Artificial Intelligence based prediction system for General Medicine

COVID-19 is highly infectious and has been extensively spread worldwide, with approximately 651 million definite cases crosswise the globe including Pakistan. At that era of pandemic where patients are not able to approach a doctor for even the routine checkups, in such curial situation even normal disease checkups are ignored by many families due to pandemic situations, those diseases may lead to be a perilous disease are results of it. Human disorders portray scenarios that even disturb or permanently cutoff the essential functions of a body parts. Consequently, the aim is to transform raw health data potential into actionable insights to applying the promising outcomes of Body Sensor Network (BSN) and State-of-Art Artificial Intelligence (AI) techniques to get proper medicine allocation to the particular health state of patient. In this paper the different techniques of Deep Learning and Machine Learning introduced to predict the actual medicine for the specific health state of patient according to data from the BSN. Experiments have been conducted on large dataset which shepherd it into 16 states of patient's health which will allotted to AI model to predict the medicine accordingly to the health state of patient. Experimental results show the 87.46% by Random Forest, 92.74% by K-Nearest Neighbors, 74.57% by Naive Bayes, 94.41% by Extreme Gradient Boost, 84.88% by Multi-Layer Perceptron in terms of precision of model training in event of classification. © 2023 IEEE.

Practical Implications of Online Learning with Nurses During Coronavirus Disease 2019.

This study explores how teaching practices were impacted by coronavirus disease 2019 (COVID-19) in a fully online nursing program. In-depth interviews were conducted of faculty in an online nursing program. The findings of the study suggest that the COVID-19 pandemic was challenging for the faculty members, but still they considered it an opportunity for personal and professional growth; they used this disruption to humanize their teaching practices by focusing on self-care to support their students in the challenging times of a global pandemic. The members also mentioned institutional support was crucial for their perseverance, growth, and job satisfaction.

More education, less mortality: The role of the employer in a more highly educated nursing workforce

There is a problem in the United States regarding the percentage of bachelor-prepared nurses in the workforce. The primary purpose of this study was to explore the role of the employer in a more educated nursing workforce through the lens of connectivism. A mixed methods study was conducted during the COVID-19 pandemic in the fall of 2021. Chief nursing officers (n = 89) from hospitals and long-term care facilities across one southeastern state were surveyed regarding the percentage of Bachelor of Science in Nursing (BSN)-prepared registered nurses (RNs) in their facilities. Fisher's exact test (r x 2) was conducted to determine if there was a significant association between the reported percentage of BSN-prepared RNs and the plans, policies, and promotions of the employer, as well as the location and type of facility. Findings suggest that the employer does play a role in the education level of nurses. There was a significant association between the reported percentage of BSN-prepared RNs and the presence of plans and goals for the facility suggesting that the employers' plans and goals increase the percentage of BSN-prepared RNs in a facility. There was also a significant association between the reported percentage of BSN-prepared RNs and the type of facility suggesting that there are more BSN-prepared RNs in the hospital setting than in the long-term care setting. An additional significant association was found related to magnet status suggesting that maintaining or pursuing magnet status does increase the percentage of BSN-prepared RNs in a facility. Four main themes evolved during the coding of the qualitative data. The four main themes were: 1) the need for more nurses, 2) the need for more resources, 3) the need for partnerships, and 4) the ramifications of the pandemic. The implications for practice included raising awareness regarding the need for more BSN-prepared RNs, developing goals related to this initiative, and forming partnerships with key stakeholders. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

RN to BSN Jump Start to Success

As part of the call to increase the number of registered nurses prepared with a Bachelor of Science in Nursing degree coupled with barriers faced in nursing education due to COVID-19, one for-profit university launched an registered nurses to Bachelor of Science in Nursing Jump Start to Success initiative. The purpose of this descriptive review of an educational innovation was to determine if outcomes between Jump Start students and matriculated students would be equal in the program. The Jump Start initiative enrolled non-matriculating students who were awaiting graduation and successful National Council of State Boards of Nursing completion by offering two courses tuition-free. During the analysis period, data revealed the first course mean final numeric grade for matriculated students was 94.5 compared to 95.0 for Jump Start students (x2 = 0.2064, p = 0.649588;t-test = -5.11, p = 1.84). Final numeric grades for both groups had a normal bell curve distribution. Additionally, 55% of Jump Start students matriculated into the registered nurses to Bachelor of Science in Nursing Online Degree Completion Option. The Jump Start initiative has provided a foundation on which future initiatives can be built that promote seamless academic progression. Educational institutions should continue to collaborate to develop creative and innovative enrollment strategies.

Anxiety and depression and the related factors in nurses of Guilan University of Medical Sciences hospitals during COVID-19: A web-based cross-sectional study.

BACKGROUND: The outbreak of COVID-19 is an unprecedented challenge to the health systems in Iran. We aimed to assess the psychological impact of this outbreak on nurses in the hospitals of Guilan University of Medical Sciences that is one of the top provinces of incidence of COVID-19. METHODS: In a web-based cross-sectional study, 441 nurses working were selected from the hospitals, from 7 to 12 April 2020. Anxiety and depression were measured using the Generalized Anxiety Disorder-7 and the Patient Health Questionnaire-9, respectively. Simple and multiple logistic regression models were used to identify the factors related to anxiety and depression. RESULTS: The majority were in contact with suspected or confirmed COVID-19 cases (93.4%) and their relatives had been infected with COVID-19 (42%). The mean of anxiety-7 and depression total scores were 8.64 ± 5.60 and 8.48 ± 6.19, respectively. Female (OR = 3.27, 95% CI = 1.01-10.64), working in COVID-19 designated hospital (OR = 1.82, 95% CI = 1.13-2.93), being suspected with COVID-19 infection (OR = 2.01, 95% CI = 1.25-3.26), and insufficient personal protective equipment (OR = 2.61, 95% CI = 1.68-4.06) were associated with anxiety. Depression was significantly associated with female sex (OR = 4.62, %95 CI = 1.24-17.16), having chronic disease (OR = 2.12, 95% CI = 1.20-3.74), being suspected or confirmed with COVID-19 infection (OR = 3.44, 95% CI = 2.11-5.59, and OR = 2.21, 95% CI = 1.04-4.70, respectively), and insufficient personal protective equipment (OR = 1.86, 95% CI = 1.19-2.91). CONCLUSION: The finding declares healthcare workers are at high risk for mental illness. Continuous supervision of the psychological consequences following infectious diseases outbreaks should be a part of the preparedness efforts of health care systems.