Assessment of Primary Care Physicians' Perception of Telemedicine Use During the COVID-19 Pandemic in Primary Health Care Corporation, Qatar.

While telemedicine has been extensively researched throughout the globe, the Middle East has seen relatively little research on the topic. The purpose of this study was to investigate the primary care physicians' perceptions of the use of telemedicine, as well as its hurdles and benefits, during the COVID-19 pandemic in the state of Qatar. In this multicenter cross-sectional study, an internally validated questionnaire was distributed among primary care physicians utilizing telemedicine during the pandemic at Primary Health Care Corporation (PHCC), the main primary care provider in Qatar. A convenience sample was taken due to the pandemic restrictions. Out of 254 analyzed questionnaires, about half of the physicians (48%) had used telemedicine in the past primarily in the form of telephone consultations. Nearly three-quarters (74%) of physicians agreed that telemedicine is an easy way to communicate during the pandemic, but only half (52%) felt it improved job performance and effectiveness. Most physicians (90%) agreed that telemedicine is safe during the pandemic, but opinion was split on whether an optimum assessment of COVID-19 disease could be conducted. The majority also considered telemedicine an effective tool for chronic disease reviews (63%) and other consultations such as blood test results and medicine prescriptions (71%). The most significant barrier to telemedicine use was a language barrier followed by a lack of proper training. About 79% of responders felt that telemedicine improves access to healthcare and assists in contacting difficult-to-reach individuals, and 74% also thought it minimizes no-shows in the clinics. Our study has shown that most physicians felt comfortable and safer using telemedicine as an alternative means to conduct consultations during the pandemic. Keeping in view its advantages, the majority suggested that it could be incorporated into daily practice even beyond the pandemic. However, concerns were raised about its use to assess COVID-19 disease, lack of training, and potential barriers such as language differences. Further studies are needed to assess the efficiency and cost-effectiveness of telemedicine as well as the evaluation of alternative media such as video consultation, which could increase the utility of telemedicine and potentially mitigate some of its disadvantages.

Perceived Stress Analysis of Undergraduate Students during COVID-19: A Machine Learning Approach

Stress is a state of mind when an individual experiences emotional or physical tensions originating from any event that results in frustration, anger, or nervousness. Unfortunately, since the inception of the COVID-19 pandemic, it has been massively witnessed among university students due to persistent usage of e-learning gears for the last two years. Due to the severity of the observed stress, accurate and early prediction and detection should play a pivotal role in treating a student. In this work, a questionnaire-based dataset on Jordanian students has been analyzed using the 5-point Likert Scale. One of the most widely used psychological instrument Perceived Stress Scale (PSS) is used to identify the stressrelated symptoms of the students. Based on the dataset, several machine learning (ML) algorithms were applied for regression and classification analysis by which mental stress has been predicted and classified. After simulation in Python, the ML regressors were evaluated through the performance metrics such as R2 Score, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Percentage Absolute Error (MAPE), and ML classifiers were assessed by accuracy, precision, recall, and F1-Score. It is observed that Linear Regression (LR) performed the best among all the regression models whereas the Logistic Regression Classifier (LRC) portrayed the highest accuracy of 97.8% among all the classifiers. Therefore, ML-based stress analysis can significantly contribute to analyzing students' mental stress during COVID-19 in an automated manner.

Management of breast incidentalomas: A closed loop audit experience at royal surrey foundation trust

Introduction: Breast Incidentalomas occur as an unexpected abnormality demonstrated on imaging performed for unrelated symptoms. Pre-COVID19 pandemic management involved urgent referrals for initial breast team evaluation. Clinical encounters occurred prior to the Multi-Disciplinary Team meeting (MDT). COVID-19 restrictions necessitated streamlining and optimising service provision with clinically appropriate encounters. Our aim was to re-audit (SU-CA-21-22-068) findings and management of breast incidentalomas during the pandemic. Methods: Pre-pandemic analysis of practice (November 2019 - January 2020) led us to the intervention of all referrals straight to MDT without an unnecessary prior clinical encounter, with secondary planned investigations and clinical assessment thereafter. Completion of audit loop and analysis included referral information, MDT outcome, imaging, and clinical correspondence with descriptive analysis. Results: Post-intervention 61 patients were referred to the MDT over an 18-month period (February 2020 - October 2021). 90% of patients were referred following CT scans. Median age 71 (range 32-93), 38% of patients had no additional breast imaging and 74% of patients did not require a tissue biopsy. 15% (n=9) were diagnosed with new breast cancer, 36% were new benign, with 34% already known lesions. 16% of patients required no further intervention. Conclusion: 15% of incidentalomas were diagnosed as malignancies, compared to local 3-4% from one stop clinics. Prompt referral to MDT accelerates triple assessment and tissue diagnosis. Streamlining of patient care optimised appropriate clinical encounters for vulnerable patients. Early senior radiological assessment at the MDT of incidentalomas during COVID-19 provided confirmation of benign features and therefore no further intervention and reassurance for 16% of patients.