Implicit bias in healthcare professionals: a systematic review
FitzGerald and Hurst BMC Medical Ethics XXXXXXXXXX:19
DOI XXXXXXXXXX/s XXXXXXXXXX
RESEARCH ARTICLE Open Access
Implicit bias in healthcare professionals:
a systematic review
Chloë FitzGerald* and Samia Hurst
Abstract
Background: Implicit biases involve associations outside conscious awareness that lead to a negative evaluation of
a person on the basis of i
elevant characteristics such as race or gender. This review examines the evidence that
healthcare professionals display implicit biases towards patients.
Methods: PubMed, PsychINFO, PsychARTICLE and CINAHL were searched for peer-reviewed articles published
etween 1st March 2003 and 31st March 2013. Two reviewers assessed the eligibility of the identified papers based on
precise content and quality criteria. The references of eligible papers were examined to identify further eligible studies.
Results: Forty two articles were identified as eligible. Seventeen used an implicit measure (Implicit Association Test
in fifteen and subliminal priming in two), to test the biases of healthcare professionals. Twenty five articles
employed a between-subjects design, using vignettes to examine the influence of patient characteristics on
healthcare professionals’ attitudes, diagnoses, and treatment decisions. The second method was included
although it does not isolate implicit attitudes because it is recognised by psychologists who specialise in implicit
cognition as a way of detecting the possible presence of implicit bias. Twenty seven studies examined racial
ethnic biases; ten other biases were investigated, including gender, age and weight. Thirty five articles found
evidence of implicit bias in healthcare professionals; all the studies that investigated co
elations found a significant
positive relationship between level of implicit bias and lower quality of care.
Discussion: The evidence indicates that healthcare professionals exhibit the same levels of implicit bias as the wide
population. The interactions between multiple patient characteristics and between healthcare professional and patient
characteristics reveal the complexity of the phenomenon of implicit bias and its influence on clinician-patient
interaction. The most convincing studies from our review are those that combine the IAT and a method measuring the
quality of treatment in the actual world. Co
elational evidence indicates that biases are likely to influence diagnosis
and treatment decisions and levels of care in some circumstances and need to be further investigated. Our review also
indicates that there may sometimes be a gap between the norm of impartiality and the extent to which it is em
aced
y healthcare professionals for some of the tested characteristics.
Conclusions: Our findings highlight the need for the healthcare profession to address the role of implicit biases in
disparities in healthcare. More research in actual care settings and a greater homogeneity in methods employed to test
implicit biases in healthcare is needed.
Keywords: Implicit bias, Prejudice, Stereotyping, Attitudes of health personnel, Healthcare disparities
* Co
espondence: XXXXXXXXXX
Institute for Ethics, History, and the Humanities, Faculty of Medicine
University of Geneva, Genève, Switzerland
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FitzGerald and Hurst BMC Medical Ethics XXXXXXXXXX:19 Page 2 of 18
Background
A patient should not expect to receive a lower standard
of care because of her race, age or any other i
elevant
characteristic. However, implicit associations (uncon-
scious, uncontrollable, or arational processes) may influ-
ence our judgements resulting in bias. Implicit biases
occur between a group or category attribute, such as being
lack, and a negative evaluation (implicit prejudice) o
another category attribute, such as being violent (implicit
stereotype) [1].1 In addition to affecting judgements, im-
plicit biases manifest in our non-ve
al behaviour to-
wards others, such as frequency of eye contact and
physical proximity. Implicit biases explain a potential
dissociation between what a person explicitly believes
and wants to do (e.g. treat everyone equally) and the
hidden influence of negative implicit associations on
her thoughts and action (e.g. perceiving a black patient
as less competent and thus deciding not to prescribe
the patient a medication).
The term ‘bias’ is typically used to refer to both implicit
stereotypes and prejudices and raises serious concerns in
healthcare. Psychologists often define bias
oadly; such as
‘the negative evaluation of one group and its members
elative to another’ [2]. Another way to define bias is to
stipulate that an implicit association represents a bias
only when likely to have a negative impact on an
already disadvantaged group; e.g. if someone associates
young girls with dolls, this would count as a bias. It is
not itself a negative evaluation, but it supports an image
of femininity that may prevent girls from excelling in areas
traditionally considered ‘masculine’ such as mathematics
[3]. Another option is to stipulate that biases are not
inherently bad, but only to be avoided when they incline
us away from the truth [4].
