How Doctors May Unwittingly Perpetuate Health Care Disparities

Physicians and Implicit Bias: How Doctors May Unwittingly
Perpetuate Health Care Disparities
Elizabeth N. Chapman, MD1,5, Anna Kaatz, MA, MPH, PhD4
, and Molly Carnes, MD, MS1,2,3,4,5
Department of Medicine, University of Wisconsin-Madison, Madison, WI, USA; 2
Department of Psychiatry, University of Wisconsin-Madison,
Madison, USA; 3
Industrial & Systems Engineering, University of Wisconsin-Madison, Madison, USA; 4
Center for Women’s Health Research,
University of Wisconsin-Madison, Madison, USA; 5 William S. Middleton Memorial Veterans Hospital Geriatric Research Education and
Clinical Center, Madison, WI, USA.
Although the medical profession strives for equal treatment
of all patients, disparities in health care are prevalent.
Cultural stereotypes may not be consciously endorsed, but
their mere existence influences how information about an
individual is processed and leads to unintended biases in
decision-making, so called “implicit bias”. All of society is
susceptible to these biases, including physicians. Research
suggests that implicit bias may contribute to health care
disparities by shaping physician behavior and producing
differences in medical treatment along the lines of race,
ethnicity, gender or other characteristics. We review the
origins of implicit bias, cite research documenting the
existence of implicit bias among physicians, and describe
studies that demonstrate implicit bias in clinical decisionmaking. We then present the bias-reducing strategies of
consciously taking patients’ perspectives and intentionally
focusing on individual patients’ information apart from
their social group. We conclude that the contribution of
implicit bias to health care disparities could decrease if all
physicians acknowledged their susceptibility to it, and
deliberately practiced perspective-taking and individuation
when providing patient care. We further conclude that
increasing the number of African American/Black physicians could reduce the impact of implicit bias on health
care disparities because they exhibit significantly less
implicit race bias.
KEY WORDS: implicit bias; health care disparities; physicians.
J Gen Intern Med 28(11):1504–10
DOI: 10.1007/s11606-013-2441-1
© Society of General Internal Medicine 2013
At graduation, most newly minted physicians take the
Hippocratic Oath, vowing to treat all patients as respected
individuals. When they enter practice, physicians are charged
to apply principles of evidence-based medicine and meet
performance measures intended to produce uniformly highquality medical care. Despite these efforts, disparities in health
care persist. The Institute of Medicine notes that these
differences result from multiple factors, but “bias, stereotyping, [and] prejudice … on the part of the health care
providers” play a role.1 The mere existence of cultural
stereotypes about social groups (e.g. women, men, Blacks,
Whites) can influence one’s behavior toward and judgment of
individuals from that stereotyped group.2 As opposed to
explicit prejudices (e.g., believing women are not as competent surgeons as men or that men are unemotional), implicit
bias occurs without conscious awareness and is frequently at
odds with one’s personal beliefs.3 Because populations in the
U.S. that experience the greatest health disparities also suffer
from negative cultural stereotypes, implicit bias among
physicians may impact clinical decision-making in ways that
perpetuate health care disparities.4,5
Humans base their perceptions of reality on received
information and experiences reinforced until they become
automatic. This ability makes human decision-making
efficient and likely provided an evolutionary advantage.
