Artificial Intelligence and Telehealth may Provide Early Warning of Epidemics

Healthcare professionals' experiences and perceptions of artificial intelligence in regional and rural health districts in Australia

INTRODUCTION

A greater understanding of Australian healthcare professionals' perceptions of artificial intelligence (AI) is needed to identify the challenges ahead as this new technology finds its way into healthcare delivery.

OBJECTIVE

The aim of this study was to identify healthcare professionals' perceptions of AI, their understanding of this technology, their education needs and barriers they perceived to its implementation.

DESIGN

Healthcare professionals in eight local health districts in New South Wales Australia were surveyed using the Shinners Artificial Intelligence Perception (SHAIP) tool.

FINDINGS

The study surveyed 176 participants from regional (59.5%), rural (36.4%) and metropolitan (4.0%) healthcare districts in Australia. Only 27% of all participants stated they are currently using AI in the delivery of care. The study found that Age, Discipline, Use of AI and Desire for Education had a significant effect on perceptions of AI, and that overall healthcare professionals believe AI will impact their role and they do not feel prepared for its use. The study showed that understanding of AI is varied and workforce knowledge is seen as the greatest barrier to implementation. More than 75% of healthcare professionals desire education about AI, its application and ethical implications to the delivery of care.

CONCLUSION

The development of education is needed urgently to prepare healthcare professionals for the implementation of AI.

The promise of data science for health research in Africa

Data science health research promises tremendous benefits for African populations, but its implementation is fraught with substantial ethical governance risks that could thwart the delivery of these anticipated benefits. We discuss emerging efforts to build ethical governance frameworks for data science health research in Africa and the opportunities to advance these through investments by African governments and institutions, international funding organizations and collaborations for research and capacity development.

Hospital sewage treatment facilities witness the fighting against the COVID-19 pandemic

Real-time evaluation of the fighting activities during a sudden unknown disaster like the COVID-19 pandemic is a critical challenge for control. This study demonstrates that the temporal variations of effluents from hospital sewage treatment facilities can be used as an effective indicator for such evaluation. Taking a typical infection-suffering city in China as an example, we found that there was an obvious decrease in effluent ammonia and COD concentrations in line with the start of city lockdown, and its temporal variations well indicated the major events happened during the pandemic control. Notably, the lagging period between the change point of effluent residual chlorine and the change points of COD and ammonia concentration coincided with a period in which there was a deficiency in local medical resources. In addition, the diurnal behavior of effluents from designated hospitals has varied significantly at different stages of the pandemic development. The effluent ammonia peaks shifted from daytime to nighttime after the outbreak of the COVID-19 pandemic, suggesting a high workload of the designated hospitals in fighting the rapidly emerging pandemic. This work well demonstrates the necessary for data integration at the wastewater-medical service nexus and highlights an unusual role of the effluents from hospital sewage treatment facilities in revealing the status of fighting the pandemic, which helps to control the pandemic.

Predicting the Economic Impact of the COVID-19 Pandemic in the United Kingdom Using Time-Series Mining

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