Using real-time data to improve patient no-shows and late cancellations

Background: The Cystic Fibrosis Foundation (CFF) advises that all persons with cystic fibrosis (PwCF) visit a cystic fibrosis (CF) care center every 3 months for evaluation, treatment, surveillance, counseling, and education [1]. In March 2020, our clinic went into modified operations in response to the COVID-19 pandemic, necessitating a temporary change in our ability to conduct routine face-to-face visits. Within 1 month, we operationalized virtual visits in addition to face-to-face visits. During the pandemic, staff noticed a drop in clinic attendance, and we implemented a quality improvement (QI) plan to study and address this trend. Method(s): Our QI team is a multidisciplinary group that is part of the Cystic Fibrosis Learning Network (CFLN). We defined the clinic fill rate (CFR) as the number of people seen over the number of available clinic slots. Each week, we determined the number of PwCF scheduled the following week and compared that number with no-shows and cancellations that occurred during that 7-day period. We also determined the number of PwCF scheduled 1 month ahead to compare it with weekly data. We used a key driver diagram to help focus our interventions (change ideas). Using run charts, we analyzed data each week to identify trends and variances. We used plan-do-study-act cycles and implemented initial interventions centered on publicizing CFF follow-up guidelines in town hall meetings, emails, and newsletters. We later identified PwCF who had a no-show history, and before clinics, our social worker communicated with each family (telephone or text) to remind them of the upcoming visit and identify any barriers to attending. During our study, Oregon experienced a surge in COVID-19 cases from the omicron variant, andwe overlaid our data with a graph of cases. Result(s): CFR was measured in 598 encounters over 28 weeks. CFR 1 month in advancewas 79%. In theweek before clinic, CFRwas 84%. After theweek, overall CFR was 66% (68% for face-to-face visits, 58% for virtual visits). Fifteen percent of our cancellations were COVID-related (increasing to 21% during the surge), but CFR did not change during the surge. After our intervention, those contacted in advance came to clinic 93% of the time, and our CFR improved to 74.8%. Conclusion(s): An 84% CFR, measured 1 week ahead of clinic, was dropping to an average of 66% because of late cancellations and no-shows, and widespread education about clinic attendance guidelines did not increase the rate. Having our social worker communicate directly with PwCF increased the overall CFR closer to our advance numbers, and 93% came to clinic. These communications also served as an additional patient interaction during which other social work needs were identified. Overall reduced clinic attendance may be related to the indirect impact of the pandemic and benefits of modulator therapy.We need to gather more postimplementation data and to consider different approaches to partnering with PwCF to achieve ideal follow-up.Copyright © 2022, European Cystic Fibrosis Society. All rights reserved

Study of Deep Learning Approaches for Diagnosing Covid-19 Disease using Chest CT Images

Since March 2020, the World Health Organization (WHO) has declared COVID-19 a pandemic. An evolving viral infection with respiratory tropism causes atypical pneumonia. Experts believe that detecting COVID-19 early stage is crucial. Early diagnosis and tracking techniques have become increasingly important to ensure an accelerated treatment process and avoid virus spread. Images from Computed Tomography (CT) scans can provide quick and precise COVID-19 screening. A subdivision of Machine Learning (ML) called Deep Learning (DL) can improve diagnostic accuracy and speed by automating screening via medical imaging in collaborative efforts with radiologists and physicians This study aims to investigate the recently popularized and extensively discussed deep learning algorithms for COVID-19 diagnosis in connection to the sequence phases involved in image processing. Getting rid of the noise in these images requires some preprocessing. Histogram equalization, fuzzy histogram equalisation, Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to improve the image quality and therefore increase the identification of the image. Afterwards, necessary features for disease detection are segmented using various deep models like U-Net, U-Net + FPN (Feature Pyramid Network), COVID-SegNet and Dense GAN. Once these distinct deep characteristics have been identified, they are extracted using a variety of different deep models. Finally, an illness is diagnosed using popular models such as SVM, ResNet-50, AlexNet, VGG16, DenseNet, and SqueezeNet. The deep learning models with a better optimization algorithm to be effective in the diagnosis of COVID-19 and also obtain a reduced and efficient feature set for image classification and feature extraction. © 2023 IEEE.

