Moving health professionals as an alternative to moving patients: The contribution of overseas visiting medical specialists to the health system in Malta

Aleatory-aware deep uncertainty quantification for transfer learning

The user does not have any idea about the credibility of outcomes from deep neural networks (DNN) when uncertainty quantification (UQ) is not employed. However, current Deep UQ classification models capture mostly epistemic uncertainty. Therefore, this paper aims to propose an aleatory-aware Deep UQ method for classification problems. First, we train DNNs through transfer learning and collect numeric output posteriors for all training samples instead of logical outputs. Then we determine the probability of happening a certain class from K-nearest output posteriors of the same DNN in training samples. We name this probability as opacity score, as the paper focuses on the detection of opacity on X-ray images. This score reflects the level of aleatory on the sample. When the NN is certain on the classification of the sample, the probability of happening a class becomes much higher than the probabilities of others. Probabilities for different classes become close to each other for a highly uncertain classification outcome. To capture the epistemic uncertainty, we train multiple DNNs with different random initializations, model selection, and augmentations to observe the effect of these training parameters on prediction and uncertainty. To reduce execution time, we first obtain features from the pre-trained NN. Then we apply features to the ensemble of fully connected layers to get the distribution of opacity score during the test. We also train several ResNet and DenseNet DNNs to observe the effect of model selection on prediction and uncertainty. The paper also demonstrates a patient referral framework based on the proposed uncertainty quantification. The scripts of the proposed method are available at the following link: https://github.com/dipuk0506/Aleatory-aware-UQ.

Reshaping the Healthcare Sector with Economic Policy Measures Based on COVID-19 Epidemic Severity: A Global Study

Governments around the world are looking for ways to manage economic consequences of COVID-19 and promote economic development. The aim of this study is to identify the areas where the application of economic policy measures would enhance the resilience of societies on epidemic risks. We use data on the COVID-19 pandemic outcome in a large number of countries. With the estimation of multiple econometric models, we identify areas being a reasonable choice for economic policy intervention. It was found that viable remediation actions worth taking can be identified either for long-, mid-, or short-term horizons, impacting the equality, healthcare sector, and national economy characteristics. We suggest encouraging research and development based on innovative technologies linked to industries in healthcare, pharmaceutical, and biotech, promoting transformation of healthcare systems based on new technologies, providing access to quality healthcare, promoting public healthcare providers, and investing in the development of regional healthcare infrastructure, as a tool of equal regional development based on economic assessment. Further, a central element of this study, i.e. the innovative identification matrix, could be populated as a unique policy framework, either for latest pandemic or any similar outbreaks in future.

What drives the healthcare sector's economic impact? Evidence from European countries

Purpose

With high public debts and suffering economies after the COVID-19 pandemic, governments will look for ways to promote recovery. Literature substantially reports on the favorable macroeconomic impact of the healthcare sector.

Design/methodology/approach

The authors use data on 19 European countries. Over 30 variables are analyzed to find factors that foster or suppress the economic impact of the healthcare sector. The economic impact is thereby expressed through five types of total multipliers, acting as dependent variables. The authors estimate multiple econometric models.

Findings

The results indicate factors that intensify or reduce the economic impact of the healthcare sector as they cause the value of one or more economic multipliers to augment or to diminish. Positive effects are expected from the growth of public funds' share in total healthcare expenditure leading to a higher output, income and value-added multipliers. The import multiplier diminishes when expenditure on healthcare as percent of GDP rises. On the other hand, rising expenditure on pharmaceuticals in the share of healthcare expenditure lowers the output multiplier. Rising GDP per capita and higher healthcare systems' technical efficiency cause the employment multiplier to lower.

Originality/value

Policymakers can strengthen the economic impact of the healthcare sector on the national economy. This could be achieved by stimulating factors, being identified in our study. Strengthening the economic impact of the healthcare sector is especially welcomed when fostering economic recovery is needed.

The health workforce: Central to an effective response to the COVID-19 pandemic in the European Region

COVID-19 has reinforced the centrality of health workers at the core of a well performing and resilient health system. It has concomitantly exposed the risks of staffing and skills shortages and the importance of protecting the health workforce. The present commentary focuses on highlighting some of the lessons learnt, challenges and future needs of the health workforce in Europe in the context of COVID-19. During the pandemic innovative and flexible approaches were implemented to meet increasing demand for health workers and new skills and responsibilities were adopted over a short period of time. We have seen the rapid adaptation and use of new technologies to deliver care. The pandemic has underlined the importance of valuing, protecting and caring for our health workforce and the need to invest appropriately and adequately in the health workforce to have sufficient, capable and well-motivated health workers. Some of the main challenges that lie ahead of us include the imperative for better investment, to need to improve recruitment and retraining whilst better retaining health workers, a focus on domestic sustainability, redeploying and developing new skills and competences among health workers, enabling more effective multi-professional collaboration and team work, improving the quality of education and training, increasing the public health focus and promoting ethical and sustainable international recruitment of health workers. The WHO European Region through its European Programme of Work 2020-2025 is fully committed to support countries in their efforts to continue to respond to COVID-19 and whilst addressing upcoming health workforce challenges.