Background: In Brazil, many public hospitals face constant problems related to high demand vis-à-vis an overall scarcity of resources, which hinders the operations of different sectors such as the surgical centre, as it is considered one of the most relevant pillars for the proper hospital functioning, due to its complexity, criticality as well as economic and social importance. Proper asset management based on well-founded decisions is, therefore, a sine-qua-non condition for addressing such demands. However, subjectivity and other difficulties present in decisions make the management of hospital resources a constant challenge. Methods: Thus, the present work proposes the application of a hybrid approach, formed by the QFD tools, fuzzy logic and SERVQUAL as a decision support tool for the quality planning of the surgical centre of the Onofre Lopes Teaching Hospital (Hospital Universitário Onofre Lopes—HUOL). To accomplish such objective, it was necessary to discover and analyse the main needs of the medical team working in the operating room, through the application of the SERVQUAL questionnaire, associated with fuzzy logic. Results: Then, the most relevant deficiencies were transformed into entries for the QFD-fuzzy, where they were translated into project requirements. Soon after, the analysis of the existing relationships between the inputs and these requirements was carried out, generating the ranking of actions with the greatest impact on the improvement of the surgical centre overall quality. Conclusions: As a result, it was found that the proposed methodology can optimize the decision process to which hospital managers are submitted, improving the surgical centre operation efficiency.

The COVID-19 pandemic is already considered one of the biggest global health crises. In Rio Grande do Norte, a Brazilian state, the RegulaRN platform was the health information system used to regulate beds for patients with COVID-19. This article explored machine learning and deep learning techniques with RegulaRN data in order to identify the best models and parameters to predict the outcome of a hospitalized patient. A total of 25,366 bed regulations for COVID-19 patients were analyzed. The data analyzed comes from the RegulaRN Platform database from April 2020 to August 2022. From these data, the nine most pertinent characteristics were selected from the twenty available, and blank or inconclusive data were excluded. This was followed by the following steps: data pre-processing, database balancing, training, and test. The results showed better performance in terms of accuracy (84.01%), precision (79.57%), and F1-score (81.00%) for the Multilayer Perceptron model with Stochastic Gradient Descent optimizer. The best results for recall (84.67%), specificity (84.67%), and ROC-AUC (91.6%) were achieved by Root Mean Squared Propagation. This study compared different computational methods of machine and deep learning whose objective was to classify bed regulation data for patients with COVID-19 from the RegulaRN Platform. The results have made it possible to identify the best model to help health professionals during the process of regulating beds for patients with COVID-19. The scientific findings of this article demonstrate that the computational methods used applied through a digital health solution, can assist in the decision-making of medical regulators and government institutions in situations of public health crisis.

Bed regulation within Brazil’s National Health System (SUS) plays a crucial role in managing care for patients in need of hospitalization. In Rio Grande do Norte, Brazil, the RegulaRN Leitos Gerais platform was the information system developed to register requests for bed regulation for COVID-19 cases. However, the platform was expanded to cover a range of diseases that require hospitalization. This study explored different machine learning models in the RegulaRN database, from October 2021 to January 2024, totaling 47,056 regulations. From the data obtained, 12 features were selected from the 24 available. After that, blank and inconclusive data were removed, as well as the outcomes that had values other than discharge and death, rendering a binary classification. Data was also correlated, balanced, and divided into training and test portions for application in machine learning models. The results showed better accuracy (87.77%) and recall (87.77%) for the XGBoost model, and higher precision (87.85%) and F1-Score (87.56%) for the Random Forest and Gradient Boosting models, respectively. As for Specificity (82.94%) and ROC-AUC (82.13%), the Multilayer Perceptron with SGD optimizer obtained the highest scores. The results evidenced which models could adequately assist medical regulators during the decision-making process for bed regulation, enabling even more effective regulation and, consequently, greater availability of beds and a decrease in waiting time for patients.

Diante da contribuição dos sistemas Instrumentados de Segurança (SIS), em especial os sistemas de alta integridade para proteção à alta pressão (HIPPS), permitindo que diferentes processos sejam levados à níveis cada vez mais seguros, o presente estudo buscou analisar a disponibilidade de um HIPPS em diferentes configurações por meio da influência da arquitetura dos seus componentes, frequências de testes e tempo médio entre os reparos. Para tanto, nos procedimentos metodológicos, foi definido um modelo base de arquitetura para posteriormente serem analisadas diferentes propostas. Tais análises foram subsidiadas pela identificação das variáveis no manual de dados “Reliability data for Safety Instrumented Systems” e modelagens matemáticas realizadas por meio de equações propostas no manual do método PDS “Reliability Prediction Method for Safety Instrumented Systems PDS Method Handbook, 2010 Edition”, podendo, assim, realizar análises comparativas de tais influências. Como achados da pesquisa, está a verificação de que, de acordo com os cenários estudados, as medidas de tempo médio para reparo e frequência de testes se mostraram mais representativas para a indisponibilidade do sistema do que as variações de arranjos. E, ainda, que o aumento da frequência de teste fomenta a elevação do PFD de falhas independentes; assim como o HIPPs na filosofia degradada, em geral, apresentou uma contribuição muito baixa, sendo irrisória para a análise final da disponibilidade do sistema. Entretanto, quando elevado o MTTR, tais falhas mostram suas importâncias para tais funções. Por fim, como suposto por Marszal e Scharpf (2002), confirmou-se que acionamentos espúrios são mais relevantes em arquiteturas redundantes, independente de tempos de testes e tempos entre reparos.

Background: Digital systems are central to regulating access to care in Brazil’s Unified Health System (SUS), but fragmented data can hinder coordination. Objective: This study examines the database structure of SUSfácilMG and identifies modernization priorities, using CORE Saúde MG as a comparative reference. Methods: We assessed metadata from 1,586 SUSfácilMG tables, focusing on naming patterns, relationships, and repeated attributes. We then compared architectural features described for CORE Saúde MG. Results: Approximately 40% of the assessed tables repeated descriptive attributes, including patient identifiers, suggesting substantial duplication. Metadata alone cannot establish operational failures, security incidents, or effects on care. Conclusion: A phased review of data models, interfaces, and governance is warranted. Standardized exchange, referential integrity, and controlled access should be evaluated alongside service continuity and data quality. Author summary: Brazil’s public health system relies on digital platforms to organize access to specialized care. In Minas Gerais, SUSfácilMG has supported this process for many years, accumulating adaptations that are reflected in the database structure. We examined the metadata of 1,586 SUSfácilMG tables, focusing on naming patterns, apparent relationships, and recurring descriptive attributes. Approxim 40% of the tables contained recurring patient-related attributes, indicating struc complexity that may complicate data maintenance and system integration. Comparison with published descriptions of CORE Saúde MG highlighted opportunities for phased modernization based on shared data structures, interoperability, and governance. Because the analysis was limited to metadata, findings identified structural opportunities rather than evidence of data inconsist security incidents, or effects on patient care.