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Implicit Bias in Healthcare- 1hr

1 Contact Hour
Meets Michigan's Requirement for License Renewal
This peer reviewed course is applicable for the following professions:
Advanced Registered Nurse Practitioner (ARNP), Certified Registered Nurse Anesthetist (CRNA), Clinical Nurse Specialist (CNS), Licensed Practical Nurse (LPN), Licensed Vocational Nurses (LVN), Midwife (MW), Nursing Student, Occupational Therapist (OT), Occupational Therapist Assistant (OTA), Physical Therapist (PT), Physical Therapist Assistant (PTA), Registered Nurse (RN), Respiratory Therapist (RT)
This course will be updated or discontinued on or before Sunday, November 26, 2023
Outcomes

92% of participants will gain awareness about implicit bias in health care and mitigation strategies.

Objectives

After completing this course, the learner will be able to:

  1. Define implicit bias and its relationship to your practice of health care.
  2. Relate implicit bias to your practice of health care.
  3. Categorize five types of implicit bias and an example for each.
  4. Describe two methods used to measure implicit bias.
  5. Outline two implicit bias mitigation strategies
CEUFast Inc. and the course planners for this educational activity do not have any relevant financial relationship(s) to disclose with ineligible companies whose primary business is producing, marketing, selling, re-selling, or distributing healthcare products used by or on patients.

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To earn of certificate of completion you have one of two options:
  1. Take test and pass with a score of at least 80%
  2. Reflect on practice impact by completing self-reflection, self-assessment and course evaluation.
    (NOTE: Some approval agencies and organizations require you to take a test and self reflection is NOT an option.)
Author:    Pauline Lisciotto (RN, MSN, APHN-BC)

Introduction

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.

A Brief Note about Explicit Bias

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.

Challenges of IB in Health Care

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).

Measuring IB

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).

Mitigating IB in Health Care: Challenges and Strategies

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:

  1. Awareness of common types of IB
  2. Legislation to institutionalize IB training across health professions and health care systems

Common Types of IB

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:

  1. Affinity-Preference for people who share qualities with you or someone you like.
    1. Example-A Clinic Director (CD) is recruiting to fill one physical therapist vacancy. The final two candidates share comparable minimum education requirements and clinical experiences. The CD selects the candidate who attended their alma mater.
    2. Rationale-Although, the candidates, are comparable, the CD selects the candidate who feels comfortable and familiar.
  2. Anchoring–Tendency to rely too heavily on the first piece of information offered during decision making.
    1. Example: While assessing a 25-year-old patient vaccinated for COVID-19, the nurse practitioner notes signs and symptoms: headache, fatigue, sore throat with red and enlarged tonsils, and fever x three days. The patient's strep test is positive, and antibiotics are prescribed. The patient finishes the prescription but returns in seven days with continued complaints of headache and growing fatigue. At this visit, a COVID-19 rapid test is performed, the result is positive.
    2. Rationale-Provider focused on the patient's presenting problem and rushed to a diagnosis that supported their initial clinical impression.
  3. Attribution-Tendency to characterize other people's successes as luck or help from others and explain their failures as lack of skill or personal shortcomings.
    1. Example-A clinical social worker (CSW) who cannot finish case notes promptly compared to their colleagues believes that their caseload has too many needy patients with complex mental health diagnoses.
    2. Rationale-CSW's justification is based on perceived situational factors.
  4. Beauty-Assumptions about people's skills or personality based on their physical appearance and tendency to favor more attractive people.
    1. Example-A client seeks a surgeon by visiting their insurance plan's website. They are impressed with a physician's photo they consider handsome and select them because they associate the surgeon's appearance with intelligence and skill.
    2. Rationale-The client relates beauty with other positive attributes such as intelligence.
  5. Confirmation-Selective focus on information that supports your initial opinion(s).
    1. Example-A dentist recovers from COVID-19 infection with mild symptoms yet remains vaccine-hesitant.
    2. Rationale-The dentist remains unvaccinated because they feel that they acquired sufficient natural immunity.
  6. Conformity-Tendency to be swayed by the views of other people.
    1. Example-A long-term care patient follows Hinduism, practices a strict vegan diet, and asks their nurse for vegan meals. The patient's roommate overhears the conversation and interjects, "dietary will send you whatever you want." Without validating the patient's request with the dietician, the nurse submits the vegan meal request.
    2. Rationale-The nurse tends to agree with people around them rather than use their professional judgment.
  7. Disability-Tendency to assign a lower quality of life because of disability.
    1. Example-An adult patient with Down syndrome and severe congenital heart disease was considered by their primary care provider (PCP) to be an inappropriate referral for a heart transplant procedure due to their intellectual/developmental delay (IDD).
    2. Rationale-The PCP underestimates the quality of life for this patient based on their IDD and automatically excludes them from consideration for an organ transplant.
  8. Gender- Preference for one gender over the other.
    1. Example-An infertility practice accepts a 35-year-old female patient with a history of infertility, and in-vitro fertilization is recommended. However, the physician refuses to provide treatment, alleging that their religious beliefs prevent them from performing the procedure for a lesbian.
    2. Rationale-The physician holds an inherent gender bias against a patient with a sexual orientation that conflicts with their religious beliefs.
  9. Halo-Focus on one positive feature about a person or service that clouds your judgment.
    1. Example-A patient asks a pharmacist for a particular sleep aid advertised by a film star. The pharmacist cautions the patient about contraindications for that product. However, the patient chooses their originally requested sleep aid.
    2. Rationale-The patient believes that the sleep aid spokesperson is honest, just like the film characters they portray.
  10. Obesity-Tendency to negatively react to a person's obesity.
    1. Example-An obese teenager receives physical therapy (PT) for back pain. The PT report indicates that the patient is non-compliant with exercise and makes little progress due to their weight. A follow-up x-ray indicates scoliosis with 30-degree curvature of the spine.
    2. Rationale-The PT report emphasizes negative feelings about the patient's obesity rather than the patient's clinical mobility status.
  11. Racial-An automatic preference for one race over another.
    1. Example-A black adult patient with chronic neuropathy and complaint of significant leg pain x two days presents to the Emergency Department. Sobbing, the patient notes that the doctor's medicine never provides relief. The triage nurse believes the patient to be narcotic seeking and determines that they can wait to be seen.
    2. Rationale-Without completing an objective clinical assessment, the triage nurse believes that this drug-seeking behavior is not unusual because the patient is black.

