Informing Mobile Health Policy for Pregnant Women in Rural Populations in Canada, with a Focus on Pre-Eclampsia.

Canadian healthcare suffers rural disparities, especially in maternal and prenatal care. Drawing on a literature review, the paper highlights the potential of mobile health (mHealth) applications to bridge this gap and improve maternal care in rural communities. mHealth tools have great potential for knowledge and trust-building among healthcare workers and pregnant women. To support the success of these solutions, more funding and policy support are required. mHealth solutions have a great potential for great economic savings while addressing healthcare disparities and ensuring everyone has access to high quality care.

Can machine learning optimize the efficiency of the operating room in the era of COVID-19?

SUMMARY: The cancellation of large numbers of surgical procedures because of the coronavirus disease 2019 (COVID-19) pandemic has drastically extended wait lists and negatively affected patient care and experience. As many facilities resume clinical work owing to the currently low burden of disease in our community, we are faced with operative booking protocols and procedures that are not mathematically designed to optimize efficiency. Using a subset of artificial intelligence called "machine learning," we have shown how the use of operating time can be optimized with a custom Python (a high-level programming language) script and an open source machine-learning algorithm, the ORTools software suite from the Google AI division of Alphabet Inc. This allowed the creation of customized models to optimize the efficiency of operating room booking times, which resulted in a reduction in nursing overtime of 21% - a theoretical cost savings of $469 000 over 3 years.