In healthcare, we need to think carefully about exactly
what is meant by bias. To fulfil the goal of delivering
impartial care, healthcare professionals should be wary
of any kind of negative evaluation they make that is
linked to membership of a group or to a particular char-
acteristic. The psychologists’ definition of bias thus may
e adequate for the case of implicit prejudice; there are
unlikely, in the context of healthcare, to be any justified
easons for negative evaluations related to group mem-
ership. The case of implicit stereotypes differs slightly
ecause stereotypes can be damaging even when they
are not negative per se. At least at a theoretical level,
there is a difference between an implicit stereotype that
leads to a distorted judgement and a legitimate associ-
ation that co
ectly tracks real world statistical informa-
tion. Here, the other definitions of bias presented above
may prove more useful.
The majority of people tested from all over the world
and within a wide range of demographics show re-
sponses to the most widely used test of implicit
attitudes, the Implicit Association Test (IAT), that indi-
cate a level of implicit anti-black bias [5]. Other biases
tested include gender, ethnicity, nationality and sexual
orientation; there is evidence that these implicit attitudes
are widespread among the population worldwide and
influence behaviour outside the laboratory [6, 7]. Fo
instance, one widely cited study found that simply chan-
ging names from white-sounding ones to black-sounding
ones on CVs in the US had a negative effect on callbacks
[8]. Implicit bias was suspected to be the culprit, and a
eplication of the study in Sweden, using Arab-sounding
names instead of Swedish-sounding names, did in fact
find a co
elation between the HR professionals who
prefe
ed the CVs with Swedish-sounding names and a
higher level of implicit bias towards Arabs [9].
We may consciously reject negative images and ideas
associated with disadvantaged groups (and may belong
to these groups ourselves), but we have all been
immersed in cultures where these groups are constantly
depicted in stereotyped and pejorative ways. Hence the
description of ‘aversive racists’: those who explicitly
eject racist ideas, but who are found to have implicit
ace bias when they take a race IAT [10]. Although there
is cu
ently a lack of understanding of the exact mech-
anism by which cultural immersion translates into impli-
cit stereotypes and prejudices, the widespread presence
of these biases in egalitarian-minded individuals suggests
that culture has more influence than many previously
thought.
The implicit biases of concern to health care profes-
sionals are those that operate to the disadvantage of
those who are already vulnerable. Examples include
minority ethnic populations, immigrants, the poor, low
health-literacy individuals, sexual minorities, children,
women, the elderly, the mentally ill, the overweight and
the disabled, but anyone may be rendered vulnerable
given a certain context [11]. The vulnerable in health-
care are typically members of groups who are already
disadvantaged on many levels. Work in political philoso-
phy, such as the De-Shalit and Wolff concept of ‘co
o-
sive disadvantage’, a disadvantage that is likely to lead to
further disadvantages, is relevant here [12]. For instance,
if a person is poor and constantly wo
ied about making
ends meet, this is a disadvantage in itself, but can be cor-
osive when it leads to further disadvantages. In a coun-
try such as Switzerland, where private health insurance
is mandatory and yearly premiums can be lowered by in-
creasing the deductible, a high deductible may lead such
a person to refrain from visiting a physician because of
the potential cost incu
ed. This, in turn, could mean
that the diagnosis of a serious illness is delayed leading
to poorer health. In this case, being poor is a co
osive
disadvantage because it leads to a further disadvantage
of poor health.
FitzGerald and Hurst BMC Medical Ethics XXXXXXXXXX:19 Page 3 of 18
The presence of implicit biases among healthcare pro-
fessionals and the effect on quality of clinical care is a
cause for concern [13–15]. In the US, racial healthcare
disparities are widely documented and implicit race bias
is one possible cause. Two excellent literature reviews
on the issue of implicit bias in healthcare have recently
een published [16, 17]. One is a na
ative review that
selects the most significant recent studies to provide a
helpful overall picture of the cu
ent state of the re-
search in healthcare on implicit bias [16]. The other is a
systematic review that focusses solely on racial bias and
thus captures only studies conducted in the US, where
ace is the most prominent issue [17]. Our review differs
from the first because it poses a specific question, is sys-
tematic in its collection of studies, and includes an
examination of studies solely employing the vignette
method. Its systematic method lends weight to the
evidence it provides and its inclusion of the vignette
method enables it to compare two different literatures
on bias in healthcare. It differs from the second because
it includes all types of bias, not only racial; partly as a
consequence, it captures many studies conducted out-
side the US. It is important to include studies conducted
in non-US countries because race understood as white
lack is not the source of the most potentially harmful
stereotypes and disparities in all cultural contexts. Fo
example, a recent vignette study in Switzerland found
that in the German-speaking part of the country, physi-
cians displayed negative bias in treatment decisions
towards fictional Se
ian patients (skin colour was un-
specified, but it would typically be assumed to be white),
ut no significant negative bias towards fictional patients
from Ghana (skin colour would be assumed to be black)
[18]. In the Swiss German context, the issue of skin
colour may thus be less significant for potential bias
than that of country of origin.2
Methods
Data sources and search strategy
Our research question was: do trained healthcare profes-
sionals display