Legislation to Institutionalize IB Training Across Health Professions and Care Settings

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:

  • In 2019, the State of California enacted the California Dignity in Pregnancy and Childbirth Act, the first US State to require IB training for perinatal healthcare professionals. The law also mandates State reporting requirements to track outcomes for pregnant women and hospitals and birthing centers to provide information on how patients can file discrimination complaints. Visit Source.
  • In 2021, the State of Illinois amended its mandatory child abuse and neglect reporter requirements, including healthcare professionals to complete one hour of training on IB awareness per licensure cycle beginning in 2022. Visit Source.
  • In 2021, the State of Michigan enacted landmark legislation that mandates licensed health care providers to complete regular implicit bias training to obtain or renew their licenses beginning in 2022. Visit Source.
  • In 2021, the State of New Jersey passed requirements for all health care professionals who provide perinatal treatment and care to pregnant persons at a hospital or birthing center to undergo explicit and IB training. Visit Source.

Case Study

Scenario/situation/patient description

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.

Intervention/strategies

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.

Discussion of outcomes

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.

Strengths and weaknesses of the approach used in the case

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).

Conclusion

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.

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References

  • State California Depart of Justice (2021) Attorney General Bonta Ensures California Perinatal Healthcare Facilities are Compliant with the Law Mandating Anti-Implicit Bias Training for Healthcare Providers. August 2021. (Accessed October 1, 2021). Visit Source.
  • Brecher, L., Hartman, S., McNeil S., Westby, A., Spicer, A., (2019) The EveryONE Project. American Academy of Family Physicians Center for Diversity and Health Equity Implicit Bias Facilitator's Guide. (Accessed August 25, 2021).Visit Source.
  • Centers for Disease Control and Prevention. (2021) Rapid Increase in Ivermectin Prescriptions and Reports of Severe Illness Associated with Use of Products Containing Ivermectin to Prevent or Treat COVID-19. (August 21, 2021). (Accessed October 17, 2021. Visit Source.
  • Conscious and Unconscious Biases in Healthcare. National Center for Cultural Competence, Georgetown University Center for Child and Human Development. (Accessed August 1, 2021). Visit Source.
  • FitzGerald, C., & Hurst, S. (2017). Implicit bias in healthcare professionals: a systematic review. BMC Medical Ethics, 18(19). Visit Source.
  • FitzGerald, C., Martin, A., Berner, D. et al. (2019). Interventions designed to reduce implicit prejudices and implicit stereotypes in real world contexts: a systematic review. BMC Psychol 7, 29. Visit Source.
  • Governor Gretchen Whitmer and LARA Announce Adopted Training Requirement to Improve Equity Across Michigan's Health Care System. June 2021. (Accessed October 1, 2021). Visit Source.
  • Governor Murphy Signs Legislation Requiring Maternal Health Care Professionals to Undergo Explicit and Implicit Bias Training. May 2021. (Accessed October 1, 2021). Visit Source.
  • Health Care Equity HB 158. Illinois General Assembly Public Act 102-0604. August 2021. (Accessed October 1, 2021). Visit Source.
  • Healthy People 2020. (Accessed September 1, 2021). Visit Source.
  • Howell, E.A. (2018) Reducing Disparities in Severe Maternal Morbidity and Mortality. Clin Obstet Gynecol. 2018 Jun;61(2):387-399. doi: 10.1097/GRF.0000000000000349.
  • Institute of Medicine (US) Committee on Understanding and Eliminating Racial and Ethnic Disparities in Health Care; Smedley BD, Stith AY, Nelson AR, editors. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. Washington (DC): National Academies Press (US); 2003. Visit Source.
  • Jordan, G. Understanding Implicit Bias in Health Care (2018) National Athletic Trainer Association. (Accessed July 30, 2021). Visit Source.
  • Narayan, M.C., CE: Addressing Implicit Bias in Nursing: A Review. Am J Nurs. 2019 Jul;119(7):36-43. doi: 10.1097/01.NAJ.0000569340.27659.5a. PMID: 31180913.
  • Penner, L.A., Dovidio, J.F., et al., (2016) The Effects of Oncologist Implicit Racial Bias in Racially Discordant Oncology Interactions. Journal of Clinical Oncology. 2016 34:24, 2874-2880. Visit Source.
  • Petersen, E.E., Davis, N.L., et al., (2019) Racial/Ethnic Disparities in Pregnancy-Related deaths United States, 2007-2016. MMWR Morb Mortal Wkly Rep 2019;68:762-765. Visit Source.
  • Project Implicit. (Accessed August 10, 2021). Visit Source.
  • Saluja, B., and Briant, Z. How Implicit Bias Contributes to Racial Disparities in Maternal Morbidity and Mortality in the United States. Journal of Women's Health. Vol. 30, No. 2. Published online. 2 Feb. 2021.
  • Smith, A., (2021). Medical News Today. Biases in healthcare: An overview. (Accessed September 9, 2021). Visit Source.
  • The Joint Commission. (2020). Quick safety issue 23 newsletter: Implicit bias in health care. Visit Source.