Use of protocol-driven medication refills by pharmacists decreases rheumatologist in-basket work and improves rheumatologist satisfaction

Burn-out among US physicians has been on the rise in the past few decades. Similarly, rheumatologists in the Geisinger Health System have experienced professional dissatisfaction through significant administrative burden and in-basket work. We embedded pharmacists into our rheumatology team in 2019 with the aim of reallocating medication refills to pharmacists, trained professionals in this domain, to help reduce physician workload and burn-out and increase satisfaction. Protocol-driven medication refill parameters per the American College of Rheumatology guidelines and new refill workflows for disease-modifying antirheumatic drugs (DMARDs) and non-DMARDs were created for use by our rheumatology pharmacists. Monthly data on medication refill volume and time saved for rheumatologists were collected from 1 January 2019 to 31 March 2021. Statistical analysis was completed via Shewhart p-charts. The volume of refills by rheumatologists decreased by 73% and the time saved per month for all the rheumatologists increased to 41.5 hours within 6 months. Physicians’ feedback was obtained via anonymous electronic surveys preintervention and postintervention. The statistical difference between the presurveys and postsurveys was calculated via two-tailed unpaired t-testing. It demonstrated reduced burn-out and improved workplace satisfaction. This study showed that the integration of rheumatology pharmacists into our practice can help improve the work life of the rheumatologists. It is important for physicians’ well-being to practice at the top of their scope and achieve work–life balance.

Detection of Bone Metastases on Bone Scans through Image Classification with Contrastive Learning

Patients with bone metastases have poor prognoses. A bone scan is a commonly applied diagnostic tool for this condition. However, its accuracy is limited by the nonspecific character of radiopharmaceutical accumulation, which indicates all-cause bone remodeling. The current study evaluated deep learning techniques to improve the efficacy of bone metastasis detection on bone scans, retrospectively examining 19,041 patients aged 22 to 92 years who underwent bone scans between May 2011 and December 2019. We developed several functional imaging binary classification deep learning algorithms suitable for bone scans. The presence or absence of bone metastases as a reference standard was determined through a review of image reports by nuclear medicine physicians. Classification was conducted with convolutional neural network-based (CNN-based), residual neural network (ResNet), and densely connected convolutional networks (DenseNet) models, with and without contrastive learning. Each set of bone scans contained anterior and posterior images with resolutions of 1024 × 256 pixels. A total of 37,427 image sets were analyzed. The overall performance of all models improved with contrastive learning. The accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve, and negative predictive value (NPV) for the optimal model were 0.961, 0.878, 0.599, 0.712, 0.92 and 0.965, respectively. In particular, the high NPV may help physicians safely exclude bone metastases, decreasing physician workload, and improving patient care.

Rethinking Consent for Stroke Trials in Time-Sensitive Situations

Informed consent is a key concept to ensure patient autonomy in clinical trials and routine care. The coronavirus disease 2019 (COVID-19) pandemic has complicated informed consent processes, due to physical distancing precautions and increased physician workload. As such, obtaining timely and adequate patient consent has become a bottleneck for many clinical trials. However, this challenging situation might also present an opportunity to rethink and reappraise our approach to consent in clinical trials. This viewpoint discusses the challenges related to informed consent during the COVID-19 pandemic, whether it could be acceptable to alter current consent processes under these circumstances, and outlines a possible framework with predefined criteria and a system of checks and balances that could allow for alterations of existing consent processes to maximize patient benefit under exceptional circumstances such as the COVID-19 pandemic without undermining patient autonomy.

