Objective: To develop a registration standard with diagnoses, outcomes and nursing interventions for an Emergency Care Unit. Method: This is applied research of technological development developed in three steps: elaboration of diagnoses/outcomes and interventions statements following the International Classification for Nursing Practice; assessment of diagnosis/outcome relevance; organization of diagnosis/outcome and interventions statements according to health needs described in TIPESC. Results: A total of 185 diagnoses were prepared, of which 124 (67%) were constant in the classification, and 61 had no correspondence. Of the 185 diagnoses, 143 (77%) were rated as relevant by 32 experienced emergency room nurses, and 495 nursing interventions were correlated to diagnoses/outcomes. Conclusion: It was possible to build a record standard for the Emergency Care Unit following standardized terminology, containing diagnostic statements/outcomes and relevant interventions for nursing practice assessed by nurses with practice in emergency.
RESUMO Objetivo: analisar o atendimento pela telemedicina em Vitória/ES de abril/2020 a mar/2021. Método: estudo de caso ancorado na categoria acesso de Thiede et al. e em dados secundários. Utilizaram-se relatórios das consultas de telemedicina da Rede Bem Estar. Incluíram-se todas as 29 Unidades Básicas de Saúde do município. Resultados: no período foram atendidos 15.548 usuários, 64% do sexo feminino (9.953) e 36% do masculino (5.595), em 21.481 consultas. O grupo etário mais atendido foi o de 30-39 anos (19,5%). O número por 10.000 hab. para todas as causas oscilou entre 35,86/10.000 hab. de out-dez/2020 e 65,75 de abr-jun/2020. Destes atendimentos, 56% (11.946) foram coronavírus (causas B342 e B972), sendo, 22,54 consultas por 10.000 hab. de out-dez/2020 e 31,96 de abr-jun/2020. Conclusões: Os resultados refletem o impacto transformador da Covid-19 nos cuidados à saúde por telemedicina como parte da resposta de primeira linha à pandemia no município de Vitória/ES. As desigualdades no acesso presencial se reproduzem na telemedicina, o que torna imprescindível manter um relacionamento forte entre o sistema de saúde, as equipes de saúde e os usuários na implantação da telemedicina. As duas formas permanecem interdependentes e complementares na busca de garantia do acesso equitativo em saúde.
Objective: to analyze telemedicine care in Vitória, Espírito Santo, Brazil, from April 2020 to March 2021. Method: based on Thied et al.’s dimensions of access, a case study was conducted using secondary data collected from the Bem Estar Network’s telemedicine reports. All 29 Basic Health Units of the municipality were included. Results: a total of 15,548 users were assisted in 21,481 consultations, 64% female (9,953) and 36% male (5,595). The most attended age group was 30-39 years old (19.5%). The number per 10,000 inhabitants for all causes ranged between 35.86/10,000 inhabitants from Oct-Dec/2020 and 65.75 from Apr-Jun/2020. Of these calls, 56% (11,946) targeted coronavirus (causes B342 and B972), ranging from 22.54 consultations per 10,000 inhabitants in Oct-Dec/2020 to 31.96 in Apr-Jun/2020. Conclusions: Results reflect the transformative impact COVID-19 had on telemedicine care as part of the first-line response to the pandemic in Vitória, Brazil. Inequalities in face-to-face access are reproduced in telemedicine, making it essential to maintain a strong relationship between the health system, health teams, and users when implementing telemedicine. Both forms of health care remain interdependent and complementary in the search to ensure equitable access to health.
RESUMO Objetivo: Elaborar um padrão de registro com diagnósticos, resultados e intervenções de enfermagem para Unidade de Pronto Atendimento. Método: Pesquisa aplicada de desenvolvimento tecnológico, desenvolvida em três etapas: elaboração dos enunciados diagnósticos/resultados e intervenções seguindo a Classificação Internacional para Prática de Enfermagem; avaliação da relevância dos diagnósticos/resultados; organização dos enunciados diagnóstico/resultado e intervenções, conforme necessidades de saúde descritas na TIPESC. Resultados: Foram elaborados 185 diagnósticos, dos quais 124 (67%) eram constantes na classificação, e 61 não tinham correspondência. Dos 185 diagnósticos, 143 (77%) foram avaliados como relevantes por 32 enfermeiros experientes em urgência e emergência, e 495 intervenções de enfermagem foram correlacionadas aos diagnósticos/resultados. Conclusão: Foi possível construir um padrão de registro para Unidade de Pronto Atendimento seguindo terminologia padronizada, contendo enunciados diagnósticos/resultado e intervenções relevantes para prática de enfermagem avaliada por enfermeiros com prática em urgência e emergência.
Introduction: the Coronavirus Disease 2019 (COVID-19) is a viral disease which has been declared a pandemic by the WHO. Diagnostic tests are expensive and are not always available. Researches using machine learning (ML) approach for diagnosing SARS-CoV-2 infection have been proposed in the literature to reduce cost and allow better control of the pandemic. Objective: we aim to develop a machine learning model to predict if a patient has COVID-19 with epidemiological data and clinical features. Methods: we used six ML algorithms for COVID-19 screening through diagnostic prediction and did an interpretative analysis using SHAP models and feature importances. Results: our best model was XGBoost (XGB) which obtained an area under the ROC curve of 0.752, a sensitivity of 90%, a specificity of 40%, a positive predictive value (PPV) of 42.16%, and a negative predictive value (NPV) of 91.0%. The best predictors were fever, cough, history of international travel less than 14 days ago, male gender, and nasal congestion, respectively. Conclusion: We conclude that ML is an important tool for screening with high sensitivity, compared to rapid tests, and can be used to empower clinical precision in COVID-19, a disease in which symptoms are very unspecific.
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