Studying the relationships between authentic leadership, structural empowerment, and civility in the palliative care sector in Portugal

Purpose This paper aims to propose a model studying the relationship of authentic leadership (AL), structural empowerment (SE) and civility in the palliative care sector. This model proposes SE as a mediator between AL and civility. Design/methodology/approach Data was collected from 213 employees working in five major public palliative care hospitals in central Portugal. The study sample was predominantly female (80.3%) and the response rate was 42.6%. Variables were measured using the Authentic Leadership Inventory, Workplace Civility Scale and Conditions of Work Effectiveness Questionnaire II scales. Hayes’ PROCESS macro for mediation analysis in SPSS was used to test the hypothesized model. Findings Results suggest that AL has a significant positive direct relationship with both SE and civility. Furthermore, SE demonstrated to play a partial mediation effect between AL and civility. Practical implications This study may be of use for healthcare administration encouraging the development of AL, suggesting that the more leaders are seen as authentic, the more employees will perceive they have access to workplace empowerment structures and a civil environment. Originality/value Considering the mainstream literature in healthcare management, to the best of the authors’ knowledge, this is the first study to date to integrate the relation of AL, SE and civility in the palliative care sector. Further, the research model has not previously been introduced when considering the mediating role structural empowerment can play between AL and civility.

Modal Frequencies Associations with Musculoskeletal Components of Human Legs for Extracorporeal Bone Healing Assessment Based on a Vibration Analysis Approach

Reliable and quantitative assessments of bone quality and fracture healing prompt well-optimised patient healthcare management and earlier surgical intervention prior to complications of nonunion and malunion. This study presents a clinical investigation on modal frequencies associations with musculoskeletal components of human legs by using a prototype device based on a vibration analysis method. The findings indicated that the first out-of-plane and coupled modes in the frequency range from 60 to 110 Hz are associated with the femur length, suggesting these modes are suitable quantitative measures for bone evaluation. Furthermore, higher-order modes are shown to be associated with the muscle and fat mass of the leg. In addition, mathematical models are formulated via a stepwise regression approach to determine the modal frequencies using the measured leg components as variables. The optimal models of the first modes consist of only femur length as the independent variable and explain approximately 43% of the variation of the modal frequencies. The subsequent findings provide insights for further development on utilising vibration-based methods for practical bone and fracture healing monitoring.

Clinical Features and Surgical Management of Pituitary Adenoma During Pregnancy: Case Series and Literature Review

Purpose Although conservative treatment was recommended for pregnant patients with pituitary adenomas (PAs), surgical treatment is occasionally necessary for those with acute symptoms. However, surgical intervention among these patients is poorly studied. Methods Six patients with PAs who underwent surgical treatment during pregnancy at Peking Union Medical College Hospital between January 1990 and June 2021 and another 35 pregnant patients profiled in the literature were included. Results All the 41 enrolled patients (mean age 29.8 ± 5.3 years) had acute symptoms including visual field defects, severe headaches, or vision loss requiring emergency pituitary surgeries. Mean tumor diameter was 2.16 ± 0.9 cm, and 92.6% were macroadenomas. PA apoplexies were found in 23 patients. The average gestation time at surgery was 25.1 ± 7.1 weeks; 55.9% of these patients underwent surgery in the second trimester of pregnancy. Multidisciplinary team was involved from before surgery to after delivery. Except one patient underwent an induced abortion, and one fetus died due to a nuchal cord, thirty-nine patients delivered successfully, and 37 of fetuses were healthy till the last follow-up. One fetus died of congenital diaphragmatic hernia, and another had a low Apgar score after a cesarean section but survived. Conclusion PA surgery for pregnant patients with PAs is effective and safe during the second and third trimesters. Pregnant patients requiring emergency PA surgery need multidisciplinary evaluation and healthcare management. Cooperation of neurosurgery, endocrinology, obstetrics, anesthesiology, and neonatology is necessary for a successful surgical intervention for pregnant patients with PAs.

An Analytics Approach to Guide Randomized Controlled Trials in Hypertension Management

Blood pressure (BP) is a significant controllable risk factor for cardiovascular disease (CVD), the leading cause of death worldwide. BP comprises two interrelated measurements: systolic and diastolic. CVD risk is minimized at intermediate BP values, a notion known as the J-curve effect. The J-curve effect imposes fundamental trade-offs in simultaneous management of systolic and diastolic BP; however, assessing a comprehensive set of joint systolic/diastolic BP treatment thresholds while explicitly considering the J-curve effect via randomized controlled trials (RCTs) is not feasible because of the time and cost-prohibitive nature of RCTs. In this study, we propose an analytics approach to identify promising joint systolic/diastolic BP threshold levels for antihypertensive treatment. More specifically, using one of the largest longitudinal BP progression data sets, we first build and fit Brownian motion processes to capture simultaneous progression of systolic/diastolic BP at the population level and externally validate our BP progression model on unseen data. We then analytically characterize the hazard ratio, which enables us to compute the optimal treatment decisions. Finally, building upon the optimal joint BP treatment thresholds, we devise a practical and easily implementable approximate policy. We estimate the potential impact of our findings through a simulation study, which indicates that the impact of explicitly considering the J-curve effect and joint systolic/diastolic BP in treatment decisions could be substantial. Specifically, we estimate that between approximately 3,000 and 9,000 premature deaths from cardiovascular disease in the United States could be prevented annually, a finding that could be tested empirically in randomized trials. This paper was accepted by Stefan Scholtes, healthcare management.