Stereotypes are well-learned sets of associations between
some trait and a social group. A stereotype may be accurate
at a group level (e.g., Wisconsinites root for the Green Bay
Packers), but inaccurate at an individual level (i.e., some
root for the Chicago Bears). Experiments first published by
Devine demonstrated two forms of bias in a race context
that result from stereotypes.2 In this now classic work,
Devine found that White participants explicitly endorsing
prejudiced beliefs toward Blacks as well as those who
holding more egalitarian beliefs were equally able to
produce lists of cultural stereotypes about Blacks (e.g.,
rhythmic, uneducated, hostile). She also found that following exposure to stereotype-activating words, highprejudiced and low-prejudiced participants equally interpreted ambiguous behavior of a Black character in a
vignette as hostile. However, when asked to write their
thoughts about Black Americans, low-prejudiced participants produced more thoughts with themes of equality,
Received December 14, 2012
Revised February 15, 2013
Accepted March 18, 2013
Published online April 11, 2013
(e.g., “it is unfair to judge people by their color”), while
high-prejudiced participants expressed more stereotypecongruent thoughts. Together, these experiments showed
that common cultural experiences create awareness of
stereotypes, which can be automatically activated in ways
that bypass deliberate thought and influence one’s judgment
in unintended and unacknowledged ways. Devine described
these as automatic (implicit) and controlled (explicit)
aspects of prejudice.2
Since Devine’s early work, the existence of implicit bias
from the automatic activation of race, gender, ethnic, age
and other stereotypes has been demonstrated to influence
judgment of and behavior toward individuals from stereotyped groups. Implicit bias is also called “unconscious” or
“non-conscious” bias6,7 and, as noted by Devine and others,
often differs starkly from explicit beliefs.3 Multiple studies
with randomized, controlled designs confirm that simply
knowing about a stereotype distorts processing of information about individuals. Resulting outcomes include unintentionally rating identically performing Black students as
less academically capable than Whites,8,9 and evaluating
identically credentialed female applicants as less qualified
than males.10,11
Implicit bias develops early in life from repeated
reinforcement of social stereotypes. Implicit pro-White bias
occurs among children as young as 3 years old throughout
the world.12–15 In a study of children and adults, Baron et
al. found that explicit beliefs about race became more
egalitarian with age, but implicit race bias remained
unchanged.15 Implicit and explicit beliefs about other
characteristics, like age and gender, may follow similar
patterns. Despite evolution of a person’s explicit beliefs,
enduring implicit bias appears to have significant influence
on behavioral interactions with individuals from stereotyped
groups. A representative study by Dovidio et al. showed
that college students’ implicit race bias had no relationship
to self-reported egalitarian attitudes, yet predicted friendliness in interactions with a Black student.16
Physicians are not immune to implicit bias. Indeed,
uncertainty and time pressure surrounding the diagnostic
process may promote reliance on stereotypes for efficient
decision-making.17–20 Physician training emphasizes group
level information, like population risk factors, and may
expose trainees to minorities in unfavorable circumstances
of illness or addiction, reinforcing stereotypes. Finally,
physicians’ vast knowledge of scientific data may create a
strong belief in their personal objectivity, promoting bias in
The most commonly used measure of implicit bias is the
Implicit Association Test (IAT), a computerized timed dual
categorization task that measures implicit preferences by
bypassing conscious processing.22 The Black/White:
Good/Bad IAT is most frequently used to assess implicit
race bias. Its participants press different computer keys
to sort photographs of African American and Caucasian
American faces as either “Black” or “White” and then
sort words like “joy, wonderful, glorious” and “agony,
horrible, evil” into “Good” and “Bad” categories,
respectively. Next, participants repeat the task, sequentially being asked to press one key when shown a
“Black” stimulus or a “Bad” word and a different key
when shown a “White” stimulus or a “Good” word, and
vice versa. Participants must answer as quickly as
possible, increasing reliance on automatic responses.
Shorter response times for White-Good and Black-Bad
associations compared with White-Bad and Black-Good
reflect a pro-White implicit bias. The magnitude of the
difference in milliseconds correlates with the degree of
one’s implicit bias.22 In the many studies that use this
version of the IAT, the vast majority of test-takers show
pro-White bias.23–25
The IAT has been used to measure implicit bias in
physicians (Table 1). Green et al. assessed explicit and
implicit race bias among internal medicine and emergency
medicine residents7 and found significant pro-White bias
despite no explicitly reported preference for Whites over
Blacks. Participants also implicitly associated Blacks with
uncooperativeness, particularly regarding procedures.7 Although Sabin et al. found less implicit race bias among
pediatricians compared with other physicians, implicit (but
not explicit) bias was still present, linking compliance with
being White.26 Sabin et al. also examined implicit race bias
in physicians as a whole using data from Harvard’s Project
Implicit© website, which allows volunteers to take various
versions of the IAT online.27 Overall, the 2,535 website
participants who reported having an MD degree showed
significant pro-White bias.28 Others have confirmed that
even in the absence of explicit race bias, implicit preference
for Whites among physicians is common.29,30 The degree of
implicit race bias varies by physician race and gender. In
Sabin et al.’s data, the presence of pro-White bias was
significant among physicians of all racial groups except
African Americans, who were neutral, while women
showed less implicit race bias than men.28 Less—but not
zero—pro-White bias has also been found among nonWhite vs. White resident physicians and medical
Race is not the only area where physicians demonstrate implicit bias. Schwartz et al. recruited physicians
and researchers attending a conference on obesity. The
participants completed four separate IATs associating
images of obese and non-obese people with being good
or bad, motivated or lazy, smart or stupid, and valuable
or worthless. Regardless of their explicit beliefs,
JGIM Chapman et al.: Physicians and Implicit Bias 1505
participants implicitly associated obese people with
negative cultural stereotypes.33 Studies examining clinical
decision-making suggest that implicit bias in other areas,
including gender and age, may also be present.34–39
Table 1. Research Relevant to Implicit Bias and Clinical Decision-Making
Lead Author,
Type of
Studies showing implicit
bias in physicians via the
Implicit Association
Test (IAT)
Schwartz, 2003 Explicit and
Implicit negative obese bias present and did not
correlate with explicit obese bias.
