Vaccine Hesitancy in the Current Landscape of the COVID-19 Pandemic

For a procedure so commonplace in a doctor’s office, so critical to the prevention of disease, and so marred by controversy, it seemed odd that in my third year of medical school, I had yet to perform a vaccination. For that reason, I was very excited for my first rotation in family medicine. One could even say I was a little overzealous to learn about vaccines. To my credit, as someone who studied healthcare economics, there is a lot to love about the social return on investment of vaccines. Two weeks into my rotation I came across my first case of vaccine hesitancy. They were parents who had immigrated to Canada and were skeptical of vaccines. They wanted to pick and choose which vaccines their child would receive. These people are called the ‘vaccine hesitant’ and, unlike the devout ‘vaccine deniers’, they are the crucial swing states in a political battle for the health of our country. [1]

Measuring Outcomes in Healthcare Economics using Artificial Intelligence: with Application to Resource Allocation

The quality of service in healthcare is constantly challengedby outlier events such as pandemics and naturaldisasters. In most cases, such events lead to critical uncertaintiesin decision making, as well as in multiple medicaland economic aspects of a hospital. External (geographical)or internal factors (medical and managerial) at hospitals,lead to shifts in planning, budgeting, and confidencein conventional processes. In some cases, support fromother hospitals becomes inevitable. This manuscript presentsthree intelligent methods that provide data-drivenindicators to help healthcare managers organize their economicsand identify the most optimum plan for resourceallocation and sharing. Using reinforcement learning, geneticalgorithms, traveling salesman, and clustering, weexperimented with different healthcare variables and presentedtools and outcomes that could be applied at healthinstitutes. In this poster, initial experiments are performed;the results are recorded, evaluated, and illustrated.

Measuring outcomes in healthcare economics using Artificial Intelligence: With application to resource management

The quality of service in healthcare is constantly challenged by outlier events such as pandemics (i.e., Covid-19) and natural disasters (such as hurricanes and earthquakes). In most cases, such events lead to critical uncertainties in decision-making, as well as in multiple medical and economic aspects at a hospital. External (geographic) or internal factors (medical and managerial) lead to shifts in planning and budgeting, but most importantly, reduce confidence in conventional processes. In some cases, support from other hospitals proves necessary, which exacerbates the planning aspect. This paper presents three data-driven methods that provide data-driven indicators to help healthcare managers organize their economics and identify the most optimum plan for resources allocation and sharing. Conventional decision-making methods fall short in recommending validated policies for managers. Using reinforcement learning, genetic algorithms, traveling salesman, and clustering, we experimented with different healthcare variables and presented tools and outcomes that could be applied at health institutes. Experiments are performed; the results are recorded, evaluated, and presented.

Suicide Prevention International (SPI)

Suicide Prevention International (SPI) was developed with the intention of addressing the increasing rates of suicide and depression worldwide; with a specific focus on developing countries as well as large areas of industrialized countries. The organization is made up of an international network of experts in various areas related to suicide, mental health, and public health. SPI’s scientific advisory council includes representatives from 22 countries with expertise in suicide prevention, public health, healthcare economics, social medicine, youth suicide, suicide in the elderly, and in the problems of those who have lost a loved one to suicide. This chapter provides a description of the organization, as well as the important projects which are being undertaken by SPI in order to treat depression and prevent suicide.