Informática em saúde: relato da implantação da sistematização da assistência de enfermagem em um hospital do Vale do Taquari/RS, Brasil

O presente estudo tem por objetivo compartilhar experiências da implantação da Sistematização da Assistência de Enfermagem com o auxílio da informática em uma unidade de clínica médica de um hospital de pequeno porte do Vale do Taquari/RS, Brasil. Trata-se de um relato de experiência, descritivo e exploratório com abordagem qualitativa, onde se acompanhou nove profissionais de enfermagem durante a implantação da SAE com auxilio da informática em uma unidade de clínica médica de um hospital geral, a implantação do processo iniciou em setembro de 2016. Os resultados foram analisados conforme preconizado por Bardin. Verificaram-se algumas dificuldades na implantação da sistematização, como o desconhecimento pelo processo e dificuldades em manusear equipamentos de informática. Constataram-se também facilidades na utilização da informática durante a implantação, como a disponibilidade de informações e melhoria da comunicação entre a equipe multiprofissional. Melhorias como a segurança do cliente e otimização dos processos de trabalho também foram observados. Considera-se que o relato possui um limitante por contemplar pouco tempo de implantação do processo, no entanto conduz a uma discussão maior sobre o assunto. Observou-se a importância de uma formação qualificada para os profissionais de enfermagem frente ao uso desta metodologia de trabalho, bem como a qualificação da assistência e implantação facilitada com o uso da informática. &nbsp

New and remarkable leafhoppers and planthoppers (Hemiptera: Auchenorrhyncha) from Switzerland

Uploaded for Mitteilungen der Schweizerischen Entomologischen Gesellschaft by Plaz

Validation of the SNACOR clinical scoring system after transarterial chemoembolisation in patients with hepatocellular carcinoma

Background Transarterial chemoembolisation is the standard of care for intermediate stage (BCLC B) hepatocellular carcinoma, but it is challenging to decide when to repeat or stop treatment. Here we performed the first external validation of the SNACOR (tumour Size and Number, baseline Alpha-fetoprotein, Child-Pugh and Objective radiological Response) risk prediction model. Methods A total of 1030 patients with hepatocellular carcinoma underwent transarterial chemoembolisation at our tertiary referral centre from January 2000 to December 2016. We determined the following variables that were needed to calculate the SNACOR at baseline: tumour size and number, alpha-fetoprotein level, Child-Pugh class, and objective radiological response after the first transarterial chemoembolisation. Overall survival, time-dependent area under receiver-operating characteristic curves, Harrell’s C-index, and the integrated Brier score were calculated to assess predictive ability. Finally, multivariate analysis was performed to identify independent predictors of survival. Results The study included 268 patients. Low, intermediate, and high SNACOR scores predicted a median survival of 31.5, 19.9, and 9.2 months, respectively. The areas under the receiver-operating characteristic curve for overall survival were 0.641, 0.633, and 0.609 at 1, 3, and 6 years, respectively. Harrell’s C-index was 0.59, and the integrated Brier Score was 0.175. Independent predictors of survival included tumour size (P < 0.001), baseline alpha-fetoprotein level (P < 0.001) and Child-Pugh class (P < 0.004). Objective radiological response (P = 0.821) and tumour number (P = 0.127) were not additional independent predictors of survival. Conclusions The SNACOR risk prediction model can be used to identify patients with a dismal prognosis after the first transarterial chemoembolisation who are unlikely to benefit from further transarterial chemoembolisation. However, Harrell’s C-index showed only moderate performance. Accordingly, this risk prediction model can only serve as one of several components used to make the decision about whether to repeat treatment

Improving continuity of patient care across sectors: study protocol of a quasi-experimental multi-centre study regarding an admission and discharge model in Germany (VESPEERA)

Background: Hospitalisations are a critical event in the care process. Insufficient communication and uncoordinated follow-up care often impede the recovery process of the patient resulting in a high number of rehospitalisations and increased health care costs. The overall aim of this study is the development, implementation and evaluation of a structured programme (VESPEERA) to improve the admission and discharge process. Methods: We will conduct an open quasi-experimental multi-centre study with four intervention arms. A cohort selected from insurance claims data will serve as a control group reflecting usual care. The intervention will be implemented in 25 hospital departments and 115 general practices in 9 districts in Baden-Wurttemberg. Eligibility criteria for patients are: age &gt; 18 years, hospital admission or hospitalisation, insurance at the sickness fund “AOK Baden-Wurttemberg”, enrolment in general practice-centred care contract. Each study arm will receive different intervention components based on the point of study enrolment and the patient’s medical need. The interventions comprise a) a structured assessment in the general practice prior to admission resulting in an admission letter b) a discharge conversation by phone between hospital and general practice, c) a structured assessment and care plan post-discharge and d) telephone monitoring for patients with a high risk of rehospitalisation. The assessments are supported by a software tool (“CareCockpit”), originally developed for structured case management programmes. The primary outcome (rehospitalisation due to the same indication within 90 days) and a range of secondary outcomes (rehospitalisation due to the same indication within 30 days; hospitalisations due to ambulatory care-sensitive conditions; delayed prescription of medication and medical products/ devices and referral to other health practitioner/s after discharge; utilisation of emergency or rescue services within 3 months; average care cost per year and patient participating in the VESPEERA programme) and quality indicators will be determined based on insurance claims data and CareCockpit data. Additionally, a patient survey on satisfaction with cross-sectoral care and health related quality of life will be conducted. Discussion: Based on the results, area-wide implementation in usual care is well sought. This study will contribute to an improvement of cross-sectoral care during the admission and discharge process. Trial registration: DRKS00014294 on DRKS / Universal Trial Number (UTN): U1111–1210-9657, Date of registration 12/06/2018

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost