> 92% of participants will gain awareness about implicit bias in health care and mitigation strategies.
CEUFast, Inc. is accredited as a provider of nursing continuing professional development by the American Nurses Credentialing Center's Commission on Accreditation. ANCC Provider number #P0274.
CEUFast, Inc. is an AOTA Provider of professional development, Course approval ID#03709. This distant learning-independent format is offered at 0.1 CEUs Intermediate, Categories: Professional Issues & Foundational Knowledge. AOTA does not endorse specific course content, products, or clinical procedures. AOTA provider number 9575.
> 92% of participants will gain awareness about implicit bias in health care and mitigation strategies.
After completing this course, the learner will be able to:
Implicit bias (IB), the human tendency to make decisions outside of conscious awareness and based on inherent factors rather than evidence, may influence the health care you provide. Also known as unconscious bias, IB establishes itself through attitudes or behaviors developed early in life that are prejudiced against or in favor of one person or group compared to another (Fitzgerald & Hurst, 2017). As identified in the literature across professional health disciplines, IB is associated with negative health disparities, health inequities, and substandard care among diverse populations. Likewise, IB may affect all persons' health by unconsciously influencing how providers perceive and act toward clients, and conversely, how clients may view provider interactions ([National Center for Cultural Competency (NCC)] August 2021; [Institute of Medicine (IOM)], 2003).
IB is unintentional and attributed to the reflexive neurological system that drives the brain's automatic processing function. As such, an individual's feelings, attitudes, and decisions are involuntary, and their subsequent actions may conflict with their stated views (NCC, 2021). Consequently, the effects of IB can be difficult to identify and measure, and actions resulting from it often are challenging to recognize and control. Health care literature describes ongoing IB mitigation efforts, including the promotion of provider awareness, participation in continuing education, advancement of policy development, legislation, and institutional changes, and the contribution of research (Fitzgerald & Hurst, 2017; NCC, 2021; Brecher et al., 2021; The Joint Commission, 2020). Learning about IB and how it differs from explicit bias, recognizing types of IB and how IB provider-client interactions are affected, and embracing strategies to address its impact on practice are approaches toward reducing barriers to equitable care, closing the gap in health disparities between diverse populations, and achieving patient-centered care.
To better understand IB, think about how it contrasts with explicit bias (EB), which is individuals' or institutions' overt expressions of bias that are deliberate and tend to be recognizable (Jordan, 2018). EB is attributed to the reflective system of the human brain that is devoted to cognitive processing (NCC, 2021). Consider the following EB example: A neurosurgeon decides to initiate a patient billing policy that excludes the acceptance of patients' insurance and demands full payment at the point of service. Staff posts a sign in the patient waiting room that states, "As of August 1, 2021, this practice does not accept health insurance." This policy openly favors affluent clients over those without financial means, and the inequitable access to care created by it is deliberate, readily identifiable, and measurable.
IB presents challenges in health care when it manifests itself inappropriately and unconsciously contributes to health disparities. Health disparities are "the differences in the burden of illness, injury, disability, or mortality outcomes between groups distinguished by characteristics such as age, gender, race, and ethnicity leading to unfair and avoidable differences in health outcomes" (Healthy People, 2020). For example, the Centers for Disease Control and Prevention reports that during the period 2007-2016, nearly 700 women died in the US annually from pregnancy-related complications (Petersen et al., 2019). Maternal mortality in the US is alarming, as are its significant racial and ethnic disparities. American Indian, Alaska Native, and black women are two to three times more likely to die of pregnancy-related causes than white women. It is understood that social determinants of health have historically prevented many people from diverse minority groups from "accessing fair opportunities for economic, physical, and emotional health, factors understood to impact health equity" (Howell, 2018). Although targeted efforts to isolate causes and develop successful mitigation strategies to combat US maternal mortality are ongoing, further innovative research and creative strategies are warranted. Suggestions for provider targeted IB research to consider on this topic may include: does a provider's IB influence their decision not to make a referral because they believe that patient to be non-compliant, or when to refer a pregnant woman considered high risk?
