Top Five Implicit Biases in Nursing and Healthcare

Implicit biases are unconscious attitudes or stereotypes that affect our perceptions, actions, and decisions. In the healthcare industry, implicit biases can seriously affect patient care and outcomes. Here are the top five implicit biases in nursing and healthcare and what we can do to recognize and address them.

1. Racial Bias

Racial bias is one of the most common implicit biases in healthcare. Studies have shown that healthcare providers tend to provide different levels of care to patients based on race or ethnicity. For example, Black patients are less likely to receive pain medication than White patients with the same condition.

To recognize and address racial bias, healthcare providers should undergo cultural competency training and learn about the impact of systemic racism on healthcare outcomes. They should also actively listen to their patients and their concerns, regardless of race or ethnicity.

2. Gender Bias

Gender bias is another common type of implicit bias in healthcare. Women are often dismissed or not taken seriously when they report symptoms, leading to delayed diagnoses and treatment. This is especially true for women of color and those in the LGBTQ+ community.

To recognize and address gender bias, healthcare providers should be aware of the unique health needs of women and the impact of gender on healthcare outcomes. They should also create a safe and inclusive environment where all patients feel comfortable discussing their health concerns.

3. Age Bias

Age bias is the tendency to treat patients differently based on their age. This can lead to undertreatment or overtreatment of certain conditions. For example, older patients may be undertreated for depression because their symptoms are dismissed as a normal part of aging.

To recognize and address age bias, healthcare providers should be aware of the unique health needs of different age groups and the impact of age on healthcare outcomes. They should also involve patients in their care and treatment decisions, regardless of age.

4. Weight Bias

Weight bias is the tendency to treat patients differently based on their weight or body size. This can lead to stigma, discrimination, and poor healthcare outcomes. For example, patients with obesity may be undertreated for certain conditions because their symptoms are attributed to their weight.

To recognize and address age bias, healthcare providers should be aware of the unique health needs of different age groups and the impact of age on healthcare outcomes. They should also involve patients in their care and treatment decisions, regardless of age.

5. Disability Bias

Disability bias is the tendency to treat patients differently based on their disability status. This can lead to inadequate care and poor healthcare outcomes. For example, patients with disabilities may be undertreated for certain conditions because their symptoms are dismissed as a normal part of their disability.

In conclusion, recognizing and addressing implicit biases in nursing and healthcare is essential for providing equitable and high-quality care to all patients. Healthcare providers should undergo cultural competency training, create a safe and inclusive environment, involve patients in their care, and focus on health behaviors and outcomes rather than stereotypes or biases. Doing so can improve healthcare outcomes and promote health equity for all.

Article from Central Valley Medical. 

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​Implicit bias training is now a requirement for many healthcare professionals around the United States! Please refer to your board to know the amount of contact hours that are required; we offer both one and two-hour courses as well as some special state-specific courses!


Studies show that implicit bias around demographics such as race, ethnicity, gender identity, sexual orientation, age, and socioeconomic status cause inequalities in treatment by healthcare professionals. This course will identify how implicit bias affects the perception and treatment decisions of healthcare professionals, how this affects patient health outcomes, and provide solutions for overcoming implicit bias on both personal and systematic levels.

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