Deep Learning Based Long Term Mortality Prediction in the National Lung Screening Trial

In this study, the long-term mortality in the National Lung Screening Trial (NLST) was investigated using a deep learning-based method. Binary classification of the non-lung-cancer mortality (i.e. cardiovascular and respiratory mortality) was performed using neural network models centered around a 3D-ResNet. The models were trained on a participant age, gender, and smoking history matched cohort. Utilising both the 3D CT scan and clinical information, the models can achieve an AUC of 0.73 which outperforms humans at cardiovascular mortality prediction. By interpreting the trained models with 3D saliency maps, we examined the features on the CT scans that correspond to the mortality signal. The saliency maps can potentially assist the clinicians' and radiologists' to identify regions of concern on the image that may indicate the need to adopt preventative healthcare management strategies to prolong the patients' life expectancy.

Design of Resources Allocation in 6G Cybertwin Technology Using the Fuzzy Neuro Model in Healthcare Systems

In 6G edge communication networks, the machine learning models play a major role in enabling intelligent decision-making in case of optimal resource allocation in case of the healthcare system. However, it causes a bottleneck, in the form of sophisticated memory calculations, between the hidden layers and the cost of communication between the edge devices/edge nodes and the cloud centres, while transmitting the data from the healthcare management system to the cloud centre via edge nodes. In order to reduce these hurdles, it is important to share workloads to further eliminate the problems related to complicated memory calculations and transmission costs. The effort aims mainly to reduce storage costs and cloud computing associated with neural networks as the complexity of the computations increases with increasing numbers of hidden layers. This study modifies federated teaching to function with distributed assignment resource settings as a distributed deep learning model. It improves the capacity to learn from the data and assigns an ideal workload depending on the limited available resources, slow network connection, and more edge devices. Current network status can be sent to the cloud centre by the edge devices and edge nodes autonomously using cybertwin, meaning that local data are often updated to calculate global data. The simulation shows how effective resource management and allocation is better than standard approaches. It is seen from the results that the proposed method achieves higher resource utilization and success rate than existing methods. Index Terms are fuzzy, healthcare, bioinformatics, 6G wireless communication, cybertwin, machine learning, neural network, and edge.

Curbing the Opioid Epidemic at Its Root: The Effect of Provider Discordance After Opioid Initiation

Although medical research has addressed the clinical management of chronic opioid users, little is known about how operational interventions shortly after opioid initiation can impact a patient’s likelihood of long-term opioid use. Using a nationwide U.S. database of medical and pharmaceutical claims, we investigate the care delivery process at the most common entry point to opioid use: the primary care setting. For patients who return to primary care for a follow-up appointment within 30 days of opioid initiation, we ask who should revisit and potentially revise the opioid-based treatment plan: the initial prescriber (provider concordance) or an alternate clinician (provider discordance)? First, using a fully controlled logistic model, we find that provider discordance reduces the likelihood of long-term opioid use 12 months after opioid initiation by 31% (95% Confidence Interval: [18%, 43%]). Both the instrumental variable analysis technique and propensity-score matching (utilizing the minimum-bias estimator approach) account for omitted variable bias and indicate that this is a conservative estimate of the true causal effect. Second, looking at patient activities immediately after the follow-up appointment, we find that this long-term reduction is at least partially explained by an immediate reduction in opioids prescribed after the follow-up appointment. Third, the data suggest that the benefit associated with provider discordance remains significant regardless of whether the patient’s initial prescriber was their regular primary care provider or another clinician. Overall, our analysis indicates that systematic, operational changes in the early stages of managing new opioid patients may offer a promising, and hitherto overlooked, opportunity to curb the opioid epidemic. This paper was accepted by David Simchi-Levi, healthcare management.

An extensive and systematic literature review for hybrid flowshop scheduling problems

Hybrid flowshop scheduling problem (HFSP) is a mixture of two classical scheduling problems as parallel machine scheduling (PMS) and flowshop scheduling (FS). In the HFSP, a series of jobs are processed respectively in a set of stages that at least one of these stages has more than one parallel machine (identical, uniform or unrelated). HFSP is a widespreadly studied subject in the literature and there are various application areas such as transportation, healthcare management, agricultural activities, cloud computing, and the most common manufacturing. Therefore, it will be useful to present a review study including recent papers and developments related to this problem for researchers. For this aim, in this paper, a systematic literature survey has been conducted with respect to HFSPs by means of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology which enables to realize systematic review and meta-analyses in a specified subject. 172 articles which are published in the 2010-2020 year interval, related to production scheduling and including a mathematical programming model to express scheduling problems have been determined as a result of this methodological review process. These articles have been statistically analyzed according to many features such as year, country, journal, number of stages, type of parallel machines, constraints, objective functions, solution methods, test instances and type of parameters. The results of statistical analyses have been presented through charts so as to provide a visual demonstration to researchers. Furthermore, it has been aimed to answer 14 predetermined research questions by means of analyses realized in the scope of this review study. Consequently, it has revealed the existing literature, recent developments and future research suggestions related to HFSP and therefore it is possible to say that this review paper provides a beneficial road map for researchers studying in this field.