How Bias In Healthcare Can Impact Social Inequities

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Introduction: Bias is something that is becoming more and more prevalent in the world today. Bias can be described as either being for or against something in a way that is usually considered unfair or unequal. Bias in healthcare can lead to some individuals not receiving the proper care needed and may even harm the individual even more. It can also affect how data gets retrieved about groups of people and the trust people put in the healthcare industry. This paper will discuss bias in healthcare data, how healthcare bias impacts data-driven algorithms in healthcare, and how bias in healthcare can impact social inequities. Bias In Healthcare Data Bias can be seen in healthcare, although it can also be “invisible.” HIMSS uses information from …show more content…

Bias in healthcare data can result in health inequities. Using things like AI can usually be beneficial for diagnoses and coming up with treatments, but can also result in patients not receiving the correct or proper care that is needed to treat them. Bias can also be defined statistically and socially when it comes to medicine. Norori et al. Al defines statistical bias as the distribution of a given dataset not reflecting the actual distribution of a population. They define social bias as inequities that may result in a flawed way for given groups of the human population. One of the biggest reasons there is bias in healthcare data is due to the lack of diversity in data being used to train computer programs that run algorithms to come up with diagnoses and treatments for individuals. How Healthcare Bias Impacts Data-driven Algorithms in Healthcare One problem that leads to healthcare data bias is the lack of AI algorithms to learn from several social groups of the human population. According to Norori et …show more content…

Tiffany Johnson states, "Others have suggested that the association of patient-level sociodemographics with postoperative mortality is related to system-level factors, including access to care, location of care, and institution type. This highlights the important intersection of structural racism and social determinants of health with healthcare inequities." The mortality of social inequities is likely to be caused by a lack of access to healthcare. As said earlier, bias in healthcare can also lead people to lose trust in healthcare providers which can ultimately lead patients to seek alternative methods of treating themselves. Conclusion In conclusion, bias is an issue that needs to be looked at closely, especially when it comes to the possibility of it being present in the healthcare field. Bias in the healthcare industry are a rising issue as the use of more and more AI programs are being integrated to help speed the process of coming up with diagnoses and treatment

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