ABSTRACT
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.
ABSTRACT
BACKGROUND: Public health campaigns aim to promote awareness, increase knowledge, and encourage a target population to adopt desirable attitudes and behaviors. Assessing their reach from a multidimensional perspective through information technology can facilitate the development of more effective campaigns in public health response. METHODS: We scrutinized seven data sources from different perspectives to assess a health campaign launched in Brazil named "Syphilis No!". This campaign is part of an Agenda for strategic actions to reduce syphilis in Brazil which includes dissemination of educommunication materials to remind people of the importance of syphilis prevention, emphasizing "test, treat and cure" concept. We developed a multidimensional analysis framework and implemented an information system to process the data from a time series perspective, and assessed the effects over time, both before and after the campaign. We descriptively analyzed data related to the campaign, including e-news, search engine activity, online courses, serological tests, medication distribution and case notification rates. FINDINGS: Regarding search engine activity, we observed the highest volume of search during the first week of campaigns in 2018 (between November 25th and December 7th). Nevertheless, analyzing this data in a trend plot revealed sustained growth until the end of 2019. From March 2018, the amount of e-news posts related to syphilis in Brazil, indexed by Google, followed an increasing slope, with a record peak in October 2019. In addition, data showed that 12 new online courses related to syphilis disease were available on the AVASUS Platform Learning Management System (LMS), to support efforts to promote lifelong learning for health professionals, teachers, and students. These courses reached more than 22,000 students between February 2019 and September 2020. Serological test data showed that the number of tests carried out in 2019 were 375·18% more than in 2015, even accounting for population growth. Finally, starting from the middle of 2018, the syphilis case notification rates followed a decreasing curve. INTERPRETATION: From this perspective, the "Syphilis No!" Project was a positive influence, inducing policy to fight syphilis in Brazil by supporting the implementation of a testing, treatment, and cure agenda (#TesteTrateCure). Certainly, this inference was made by analyzing multidimensional aspects and because, prior to 2018, the country had largely neglected this disease, with no records of communication actions during that period.
Subject(s)
Epidemics , Syphilis , Brazil/epidemiology , Health Promotion , Humans , Public Health , Syphilis/epidemiology , Syphilis/prevention & controlABSTRACT
Technological advances play an undeniable role in strengthening health systems. With regard to digital technologies, information systems and the analysis of health data are playing a growing role in health surveillance and preparing for and responding to disease outbreaks, the theme addressed by this article within the context of the Covid-19 pandemic in the State of Rio Grande do Norte. This study departs from the assumption that digital health interventions can increase Covid-19 response capacity. We developed a technology ecosystem that integrates different information systems to meet the needs outlined in international regulations governing the response to the pandemic. In addition to the main elements of the ecosystem, this article describes the application of this instrument by different institutional actors. The main decision making tool used in the state government's Covid-19 response, the ecosystem is a model for digital health interventions in Brazil's national health service. This experience in Rio Grande do Norte brings together elements that can contribute to studies investigating the resilience of health systems and analyzing health policies in emergency situations.
É inegável o papel dos avanços tecnológicos para o fortalecimento da saúde. No tocante às tecnologias digitais, trata do uso crescente dos sistemas de informação e análise de dados em saúde nas ações de preparo, vigilância e resposta a surtos epidemiológicos, tema abordado neste artigo no contexto da pandemia provocada pelo vírus Sars-CoV-2 no estado do Rio Grande do Norte. Este estudo parte do pressuposto de que é possível potencializar a gestão da resposta à Covid-19 por meio da saúde digital. Assim, a pesquisa desenvolveu um Ecossistema tecnológico que integra diferentes sistemas de informação para atender as necessidades previstas nas normativas internacionais frente à pandemia. Este artigo descreve, além do Ecossistema e sua estrutura, um conjunto de análises sobre a aplicação desse dispositivo por diversos atores institucionais. O Ecossistema foi a principal ferramenta em uso no estado para o processo decisório em resposta à Covid-19, sendo um modelo para a intervenção de saúde digital no Sistema Único de Saúde. A experiência do Rio Grande do Norte reúne, portanto, elementos que contribuem para os estudos sobre resiliência de sistemas e análises de políticas públicas em saúde em situações de emergência.