Sabin, 2008 Explicit and
Despite a lack of explicit race bias, pro-White implicit
bias present, but did not correlate with responses to
Sabin, 2009 Implicit Black
Significant pro-White bias present, regardless of race,
but Black physicians had a less than non-Black
physicians, and female physicians had less than male
Explicit and
Medical students had significant pro-White implicit
bias, throughout training. Black and biracial students
tended to have less than White, Hispanic and Asian
Studies linking IATmeasured bias with clinical
decision-making or patient
Cooper, 2012 Implicit Black
Implicit pro-White bias and an association of Whites
with compliance found. Increased provider implicit
bias associated with physician verbal dominance and
less positive perceptions of interactions by Black
Green, 2007 Explicit and
No explicit race bias but significant pro-White implicit
bias found. More pro-White bias associated with lower
referral rates of Black vignette patient for
thrombolysis. Participants aware of study intent more
likely to treat patients similarly, regardless of their
implicit race bias.
Penner, 2010 Explicit and
Physicians with low explicit but high implicit race bias
were rated more poorly by Black patients and had
lower rates of patient satisfaction than those with low
explicit and implicit bias. Physicians with both high
explicit and high implicit biases were not rated as
poorly as those with low explicit and high implicit biases.
Sabin, 2012 Explicit and
Pro-White bias present; participants implicitly
associated Black patients with non-adherence despite
absent explicit biases. Implicit pro-White bias
associated with providing opioids less for Black
children with postoperative pain.
Vignette studies suggesting
the presence of implicit
Borkhoff, 2008 None Women Despite being otherwise identical, male patients referred
for TKA significantly more often than females, even
when adjusting for covariates.
Chapman, 2001 None Women Physicians are more likely to diagnose male patients
with COPD when provided with history and
examination alone. Providing PFTs consistent with
COPD mitigated this difference.
Madan, 2001
and 2006
None Elderly
Breast conservation therapy recommended significantly
more frequently for younger vignette patients, even
after adjusting for covariates.
Reuben, 1995 Explicit Elderly
Varying degrees of explicit bias about elderly patients
present. Elderly patients were treated significantly less
aggressively than young patients
Uncapher, 2000 None Elderly
Depression and suicidality recognized in both elderly and
younger patients; treatment offered to elderly less and
providers less optimistic regarding treatment benefit.
Real clinical scenarios
where bias is suggested
Drwekci, 2011 None Black
Nurses asked to take the patient’s perspective offered
equal pain treatment, regardless of patient race,
whereas those not asked to do so recommended more
pain medication for White patients.
Todd, 1993 None Hispanic
Hispanic patients significantly more likely not to receive
analgesia in the ER compared to non-Hispanic
patients, even when adjusting for confounders
Todd, 1994 None Hispanic
Hispanic patients and non-Hispanic patients did not
differ significantly in pain ratings, and there was no
difference in physician–patient disparity in pain ratings
for Hispanic patients vs. non-Hispanic patients
Todd, 2000 None Black
Patients had similar pain ratings regardless of race, yet
Black patients were significantly less likely to receive
pain medication, even after adjusting for covariates
COPD chronic obstructive pulmonary disease; IAT implicit association test; PFT pulmonary function test; TKA total knee arthroplasty
1506 Chapman et al.: Physicians and Implicit Bias JGIM
Demonstrating that physicians have measurable implicit bias
does not prove that this bias affects patient–doctor interactions
or alters the treatment patients receive. However, research
supports a relationship between patient care and physician bias
in ways that could perpetuate health care disparities.4 In a
qualitative study of patient–doctor communication, Cooper et
al. found that physicians’ implicit pro-White bias on the IAT
correlated with Black patients’ perceptions of poorer communication and lower quality care.30 Penner et al. found that
Black patients were less satisfied with physicians who had low
explicit but high implicit race bias, rating them as less warm,
friendly, and team-oriented compared to physicians with equal
degrees of implicit and explicit bias.32 The small number of
family medicine residents in this study showed less implicit
bias than physicians in other studies, possibly because most of
them were born outside the U.S. and were caring only for nonWhite patients. Dovidio et al. also found that the perception of
an interaction between White physicians and Black patients
was affected by a physician’s implicit race bias, even in the
absence of explicit biases.16 Such negative perceptions could
alter their behavior in ways that reduce adherence, return for
follow-up, or trust and thus contribute to disparities in care.
