RESUMO Objetivo verificar as causas da não conferência do carro de emergência e o efeito sobre a taxa de adesão, por meio do emprego de ferramentas da qualidade em uma Unidade de Terapia Intensiva Neonatal. Método pesquisa mista do desenho exploratório sequencial, desenvolvida com enfermeiros e fonte documental na Unidade de Terapia Intensiva Neonatal de hospital universitário, em três etapas: 1) Brainstorming para levantamento das causas de não conferência/construção de Lista de Verificação; 2) Coleta/análise de dados pela aplicação da Lista de Verificação e Diagrama de Pareto; 3) Análise documental. Utilizou-se o teste qui-quadrado para verificar o efeito do emprego das ferramentas de qualidade na adesão à conferência. Resultados 13 causas de não conferência do carro de emergência foram identificadas, sendo oito evitáveis e cinco não evitáveis. As causas evitáveis (n=63) representaram 87,5%, sendo as principais: falta de hábito (n=17; 27%), priorizar atividades assistenciais (n=17; 27%) e realizar divisão do cuidado dos pacientes/priorizar atividades administrativas (n=9; 14,3%). A aplicação das ferramentas da qualidade teve efeito significativo (p-valor<0,001) na adesão à conferência. Conclusão e implicações para a prática o emprego das ferramentas da qualidade foi factível para a identificação causal da não conferência do carro de emergência e melhoria na sua adesão.

Background We propose a mathematical model formulated as a finite-horizon Markov Decision Process (MDP) to allocate capacity in a radiology department that serves different types of patients. To the best of our knowledge, this is the first attempt at considering radiology resources with different capacities and individual no-show probabilities of ambulatory patients in an MDP model. To mitigate the negative impacts of no-show, overbooking rules are also investigated. Methods The model’s main objective is to identify an optimal policy for allocating the available capacity such that waiting, overtime, and penalty costs are minimized. Optimization is carried out using traditional dynamic programming (DP). The model was applied to real data from a radiology department of a large Brazilian public hospital. The optimal policy is compared with five alternative policies, one of which resembles the one currently used by the department. We identify among alternative policies the one that performs closest to the optimal. Results The optimal policy presented the best performance (smallest total daily cost) in the majority of analyzed scenarios (212 out of 216). Numerical analyses allowed us to recommend the use of the optimal policy for capacity allocation with a double overbooking rule and two resources available in overtime periods. An alternative policy in which outpatients are prioritized for service (rather than inpatients) displayed results closest to the optimal policy, being also recommended due to its easy implementation. Conclusions Based on such recommendation and observing the state of the system at any given period (representing the number of patients waiting for service), radiology department managers should be able to make a decision (i.e., define number and type of patients) that should be selected for service such that the system’s cost is minimized.

Background Patients' no‐shows negatively impact healthcare systems, leading to resources' underutilisation, efficiency loss, and cost increase. Predicting no‐shows is key to developing strategies that counteract their effects. In this paper, we propose a model to predict the no‐show of ambulatory patients to exam appointments of computed tomography at the Radiology department of a large Brazilian public hospital. Methods We carried out a retrospective study on 8382 appointments to computed tomography (CT) exams between January and December 2017. Penalised logistic regression and multivariate logistic regression were used to model the influence of 15 candidate variables on patients' no‐shows. The predictive capabilities of the models were evaluated by analysing the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC). Results The no‐show rate in computerised tomography exams appointments was 6.65%. The two models performed similarly in terms of AUC. The penalised logistic regression model was selected using the parsimony criterion, with 8 of the 15 variables analysed appearing as significant. One of the variables included in the model (number of exams scheduled in the previous year) had not been previously reported in the related literature. Conclusions Our findings may be used to guide the development of strategies to reduce the no‐show of patients to exam appointments.

The Mato Grosso State is the main producer of corn of the Brazil and its production has been increasing every year. In this sense, is very important to gain information about future production to planning and monitoring of the corn crops. In this way, the main aim of this paper is to compare the performance showed by the forecast models of time series and to choose the best model. The historical data of corn crop from 1976/1977 to 2017/2018 was obtained with CONAB (The Brazilian National Supply Company). Then, the time series pattern was analyzed, as well as the descriptive statistics of the data obtained. Subsequently, electronic spreadsheets were developed for application and analysis of the evaluated models. With the results it was verified that the trend exponential smoothing model (Holt's linear model) presented the smallest prediction errors, and then it was selected to predict the next seven crops (from 2018/2019 to 2024/2025). The forecast obtained by this model for the 2024/2025 crop indicates that total corn production in the state of Mato Grosso will increase by approximately 70% compared to the 2017/2018 crop production.