The number and composition of work hours for attending physicians in Taiwan

Long work hours among physicians is a worldwide issue in the healthcare arena. Previous studies have largely focused on the work hours of resident physicians rather than those of attending physicians. The purpose of this study was to investigate total work hours and the composition of those work hours for attending physicians across different hospital settings and across different medical specialties through a nationwide survey. This included examining differences in physician workload and its composition with respect to different hospital characteristics, and grouping medical specialties according to the work similarities. A cross-sectional self-reported nationwide survey was conducted from June to September of 2018, and the two questionnaires were distributed to all accredited hospitals in Taiwan. The number of physician work hours in different types of duty shifts were answered by medical specialty in each surveyed hospital. Each medical specialty in a hospital filled only one response for its attending physicians. The findings reveal that the average total work hours per week of an attending physician is around 69.1 h, but the total work hours and their composition of different duty shifts varied among hospital accreditation levels, geographic locations, emergency care responsibilities, and medical specialties. Because of the variance in the number and composition of attending physicians’ work hours, adjusting physician work hours to a reasonable level will be a major challenge for health authority and hospital managers.

AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks

The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hoped artificial intelligence (AI) to help reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. Here, we present our experience in building and deploying an AI system that automatically analyzes CT images to detect COVID-19 pneumonia features. Different from conventional medical AI, we were dealing with an epidemic crisis. Working in an interdisciplinary team of over 30 people with medical and / or AI background, geographically distributed in Beijing and Wuhan, we were able to overcome a series of challenges in this particular situation and deploy the system in four weeks. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we were able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases. Besides, the system automatically highlighted all lesion regions for faster examination. As of today, we have deployed the system in 16 hospitals, and it is performing over 1,300 screenings per day.

ANALYSIS OF INDICATORS OF ACTIVITY OF CHILDREN’S DENTAL SERVICE IN THE POLTAVA REGION (2014–2016)

Purpose of the study. Analysis of qualitative and quantitative indicators of activity of institutions providing dental care for children of Poltava region for 2014–2016. Materials and methods. Accounting and reporting documentation of health care institutions providing dental care to children of Poltava region (f. 039-2/o, f.049/o, f.20, f.17) for 2014–2016, annual statistical reports of the Poltava Regional Analytical Center for Medical Statistics for 2014–2016. Methods: biblio-semantic, medico-statistical, methods of system hike and system analysis. Results. Analysis of indicators of children`s dental institutions showed an increased workload for doctors due to the reduction in the positions of physicians and increasing the number of visits, deterioration of the dental health of children in the region, lack of preventative work in some areas of the region or lack of effectiveness. Conclusions. The availability of pediatric dentists in the children`s population of Poltava Oblast is insufficient. Reducing the number of pediatric dentists leads to a significant increase in physician workload. In some districts of the Poltava oblast, there are poor or insufficient indicators of planned readjustment and low activity or ineffectiveness of preventative work on dental diseases. Keywords: dental care for children, analysis of indicators of activity.

Detection of Thyroid Nodules with Ultrasound Images Based on Deep Learning

Background: Thyroid nodules are a common clinical entity with high incidence. Ultrasound is often employed to detect and evaluate thyroid nodules. The development of an efficient automated method to detect thyroid nodules using ultrasound has the potential to reduce both physician workload and operator-dependence. Objective: To study the method of automatic detection of thyroid nodules based on deep learning using ultrasound, and to obtain the detection method with higher accuracy and better performance. Methods: A total of 1200 ultrasound images of thyroid nodules and 800 ultrasound thyroid images without nodule are collected. An improved faster R-CNN based detection method of thyroid nodule is proposed. Instead of using VGG16 as the backbone, ResNet is employed as the backbone for faster R-CNN. SVM, CNN and Faster-RCNN methods are used for thyroid nodule detection test. Precision, sensitivity, specificity and F1-score indicators are used to evaluate the detection performance of different methods. Results: The method based on deep learning is superior to that based on SVM. Faster R-CNN method and the improved method are better than CNN method. Compared with VGG16 as the backbone, RestNet101 backbone based faster R-CNN method achieves better thyroid detection effect. From the accuracy index, the proposed method is 0.084, 0.032 and 0.019 higher than SVM, CNN and faster R-CNN, respectively. Similar results can be seen in precision, sensitivity, specificity and F1-Score indicators. Conclusion: The proposed method of deep learning achieves the best performance values with the highest true positive and true negative detection compared to other methods and performs best in the detection of thyroid nodules.