Perceptions of poorer care or communication alone may
not directly alter the quality of treatment provided, but
research also supports a link between disparate treatment
decisions and implicit provider bias. This research exists in
two forms: studies comparing treatment recommendations
for patients who are identical except for social category
information, and studies directly measuring implicit bias
and then determining the correlation between measured bias
and physicians’ decisions. The former studies assume
providers are not intentionally acting with any explicit bias,
while the latter group quantifies explicit and implicit bias.
Two studies found that Black patients seen in emergency
departments receive less analgesia than White patients.40,41
Hispanic patients in one study were seven times less likely
to receive opioids in the emergency room than nonHispanic patients with similar injuries, even when adjusting
for confounders.42 These findings were duplicated in Black
patients.43 In a follow-up study, researchers assessed
physicians’ ability to quantifying pain in Hispanic patients
compared to non-Hispanic patients. They found that
physicians could accurately judge patients’ pain severity
regardless of ethnicity yet still provided less analgesia to
Hispanic patients with severe injuries.44 Even in the
absence of direct measurement of implicit bias, these
compelling data suggest that physicians make treatment
decisions that divide patients with similar clinical presentations along lines of race or ethnicity.
Sabin et al. measured implicit race bias in pediatricians
who were then provided with identical vignettes randomly
identifying a patient as either Black or White. Providers
who had a high degree of implicit pro-White bias were
significantly more likely to disagree with the standard of
care—providing opioid analgesics to the patient—than those
with a low degree of bias, while explicit race attitudes had no
bearing on treatment.29 Green et al. demonstrated that
providers’ implicit attitudes about race may contribute to the
disparity in care of patients with acute coronary syndrome,7
where Black patients are less likely than Whites to receive
appropriate therapies.45–49 Internal medicine and emergency
medicine residents took the Black/White:Good/Bad IAT and
also answered questions about a vignette of a patient with
chest pain randomly designated as Black or White. Residents
with greater implicit bias were significantly less likely to
recommend thrombolysis for Black patients than those with
less bias. Again, implicit bias measured in this study was
associated with different treatment of theoretical patients that
corresponds with a real-life health care disparity.
Implicit gender bias among physicians may also unknowingly sway treatment decisions. Women are three times less
likely than men to receive knee arthroplasty when clinically
appropriate. Although physicians denied that patient gender
influenced decisions to refer patients for the procedure,50–52 a
study by Borkhoff et al. challenged this. In the study, orthopedic
surgeons and family practitioners received vignettes featuring a
patient with moderate unilateral knee pain and a radiograph
revealing osteoarthritis. Identical vignettes were randomly
ascribed to a female or male patient. The statistically significant
odds ratio for referring the male vs. female patient for
arthroplasty was 22.1 for orthopedic surgeons and 2.21 for
family practitioners.38 Implicit assumptions based on stereotypes that men are more stoic than women or more likely to
engage in rigorous activities that would benefit from joint
replacement may contribute to the disparity and adversely
influence the care of individual female patients.38,51,52
Implicit stereotype-based bias may also contribute to gender
differences in the diagnosis of chronic obstructive pulmonary
disease (COPD), even in the face of near comparable smoking
rates between men and women and women’s increased
susceptibility to the disease. Chapman et al.39 created a clinical
vignette of a middle-aged patient presenting with a chronic
cough and a smoking history. All vignettes were identical
except for the randomly assigned patient gender. Female
patients were more likely to receive a diagnosis of asthma or
a non-respiratory problem, while identical male patients were
more likely to be diagnosed with COPD.39 Implicit bias was not
measured, but assumptions that women are less likely to smoke
and more apt to manifest anxiety as respiratory complaints may
have played a role in this diagnostic disparity.
