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.

Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2017-04-19T15:55:11Z No. of bitstreams: 1 Fábio de Oliveira Dias_.pdf: 3212992 bytes, checksum: 687279fc82a1e707a3ba09c241b5e09b (MD5) / Made available in DSpace on 2017-04-19T15:55:11Z (GMT). No. of bitstreams: 1 Fábio de Oliveira Dias_.pdf: 3212992 bytes, checksum: 687279fc82a1e707a3ba09c241b5e09b (MD5) Previous issue date: 2017-02-17 / Nenhuma / A computação móvel e ubíqua tem propiciado o advento de soluções que permitem o monitoramento em tempo real de sinais provenientes de sensores e o seu processamento por aplicações que podem executar ações de acordo com as condições encontradas. Esta característica possibilita o uso da tecnologia para o monitoramento de condições de saúde de pacientes, denominado de cuidados ubíquos. Em diversas situações, a fim de salvar vidas de pacientes, é necessária a análise de seus sinais vitais de forma a prevenir eventuais colapsos. Este trabalho se insere nestas condições, estando voltado para a antecipação de ações de times de resposta rápida baseado em análise preditiva, propondo o modelo Predictvs. Um Time de Resposta Rápida busca prevenir mortes de pacientes que tenham piora clínica fora de ambientes de Unidades de Tratamento Intensivo em hospitais. De forma diversa dos trabalhos relacionados, que se preocupam apenas com ambientes de tratamento intensivo, o modelo Predictvs busca antecipar ações dos times de resposta rápida, através da análise dos sinais vitais dos pacientes com o uso de escores de alerta precoce e regressão linear. A contribuição científica do modelo é dada em virtude da possibilidade de efetuar a predição em tempo real de possíveis situações de colapso dos pacientes através do monitoramento e análise dos sinais vitais. A avaliação do Predictvs foi efetuada com a utilização de cenários, com a implementação de um protótipo e através de diversas simulações. Análises efetuadas com cerca de 228000 medições provenientes de um dataset público apresentaram bons resultados, onde a precisão da predição para a medição seguinte se mostrou bastante alta, atingindo mais de 99% no caso da frequência cardíaca e 100% na saturação de oxigênio arterial, ultrapassando 95% nos demais sinais vitais. Além disso, o índice de falsos negativos foi consideravelmente baixo, atingindo menos de 1% na frequência cardíaca e na saturação de oxigênio arterial. O índice de falsos positivos também foi baixo, embora não tanto quanto o de falsos negativos. No entanto, predições para três ou mais medições futuras mostram queda na precisão (mesmo demonstrando valores de acerto relativamente expressivos, com diversos sinais fisiológicos acima de 98%) e aumento do número de falsos negativos e, principalmente, de falsos positivos. / The mobile and ubiquitous computing has allowed the emergence of solutions that enable real-time monitoring of signals coming from sensors and processing for applications that can perform actions according to the conditions found. This feature enables the use of this technology for monitoring health conditions of patients, called ubiquitous healthcare. In several situations, in order to save his lives, it is necessary to analyze the vital signs of patients to prevent any collapses. This work is part of these conditions and is aimed at anticipating the actions of rapid response teams based on predictive analysis, proposing the Predictvs model. A Rapid Response Team intends to prevent deaths in patients who have clinical deterioration outside of intensive care units in hospitals environments. Differently of related works, which are concerned only with intensive care environments, the Predictvs model seeks to anticipate the actions of teams of rapid response through the analysis of vital signs of patients with the use of early warning scores and linear regression. The scientific contribution of the presented model is that we could better predict possible collapse situations of patients, through the monitoring and analysis of vital signs. The Predictvs evaluation was performed with the use of scenarios, implementation of a prototype and several simulations. Analyzes performed with about 228,000 measurements from a public dataset showed good results, where the accuracy of the prediction for the next measurement was very high, reaching more than 99% in the case of heart rate and 100% in arterial oxygen saturation, surpassing 95% in other vital signs. In addition, the false negative index was considerably lower, reaching less than 1% in heart rate and arterial oxygen saturation. The rate of false positives was also low, although not so much as that of false negatives. However, predictions for three or more future measurements show a drop in accuracy (even showing relatively expressive set values with several physiological signals above 98%) and an increase in the number of false negatives and, mainly, false positives.