Integrating nursing informatics into undergraduate nursing education in Africa: a scoping review

Background: Information and communication technologies have become omnipresent in healthcare systems globally, and since nurses comprise the majority of the health sector workforce, they are expected to be adequately skilled to work in a technology-mediated environment. Integrating nursing informatics into undergraduate nursing education is a cornerstone to nursing education and practice in Africa. Aim: This scoping review aimed to evidence the integration of nursing informatics into undergraduate nursing education in Africa. Methods: A scoping review of the literature used electronic databases including CINAHL Plus databases; EmCare; MEDLINE Ovid; Scopus; ERIC ProQuest; Web of Science; Google; and Google Scholar to locate papers specific to the African context. From a total of 8723 articles, 19 were selected for critique and synthesis. Results: Selected studies indicated that nursing students used several information and communication technologies tools primarily for academic purposes, and rarely for clinical practice. In Africa, the challenges for teaching informatics in nursing education included: limited information and communication technologies skills among faculty and students; poor teaching strategies; and a lack of standardization of nursing informatics competencies. Successful integration of nursing informatics into undergraduate nursing education in African countries depends on restructuring nursing informatics content and teaching strategies, capacity building of the faculty and students in information and communication technologies, political commitment, and collaborative partnership. Conclusion: Nursing informatics is scarce in undergraduate nursing education in Africa due to the implementation and adoption challenges. Responding to these challenges requires a multi-sectoral approach in the revision of undergraduate nursing curricula. Implication for nursing education, practice, policy and research: This study highlights the importance of nursing informatics in undergraduate nursing education, with its challenges and success. Nursing education policies should support the development of well-standardized nursing informatics content and appropriate teaching strategies to deliver it. Further research is needed to establish which aspects of nursing informatics are integrated into undergraduate nursing education and nursing practice, implementation process, challenges and possible solutions. Collaborative partnerships are vital to developing nursing informatics policies to better prepare graduate nurses for the African healthcare workforce in the digital era

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Mechanisms of Spontaneous Curvature Inversion in Compressed Graphene Ripples for Energy Harvesting Applications via Molecular Dynamics Simulations

Electrically conductive, highly flexible graphene membranes hold great promise for harvesting energy from ambient vibrations. For this study, we built numerous three-dimensional graphene ripples, with each featuring a different amount of compression, and performed molecular dynamics simulations at elevated temperatures. These ripples have a convex cosine shape, then spontaneously invert their curvature to concave. The average time between inversion events increases with compression. We use this to determine how the energy barrier height depends on strain. A typical convex-to-concave curvature inversion process begins when the ripple’s maximum shifts sideways from the normal central position toward the fixed outer edge. The ripple’s maximum does not simply move downward toward its concave position. When the ripple’s maximum moves toward the outer edge, the opposite side of the ripple is pulled inward and downward, and it passes through the fixed outer edge first. The ripple’s maximum then quickly flips to the opposite side via snap-through buckling. This trajectory, along with local bond flexing, significantly lowers the energy barrier for inversion. The large-scale coherent movement of ripple atoms during curvature inversion is unique to two-dimensional materials. We demonstrate how this motion can induce an electrical current in a nearby circuit

Towards Automated Diagnosis with Attentive Multi-modal Learning Using Electronic Health Records and Chest X-Rays

Jointly learning from Electronic Health Records (EHR) and medical images is a promising area of research in deep learning for medical imaging. Using the context available in EHR together with medical images can lead to more efficient data usage. Recent work has shown that jointly learning from EHR and medical images can indeed improve performance on several tasks. Current methods are however still not independent of clinician input. To obtain an automated method only prior patient information should be used together with a medical image, without the reliance on further clinician input. In this paper we propose an automated multi-modal method which creates a joint feature representation based on prior patient information from EHR and associated X-ray scan. This feature representation, which joins the two different modalities through attention leverages the contextual relationship between the modalities. This method is used to perform two tasks: diagnosis classification and free-text diagnosis generation. We show the benefit of the multi-modal approach over single-modality approaches on both tasks

Treating persistent asthma in rural Rwanda: characteristics, management and 24-month outcomes

SETTING: In 2007, the Rwandan Ministry of Health, with support from Partners In Health, introduced a district-level non-communicable disease programme that included asthma care. OBJECTIVE: To describe the demographics, management and 24-month outcomes of asthma patients treated at three rural district hospitals in Rwanda. DESIGN: We retrospectively reviewed electronic medical records of asthma patients enrolled from January 2007 to December 2012, and extracted information on demographics, clinical variables and 24-month outcomes. RESULTS: Of the 354 patients, 66.7% were female and 41.5% were aged between 41 and 60 years. Most patients (53.1%) were enrolled with moderate persistent asthma, 40.1% had mild persistent asthma and 6.8% had severe persistent asthma. Nearly all patients (95.7%) received some type of medication, most commonly a bronchodilator. After 24 months, 272 (76.8%) patients were still alive and in care, 21.1% were lost to follow-up, 1.7% had died and 0.3% had transferred out. Of the 121 patients with an updated asthma classification at 24 months, the severity of their asthma had decreased: 17.4% had moderate and 0.8% had severe persistent asthma. CONCLUSION: Our findings show improvements in asthma severity after 24 months and reasonable rates of loss to follow-up, demonstrating that asthma can be managed effectively in rural, resource-limited settings.</p