processes, such as breast cancer, where Hispanics and Blacks have been identified as consistently not receiving high-quality care in treating breast cancer (AHRQ, 2022). See the Highlighting the Agency of Healthcare Research and Quality box for more information on the quality measures tracked. Biases in healthcare are multidirectional—provider to patient and patient to provider, and also organization to provider and organization to patient. These biases are usually in the form of preferences. Some examples of preferences that are biased and can be from the patient or the provider are gender, skin tone, and even age. It does not matter which direction the biases are going; the result is a decrease in the quality of care provided. Patient preferences can lead to decreased quality of care received because the more qualified provider was not chosen. I want to point out a crucial difference in choosing a provider for modesty or religious reasons over stereotype reasons. For example, a female may prefer a female gynecologist for personal modesty reasons instead of stereotypical biases or to protect a possible religious or cultural custom that a women should not be alone with a man that is not her husband. A bias would come into play in this situation if the female provider were chosen based on other preferences, such as race and age. However, biases from patients toward providers, while perhaps not purposeful, can harm the provider. Comments toward providers are usually about gender, age, and race and can be seen as insults. For example, a female doctor walks into the patient's room, and the family members think she is a nurse. A male nurse receives comments about his gender and role or a request from the patient for a female nurse. Questions can also come in the form of providing credentials and qualifications. Many providers have felt less than, unwelcomed, and devalued, which leads to low self- esteem, low self-worth, depression, and anxiety (Turner et al., 2021). Figure 1 identifies areas where implicit biases are predominantly seen in healthcare. We will take a deeper look into what a few of these look like in practice.
Box 1 Michigan R.338.7004
“Beginning June 1, 2022, and for every renewal cycle thereafter, in addition to completing any continuing education required for renewal, reregistration, or relicensure, an applicant for license or registration renewal, reregistration, or relicensure under article 15 of the code MCL 333.16101 to 333.18838, except those licensed under part 188 of the code, MCL 333.18801 to 333.18838 shall have completed a minimum of 1 hour of implicit bias training for each year of the applicant's license or registration cycle.” Note: From https://ars.apps.lara.state.mi.us/AdminCode/ DownloadAdminCodeFile?FileName=R%20338.7001%20to%20 R%20338.7005.pdf&ReturnHTML=True AHRQ releases the National Healthcare Quality and Disparities Report annually, and the information released is from the year prior to the publication year. For example, the 2022 report presents data gathered for 2021. The comprehensive data in the report provide yearly updates on social determinants of health, including the overall quality of healthcare and healthcare disparities. The report presents measurable trends for access to care, affordable care, care coordination, effective treatment, healthy living, patient safety, and person-centered care. The organization’s mission is to “produce evidence to make healthcare safer, higher quality, more accessible, equitable, and affordable, Highlighting the Agency of Healthcare Research and Quality and to work within the U.S. Department of Health and Human Services and with other partners to make sure that the evidence is understood and used.” You can find more information and the most current data at https://www.ahrq.gov/.
Polling Question: Unconscious Biases
Figure 1. Areas of Bias
Noted. Adapted from https://www.medicalnewstoday.com/articles/biases-in-healthcare
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Book Code: MMD0926
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