Statistical Analysis of Online Learning Through Cloud Environment in Higher Education Institutions Using R Programming During COVID-19

Online learning through cloud platforms takes place on the cloud - a virtual space that is not tied to any computer. The cloud-based learning management systems bring vast benefits at all educational levels. These systems provide a powerful teaching tool. More educators are adopting them as their main learning management system, and students find them very intuitive to use. An online learning system based on cloud computing infrastructure is possible and it can significantly progress the effectiveness of investment that can make this system into a righteous path and accomplish a win-win situation for students and teachers. This online study comprises of several segments, namely, demographic details of the students, their usage of smart devices, device ownership, device connectivity and number of hours the device is connected in online. It consists of approximately 360 respondents. The statistical analysis was performed using R programming. The results indicated that most of the respondents utilize smartphones to access online classes through cloud platform. During pandemic circumstances the smart devices facilitated faculties and students of higher education sectors to teach and learn their subjects. © 2022 American Institute of Physics Inc.. All rights reserved.

IMI-CDE: an interactive interface for collaborative mapping of study variables to common data elements

The National Institute of Health (NIH) launches the RADx Radical research collaboratives (RADx-rad) to advance new, non-traditional approaches for COVID-19 testing. RADx-rad projects are required to adopt common data elements (CDEs) to collect data to increase data interoperability. To overcome the challenges in finding appropriate CDEs for a wide range of study variables, we create a web application - IMI-CDE to ease the burden of mapping study variables to CDEs from researchers. IMI-CDE can automatically recommend CDE candidates for a study variable based on its name and description. Together with interactive mapping interfaces, IMI-CDE allows researchers to perform variable-CDE mapping with one mouse click. In addition, the IMI-CDE application supports users with multiple roles to work collaboratively on the mapping tasks. We have piloted the IMI-CDE with RADx-rad projects. 22 researchers from 8 different projects have started to use the IMI-CDE system for variable-CDE mappings. The beta-testing evaluators reported the system is intuitive, effective, and easy to use. © 2022 IEEE.

Efficacy and toxicity of carboplatin and gemcitabine dosed on day 1/day 8 versus day 1 alone for platinum-sensitive recurrent epithelial ovarian cancer

Background: Carboplatin, gemcitabine +/-bevacizumab is a preferred regimen for recurrent, platinumsensitive ovarian cancer (PSOC). A phase III trial established that the regimen of carboplatin on Day 1 (D1) and gemcitabine on D1 and Day 8 (D8) was associated with acceptable toxicity and improved progression free survival (PFS) compared to carboplatin alone. Treatment with gemcitabine on D8 incurs more exposure to cytotoxic therapy and increased burden on patients and the healthcare system, especially during the COVID-19 pandemic. However, it is unknown whether D1/D8 gemcitabine imparts an improvement in efficacy compared to D1 alone. Our objective was to compare efficacy and toxicity of carboplatin and gemcitabine D1/D8 (CG-D1/8) with a modified D1 regimen (CG-D1). Methods: A retrospective single-institution cohort study was performed in women with recurrent PSOC treated with carboplatin, gemcitabine +/-bevacizumab from 2009-2020. Data was analyzed by intention to treat comparing women who received CG-D1/8 vs CG-D1. Data was also analyzed by 3 groups: CG-D1/8 vs CG-D1/8 but dropped D8 vs CG-D1. The primary endpoint was response rate (RR), defined as complete or partial response at 6 cycles or maximum cycles if <6. Secondary outcomes included PFS, overall survival (OS), toxicity, Neulasta use and dose reduction. Results: Of 200 patients, 26% completed CGD1/ D8, 21.5% started CG-D1/D8 but dropped D8, and 52.5% received CG-D1. There were no significant differences in age, race, or ECOG between cohorts. Among CG-D1/D8, 45.3% dropped D8 primarily due to neutropenia (51.2%) or thrombocytopenia (30.2%). The RR at 6 cycles was 68.7% for CG-D1/8 completed, 70.7% for CG-D1/8 dropped D8, and 69.3% for CG-D1 (p=0.97). The median PFS was 13.1, 12.1 and 12.4 months for CG-D1/8 completed, CG-D1/8 dropped D8, and CG-D1, respectively (p=0.29). Similarly, median OS was 28.2, 33.5 and 34.3 months for the above groups respectively (p=0.42). While there was no difference in concurrent bevacizumab use for CG-D1/8 and CG-D1 (34.7% vs 29.5%, p=0.43), among the CG-D1/8 patients, a significantly higher proportion of patients who dropped D8 received bevacizumab (51.2% vs 21.2%, p=0.006). Table 1 lists secondary outcomes. Conclusions: There was no significant difference in RR, PFS or OS among women with PSOC receiving CG-D1/8 vs CG-D1, regardless of whether D8 was dropped. CG-D1/8 was associated with significantly greater hematologic toxicity. These findings suggest a modified D1 regimen may be a suitable alternative to standard CG-D1/8 treatment and warrant prospective validation.