With growing evidence that implicit bias in physician
decision-making makes a significant contribution to perpetJGIM Chapman et al.: Physicians and Implicit Bias 1507
uating health care disparities, it is critical to find ways to
reduce its impact. Conceptualizing implicit bias as a “habit
of mind” provides a useful framework for developing
interventions.53–55 This approach allows mobilization of a
large body of work on facilitating intentional behavioral
change.53 As with any behavioral change, individuals need
to become aware of their habitual engagement in an
undesirable behavior and be provided with strategies to
increase self-efficacy to engage in a new desirable behavior.56 The study by Green et al. provides an example of how
simply increasing physicians’ awareness of their susceptibility to implicit bias changes behavior.7 A subset of
participants who were aware that bias was a focus of the
investigation were significantly more likely to recommend
thrombolysis for Black patients, even if they had a high
degree of implicit pro-White bias.7
Although awareness is important, as with clinical efforts
to change patients’ undesirable health behaviors,57 it is not
sufficient to reduce the automatic, habitual activation of
stereotypes and the subsequent impact of implicit bias in
medical decision-making. One strategy that appears effective in reducing implicit bias is individuating. Individuating
involves conscious effort to focus on specific information
about an individual, making it more salient in decisionmaking than that person’s social category information (e.g.
race or gender). Lebrecht et al. tasked White participants to
indicate whether they had previously seen a Black face
presented to them. Those trained to differentiate Black faces
were more accurate than untrained participants, and their
accuracy correlated with reduced implicit race bias.58
Chapman et al.’s study of gender disparities in COPD
diagnoses supports the benefit of individuation in reducing
the influence of implicit bias in medical practice. In the
study, the initial gender differences in diagnosis were
eliminated when physicians were provided with spirometry
data consistent with COPD.39 Specific, individuated patient
information prevents physicians from filling in partial
information with stereotype-based assumptions. Unfortunately, medicine is often practiced under time-pressured
circumstances with incomplete information.
Another strategy to mitigate the impact of implicit bias is
perspective-taking. Originally described by Galinsky et
al.,59 this is a conscious attempt to envision another
person’s viewpoint and can reduce implicit bias in social
interactions.59,60 Drwecki et al. applied perspective-taking
to a clinical setting. In this study, nurses were shown
pictures of either White or Black patients with genuine
expressions of pain and asked how much pain medication
they recommended. Nurses told to use their best judgment
recommended significantly more pain medication for White
than Black patients, whereas nurses instructed to imagine
how the patient felt recommended equal analgesic treatment
regardless of race.61 It may thus be possible to reduce
disparities in pain treatment if providers actively attempt to
take patients’ perspectives. In addition to behavioral strategies
aimed at individual physicians, the contribution of implicit
race bias to health care disparities could be reduced on a
population level by increasing the number of African
American/Black physicians because they consistently demonstrate less race bias.7,28,31 Certainly society is not divided
along lines of race, gender, age or weight alone, and the impact
of implicit social bias is likely far more complex than available
data can capture. Nonetheless, individuating and perspectivetaking are bias-mitigating strategies that should be effective
regardless of a patient’s social group.
Despite the best intentions of physicians to provide equal
treatment to all, disparities linger and may lead to
unacceptable increases in morbidity and mortality for some.
Many factors have helped create these disparities, including
implicit bias, an unintentional, unacknowledged preference
for one group over another. Implicit bias is present in
physicians and correlates with unequal treatment of
patients. We suggest the contribution of implicit bias to
health care disparities could be reduced if all physicians
acknowledged their susceptibility to such bias and deliberately practiced perspective-taking and individuation. Additionally, increasing the number of African American/Black
physicians could reduce the impact of implicit bias on some
health care disparities because they exhibit significantly less
implicit race bias. Although challenging, these strategies
may help create a practice of medicine that embodies the
ideals and guiding principles that attract physicians to the
Acknowledgements: Dr. Chapman is a Geriatric Fellow at the
Madison GRECC and Dr. Kaatz is a postdoctoral fellow at the Center
for Women’s Health Research. This is GRECC Manuscript No. 2013-
14. Dr. Carnes’s research on implicit bias is supported by NIH grant
no. R01 GM088477 and DP4 GM096822.
Conflicts of Interest: The authors declare that they do not have a
conflict of interest.
Corresponding Author: Elizabeth N. Chapman, MD; William S.
Middleton Memorial Veterans Hospital Geriatric Research Education
and Clinical Center, Madison VA GRECC, 2500 Overlook Terrace,
Madison, WI 53705, USA (e-mail: [email protected]).
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