Subject(s)
COVID-19 , State Medicine , Brazil , Ecosystem , Humans , Pandemics , SARS-CoV-2 , TechnologyABSTRACT
Resumo É inegável o papel dos avanços tecnológicos para o fortalecimento da saúde. No tocante às tecnologias digitais, trata do uso crescente dos sistemas de informação e análise de dados em saúde nas ações de preparo, vigilância e resposta a surtos epidemiológicos, tema abordado neste artigo no contexto da pandemia provocada pelo vírus Sars-CoV-2 no estado do Rio Grande do Norte. Este estudo parte do pressuposto de que é possível potencializar a gestão da resposta à Covid-19 por meio da saúde digital. Assim, a pesquisa desenvolveu um Ecossistema tecnológico que integra diferentes sistemas de informação para atender as necessidades previstas nas normativas internacionais frente à pandemia. Este artigo descreve, além do Ecossistema e sua estrutura, um conjunto de análises sobre a aplicação desse dispositivo por diversos atores institucionais. O Ecossistema foi a principal ferramenta em uso no estado para o processo decisório em resposta à Covid-19, sendo um modelo para a intervenção de saúde digital no Sistema Único de Saúde. A experiência do Rio Grande do Norte reúne, portanto, elementos que contribuem para os estudos sobre resiliência de sistemas e análises de políticas públicas em saúde em situações de emergência.
Abstract Technological advances play an undeniable role in strengthening health systems. With regard to digital technologies, information systems and the analysis of health data are playing a growing role in health surveillance and preparing for and responding to disease outbreaks, the theme addressed by this article within the context of the Covid-19 pandemic in the State of Rio Grande do Norte. This study departs from the assumption that digital health interventions can increase Covid-19 response capacity. We developed a technology ecosystem that integrates different information systems to meet the needs outlined in international regulations governing the response to the pandemic. In addition to the main elements of the ecosystem, this article describes the application of this instrument by different institutional actors. The main decision making tool used in the state government's Covid-19 response, the ecosystem is a model for digital health interventions in Brazil's national health service. This experience in Rio Grande do Norte brings together elements that can contribute to studies investigating the resilience of health systems and analyzing health policies in emergency situations.
Subject(s)
Humans , State Medicine , COVID-19 , Technology , Brazil , Ecosystem , Pandemics , SARS-CoV-2ABSTRACT
Abstract Introduction Brazilian Telehealth Program was instituted by the Ministry of Health in 2007. Its initial structure was composed by nine telehealth centers administered by public higher education institutions. No standards, processes, applications or quality indicators had been defined since its creation. All this, combined with the decentralization of the centers, led each one of them to develop their own system, with different programming languages and architectures. The lack of regulation and integration of the information with the Ministry of Health made it difficult to evaluate the program. In this context, this paper describes the specification, implementation and validation of an architecture, entitled SMART, to integrate the various telehealth platforms developed by the centers. Such architecture aims to standardize information so that the Ministry of Health can monitor and evaluate the results of Telehealth actions. Methods SMART's architecture consists of four main components: a web tool for data manipulation; a web service to receive the center's production data; a component responsible for converting the received data into decision support data; and a component that collects data from external sources to compose the data warehouse. Results The architecture was validated with performance tests, which were executed under extreme workloads. The results of the experiments were summarized in order to attest SMART's effectiveness. Conclusion The analysis of the results obtained on real data shows that the project's performance remained stable under the workloads and its high quality was proven due to the absence of errors during the experiments.
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Introduction: The communication of information systems with biomedical devices has become complex not only due to the existence of several private communication protocols, but also to the immutable way that software is embedded into these devices. In this sense, this paper proposes a service-oriented architecture to access biomedical devices as a way to abstract the mechanisms of writing and reading data from these devices, thus contributing to enable the focus of the development team of biomedical software to be intended for its functional requirements, i.e. business rules relevant to the problem domain. Methods The SOA-BD architecture consists of five main components: A Web Service for transport and conversion of the device data, Communication Protocols to access the devices, Data Parsers to preprocess data, a Device Repository to store data and transmitted information and Error handling, for error handling of these information. For the development of SOA-BD, technologies such as the XML language and the Java programming language were used. Besides, Software Engineering concepts such as Design Patterns were also used. For the validation of this work, data has been collected from vital sign monitors in an Intensive Care Unit using HL7 standards. Results The tests obtained a difference of about only 1 second in terms of response time with the use of SOA-BD. Conclusion SOA-BD achieves important results such as the reduction on the access protocol complexity, the opportunity for treating patients over long distances, allowing easier development of monitoring applications and interoperability with biomedical devices from diverse manufacturers.
