera håll i världen men det finns indikationer på att det brister i följsamheten till dessa. Syfte: Studiens syfte var att undersöka följsamheten till Early Warning Scores samt de faktorer som påverkar följsamheten. Metod: Studien genomfördes som en litteraturöversikt där 14 vetenskapliga artiklar inkluderades. Studierna hade kvantitativa, kvalitativa och blandade ansatser. Artiklarna söktes i databaserna PubMed, CINAHL och Web of Science. En innehållsanalys av studiernas resultat genomfördes och resultatet sammanställdes i ett antal kategorier. Resultat: Följsamheten visade sig vara högre till observationer av patientens vitalparametrar än till de åtgärdsriktlinjer som finns. Faktorer som påverkar följsamheten var sjuksköterskans kliniska erfarenhet, samarbete mellan professioner, bemanning, felkalkylering, dokumentation och rapportering. Konklusion: Följsamhet till EWS brister på många sätt och flera faktorer påverkar följsamheten. Faktorerna som påverkade följsamheten är sjuksköterskans kliniska erfarenhet, samarbete mellan professioner, felkalkylering, bemanning, dokumentation och rapportering. / Background: Early recognition of serious clinical deterioration is of great importance for minimizing suffering and serious adverse events. For early recognition, several physiological track and trigger systems have been developed, among them the early warning scores. These have been implemented in many places across the world but there is uncertainty about adherence to these systems. Aim: The aim of this study was to investigate adherence to Early warning score-systems and to evaluate what factors affect this adherence. Method: The study was conducted as a literature review including 14 articles with quantitative, qualitative and mixed-methods approaches. Searches were made in the PubMed, CINAHL and Web of Science databases. Content-analysis was used to identify themes. Results: Adherence seems higher to observations than to clinical responses. The main factors affecting adherence are the clinical experience of nurses, collaboration between professions, staffing, miscalculation, documentation and reporting. Conclusion: Adherence to EWS is lacking in many ways and several causes for this have been accounted for. Factors affecting adherence was the clinical experience of nurses, cooperation between professions, staffing, miscalculation, documentation and reporting.

The nursing culture of an inpatient pediatric unit was resistant to activating pediatric rapid response team (PRRT) alerts despite guidelines for activation. Nurses routinely assessed patients and assigned a pediatric early warning score (PEWS); however, the level of illness severity was not interpreted consistently among nurses and a PEWS action algorithm did not exist to guide nurses' minimal actions based on the PEWS score. Guided by 3 adult learning theories (Knowles, Kolb, and Bandura) and 1 evaluation model (Kirkpatrick), this staff education project sought to educate pediatric nurses on a PEWS action algorithm and determine whether this project improved nurses' knowledge, situational awareness, and attitude toward activating PRRT alerts. A convenience sample of 30 pediatric nurses completed a preeducation knowledge survey (EKS), attended an interactive PEWS education class, and completed a postEKS. After participating in the class, correct responses on the EKS increased from 43% to 82% and, using the Wilcoxon-signed rank test, a significant increase was noted in nurses' responses to questions related to self-efficacy, factual knowledge, and application. The overall increase in the nurses' self-efficacy and knowledge about the PEWS might enhance critical-thinking skills, foster identification of patients at risk for clinical deterioration, and empower nurses to follow the PEWS action algorithm including activation of PRRT alerts when indicated. This project has the potential to effect positive social change by supporting nurses' actions designed to improve pediatric patient outcomes.

Over one million high school students drop out of school each year in this country. Dropping out of school is a serious problem for the student, community, and the nation. Often dropouts are unable to compete in an increasingly technological society and face numerous consequences from their decision to leave school early including higher levels of poverty, unemployment, public assistance, incarceration, and poor health. Dropping out is a gradual process of school disengagement and related to individual, family, and school factors. In the past, it has been difficult to track individual student's progress through school and to determine accurate dropout and graduation rates. In 2005, the National Governors Association made a commitment to implement a uniform method to calculate and report graduates and dropouts as well as better data collections systems.This study intended to replicate aspects of other major studies around the county to determine the best early predictors of dropping out of school in this large school district in southern Arizona and use this information to build an early warning system. Student data were obtained from the district's Research and Accountability office for a cohort of students (n=6751) who began the ninth grade in fall 2006 and graduated or should have graduated in 2010. Data collected included general demographic information, academic data, number of schools attended, and school withdrawal codes.The intent of this research was to determine if there were statistically significant differences between dropouts and graduates in the variables collected and which variables yielded the highest effect sizes and should be included in the district's early warning system.Two analyses were used to determine significance differences between dropouts and graduates. Then four analyses were performed to determine the highest-yield variables for this district. Consistent with recent research in the field, the variables of ninth grade attendance, ninth grade English and Math grades, and GPA were the strongest predictors of student dropouts.Local educators can use this early warning information to help identify potential high school dropouts as early as possible and intervene more efficiently and effectively with these students.