IMPACT

Title

Implicit Bias in Healthcare

Document Type

Presentation

Type

Information Motivating Public Activism (IMPACT)

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.

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Apr 27th, 9:30 AM Apr 27th, 12:50 AM

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.