The COVID-19 Pandemic and Early Childhood Education in Ethiopia, Liberia, and Pakistan: Perspectives of Pre-primary School Teachers

School closures due to the COVID-19 pandemic have affected teachers and changed their role significantly. This chapter explores the extent to which early childhood education (ECE) teachers were engaged in children’s early learning during school closure through communication with their parents and families in three countries: Ethiopia, Liberia, and Pakistan. Using mobile phone surveys and key informant interviews, we investigate the support pre-primary teachers received during school closures and how they were able to support children’s home-based learning and prepare for school reopening. In all three countries, many teachers were in contact with children and families during school closures, yet the percentage varies by country, from 33% in Ethiopia to 64% in Liberia and 100% in Pakistan. Teachers in Ethiopia and Pakistan reported that children from disadvantaged backgrounds were missing out on essential support, but about 70% of ECE teachers sought to accommodate their learning needs or support psychosocial well-being. We discuss policy implications to support ECE teachers and children in the current and future crises in order to improve responsiveness and resilience in ECE systems. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An Early Intervention Technique for At-Risk Prediction of Higher Education Students in Cloud-based Virtual Learning Environment using Classification Algorithms during COVID-19

Higher Education is considered vital for societal development. It leads to many benefits including a prosperous career and financial security. Virtual learning through cloud platforms has become fashionable as it is expediency and flexible to students. New student learning models and prediction outcomes can be developed by using these platforms. The appliance of machine learning techniques in identifying students at-risk is a challenging and concerning factor in virtual learning environment. When there are few students, it is easy for identification, but it is impractical on larger number of students. This study included 530 higher education students from various regions in India and the outcomes generated from online survey data were analyzed. The main objective of this research is to predict early identification of students at-risk in cloud virtual learning environment by analyzing their demographic characteristics, previous academic achievement, learning behavior, device type, mode of access, connectivity, self-efficacy, cloud platform usage, readiness and effectiveness in participating online sessions using four machine learning algorithms namely K Nearest Neighbor (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Random Forest (RF). Predictive system helps to provide solutions to low performance students. It has been implemented on real data of students from higher education who perform various courses in virtual learning environment. Deep analysis is performed to estimate the at-risk students. The experimental results exhibited that random forest achieved higher accuracy of 88.61% compared to other algorithms © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Sars-cov-2 and covid-19: A brief review for family physicians

SARS-CoV-2, the newest coronavirus, causes COVID-19, a disease that runs the gamut of symptoms from none too mild to severe to death. The severe cases are most often due to acute respiratory distress. In addition to pulmonary symptoms, the virus causes a wide variety of pathological manifestations involving multiple other systems, including eliciting an exaggerated immune response that contributes to fatalities. The elderly are at the highest risk of severe disease. Higher mortality is seen among males, along with individuals with pre-existing comorbidities such as cardiovascular disease and diabetes, among others. Although pregnancy has not been identified as a risk factor yet, more research is needed to assess vertical transmission and strict perinatal precautions are recommended to minimize infecting newborns. Although COVID-19 in children is less likely to be severe, recent cases, albeit rare, have emerged of a multiorgan inflammatory syndrome, similar to Kawasaki disease. Early diagnosis can be done using molecular tests that detect viral genome, while cases manifesting late symptoms can be detected using serological tests looking for antibodies. Although there are no FDA-approved vaccines or therapeutics for prophylaxis, there are many viable vaccine candidates either in clinical trials or awaiting study in humans. Of the several drugs being considered for treatment, some target the virus, while others address the host factors that facilitate virus infection, from proteases that enable virus entry, to cytokines that elicit a harmful and out-of-control immune response. While we await a standardized prophylactic regimen, it is our collective responsibility to continue engaging in prevention measures.