(1) Background: electronic medical record (EMR) systems remain a significant priority for the improvement of healthcare services. However, their implementation may have resulted in a burden on healthcare workers (HCWs). This study aimed to determine the prevalence of burnout symptoms among HCWs who use EMRs at their workplace, as well as burnout-associated factors. (2) Methods: an analytical cross-sectional study was conducted at six public health clinics equipped with an electronic medical record system. The respondents were from a heterogeneity of job descriptions. Consent was obtained before enrolment into the study. A questionnaire was distributed through an online platform. Ethical approval was secured. (3) Results: a total of 161 respondents were included in the final analysis, accounting for a 90.0% response rate. The prevalence of burnout symptoms was 10.7% (n = 17). Three significant predictors were obtained in the final model: experiencing ineffective screen layouts and navigation systems, experiencing physical or verbal abuse by patients, and having a poor relationship with colleagues. (4) Conclusions: the prevalence of burnout symptoms among healthcare workers working with electronic medical record systems was low. Despite several limitations and barriers to implementation, a paradigm shift is needed to equip all health sectors with electronic medical record systems to improve healthcare service delivery. Continuous technical support and financial resources are important to ensure a smooth transition and integration.

Convolutional Neural Networks (CNNs) have garnered significant utilisation within automated image classification systems. CNNs possess the ability to leverage the spatial and temporal correlations inherent in a dataset. This study delves into the use of cutting-edge deep learning for precise image data classification, focusing on overcoming the difficulties brought on by the COVID-19 pandemic. In order to improve the accuracy and robustness of COVID-19 image classification, the study introduces a novel methodology that combines the strength of Deep Convolutional Neural Networks (DCNNs) and Generative Adversarial Networks (GANs). This proposed study helps to mitigate the lack of labelled coronavirus (COVID-19) images, which has been a standard limitation in related research, and improves the model’s ability to distinguish between COVID-19-related patterns and healthy lung images. The study uses a thorough case study and uses a sizable dataset of chest X-ray images covering COVID-19 cases, other respiratory conditions, and healthy lung conditions. The integrated model outperforms conventional DCNN-based techniques in terms of classification accuracy after being trained on this dataset. To address the issues of an unbalanced dataset, GAN will produce synthetic pictures and extract deep features from every image. A thorough understanding of the model’s performance in real-world scenarios is also provided by the study’s meticulous evaluation of the model’s performance using a variety of metrics, including accuracy, precision, recall, and F1-score.