In 2003, the Institute of Medicine's formative report Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care laid a foundation for exploration into health care disparities in the US, including bias toward patients of diverse racial, ethnic, or cultural populations. The report concluded that "bias, stereotyping, prejudice, and clinical uncertainty on the part of health care providers may contribute to racial and ethnic disparities in health care" (IOM, 2003). More recently, Fitzgerald and Hurst's (2018) systematic review of 42 articles discussed robust documentation of IB among nurses and physicians and reinforced the negative effects of professional caregivers' IB on vulnerable populations including, "minority ethnic populations, immigrants, socioeconomically challenged individuals, persons with low health literacy, sexual minorities, children, women, elderly, mentally ill, overweight and the disabled." These reports and studies contribute to the evolving body of knowledge about IB in health care through research and provoke thoughts about the effects of IB on health outcomes.
Multidisciplinary health literature indicates that many factors contribute to health disparities, including "quality of healthcare, underlying chronic conditions, structural racism, and IB" (Petersen et al, 2019). Narayan (2019) cites literature that indicates health care providers' IB is associated with "inequitable care and negative effects on patient care including inadequate patient assessments, inappropriate diagnoses and treatment decisions, less time involved in patient care, and patient discharges with insufficient follow-up." Additionally, Saluja and Bryant (2021), suggest that IB can affect provider-patient communication among people of color. The effects may include "subtle racial biases expressed by providers, such as approaching patients with a condescending tone that decreases the likelihood that patients will feel heard and valued by their providers." Variation in therapy options may also occur based on assumptions about clients' treatment adherence capabilities or presumed health issues.
Additionally, IB may negatively impact clinical outcomes, as well as violate patient trust. Penner et al., (2016) found in a study of black oncology patients and their physicians that "patients perceived providers high in IB as less supportive of and spent less time with their patients as compared to providers low in implicit bias. In turn, black patients recognized those attitudes and viewed high-implicit-bias physicians as less patient-centered than physicians low in this bias. The patients also had more difficulty remembering what their physicians told them, had less confidence in their treatment plans, and thought it would be more difficult to follow recommended treatments." These findings on providers' implicit racial bias underscore patients' perceptions of their experiences with providers' IB. However, its overall effects on health care quality and health outcomes for diverse populations invite further exploration (Penner et al., 2016).
Surprising to many providers, the level of IB demonstrated by health care professionals is understood to be comparable to the general population (Fitzgerald & Hurst, 2017). Given the unconscious nature of IB, directly asking providers about their IB through a self-report survey is not recommended. However, two common methods used to assess IB are Implicit Association Testing and Assumption Method.
Implicit Association Testing (IAT) is a computer-generated online testing method that "measures implicit associations between participants' concepts and attitudes across a wide range of domains: race and ethnicity, disability, sexuality, age, gender, religion, and weight." For over 20 years, web-based IAT data has been collected through Project Implicit, a consortium of researchers from Harvard University, the University of Virginia, and the University of Washington to study and promote the understanding of attitudes, stereotypes, and other hidden biases that influence perception, judgment, and action (Project Implicit, 2021).
Assumption Method (AM) is a clinical vignette-based testing method that measures differences across participants' responses. The vignettes are designed to be the same except for one variable, such as gender. Inferences are made based on statistically significant responses correlated with the selected feature, such as the patient's gender. An inference is made that "the response is partly due to the result of implicit processes in the subject's decision-making "(Fitzgerald & Hurst, 2017).
