IMPACT
Implicit Bias in Healthcare
Document Type
Presentation
Type
Information Motivating Public Activism (IMPACT)
UN Sustainable Development Goal
UNSDG #10: Reduced Inequalities
Start Date
27-4-2022 9:30 AM
End Date
27-4-2022 12:50 AM
Abstract
This study analyzes existing data about healthcare access, focusing on marginalized populations. This paper includes statistics and informational graphics pertaining to health care access for minorities, as well as analysis of why such disparities are present. As a whole, major factors that impact healthcare access are income level, race, and sexual and gender orientation. Existing implicit bias as well as lack of access affect the quality and availability of care for such minority populations. The study largely focuses on impacts and scenarios seen in the United States, where the lack of universal healthcare affects healthcare access significantly. The lack of universal health care causes care to only be accessible to those in higher income levels, which amplifies income disparities between different minority groups and how it correlates with healthcare access. Using this data and R studio, data was separated and compiled into subsets. Using Python, this data will be represented in various graphs and other graphics.
Implicit Bias in Healthcare
This study analyzes existing data about healthcare access, focusing on marginalized populations. This paper includes statistics and informational graphics pertaining to health care access for minorities, as well as analysis of why such disparities are present. As a whole, major factors that impact healthcare access are income level, race, and sexual and gender orientation. Existing implicit bias as well as lack of access affect the quality and availability of care for such minority populations. The study largely focuses on impacts and scenarios seen in the United States, where the lack of universal healthcare affects healthcare access significantly. The lack of universal health care causes care to only be accessible to those in higher income levels, which amplifies income disparities between different minority groups and how it correlates with healthcare access. Using this data and R studio, data was separated and compiled into subsets. Using Python, this data will be represented in various graphs and other graphics.