Typically, health care professionals intend to provide optimal care to all patients, but IB may negatively impact their aim. Strategies to disrupt IB, such as promoting self-awareness and participation in formal training, suggest that biases learned earlier in life may be mitigated (Fitzgerald et al., 2019). Efforts to define consistent, evidence-based bias reduction strategies are advancing, and evaluation is ongoing. Meanwhile, learning about types of IB and how they may affect health care remains important. Likewise, the support of institutional changes is necessary to sustain meaningful, ongoing mitigation efforts. The literature is rich with resources to mitigate IB, including but not limited to the following topics:
Learning about common types of IB and their unintended effects between health professionals and patients is a strategy to build IB awareness. The following list is not intended to be exhaustive but to present a range of IBs that may influence provider-patient or institutional decisions (Brecher et al., 2021; NCC, 2021; Smith, 2021). Reflect on how your beliefs may confirm or conflict with the examples and how you might be affected in these scenarios:
Recognizing the need to mitigate IB, address health disparities, and further ensure the quality of care provided by licensed health care providers among diverse populations, required IB health provider training is emerging across the US. These laws empower policymakers, health care licensure boards, and health care settings to improve health professionals' IB knowledge, with the intent to effect positive change in systems of care. Likewise, they present opportunities for data collection to measure IB changes and evaluate patients' health outcomes over time. The following list includes examples of recent legislation to address IB in professional health care:
A 66-year-old Hispanic male resides in a rural community. He contacts their primary care provider's (PCP) office with the following complaints: temperature 100.2 degrees Fahrenheit x three days, headache, body ache, fatigue, nasal congestion with a runny nose. They underwent a Covid-19 polymerase chain reaction (PCR) test at their local pharmacy yesterday, received their positive test result today, and are anxious to speak to their PCP about treatment.
A telehealth appointment is conducted with their PCP. The patient's condition warrants community-based treatment, and strategies are discussed. The patient specifically asks about medication to cure Covid-19. They had heard about it from a friend and believe that many people get it through their local livestock supply store. Their PCP responds that they understand from speaking with other local health care professionals that some are recommending Ivermectin therapy, which coincidentally is available for livestock. The PCP proceeds to write that prescription to be filled at the pharmacy.
The Centers for Disease Control and Prevention (CDC, 2021) reports that the US Food and Drug Administration has not authorized the use of Ivermectin for the prevention or treatment of COVID-19. Likewise, Ivermectin has not been recommended by the National Institutes of Health's COVID-19 Treatment Guidelines Panel for treatment of COVID-19. The PCP's decision to prescribe this medication appears to be influenced by their implicit bias (IB) to conform with their patient's request and some colleagues' anecdotal treatment recommendations. It is not an evidence-based treatment decision. Rather, the treatment decision is consistent with conformity bias, a type of IB.
Typically, health care professionals intend to provide optimal care to all patients, but IB may negatively impact their aim. IB is the human tendency to make decisions outside of conscious awareness and based on inherent factors rather than evidence (Fitzgerald & Hurst, 2017).
Conformity bias is a type of IB associated with the tendency to be influenced by other people's views (Brecher, 2021).
IB is the unconscious and therefore unintentional human tendency to make decisions based on inherent factors rather than evidence. No one is immune, not even health care professionals. Recognizing common types of IB by building self-awareness and participating in voluntary or mandatory training are steps that health professionals may take to minimize its impact on their care. Likewise, State governments' mandates specific to IB in healthcare are embedding training across health professions and care settings into law. More research is needed to measure how IB training may change health providers' short- and long-term beliefs, practices, and patients' perceptions. Ultimately, these steps are intended to minimize IB among health care providers, reduce barriers to equitable care, close the gap in health disparities between diverse populations, and meet patients' needs.
CEUFast, Inc. is committed to furthering diversity, equity, and inclusion (DEI). While reflecting on this course content, CEUFast, Inc. would like you to consider your individual perspective and question your own biases. Remember, implicit bias is a form of bias that impacts our practice as healthcare professionals. Implicit bias occurs when we have automatic prejudices, judgments, and/or a general attitude towards a person or a group of people based on associated stereotypes we have formed over time. These automatic thoughts occur without our conscious knowledge and without our intentional desire to discriminate. The concern with implicit bias is that this can impact our actions and decisions with our workplace leadership, colleagues, and even our patients. While it is our universal goal to treat everyone equally, our implicit biases can influence our interactions, assessments, communication, prioritization, and decision-making concerning patients, which can ultimately adversely impact health outcomes. It is important to keep this in mind in order to intentionally work to self-identify our own risk areas where our implicit biases might influence our behaviors. Together, we can cease perpetuating stereotypes and remind each other to remain mindful to help avoid reacting according to biases that are contrary to our conscious beliefs and values.