[Researchers are finding new ways to mitigate implicit bias in
health care providers]
[[link removed]]

SCIENCE SUNDAY: DO NO UNCONSCIOUS HARM  
[[link removed]]


 

Rodrigo Pérez Ortega
March 2, 2023
Science
[[link removed]]


*
[[link removed]]
*
[[link removed]]
*
*
[[link removed]]

_ Researchers are finding new ways to mitigate implicit bias in
health care providers _

, Thumy Phan

 

Before being diagnosed with an autoimmune disease several years ago,
Linda Chastine says she was skinny. But the lifesaving steroid
treatment for her condition made the Seattle community organizer
[[link removed]] gain roughly 45 kilograms in 1 year. Yet
her doctors often fail to recognize the relationship between the
autoimmune condition, her medication, and the weight gain. Instead
they routinely bring up being overweight as her main problem, she
says, and suggest she count calories. “It’s a very traumatic
experience to have a doctor dismiss a lot of your complaints or
concerns based on weight,” Chastine says.

Stories like Chastine’s are unfortunately common, say researchers
who examine how implicit biases—unconscious assumptions based on
skin color, gender, sexual preference, or appearance—in health care
providers affect patient care. Chastine, who is Black and queer, is
now channeling her troubled experience with the medical establishment
to aid studies of implicit bias and identify ways to counter it. She
is part of a 5-year collaboration between various departments at both
the University of Washington (UW) and the University of California,
San Diego (UCSD), in which a team is developing a tool to give
physicians feedback in real time during patient visits—or shortly
after—on what they can do to mitigate their unconscious prejudices.

That project, called UnBIASED
[[link removed]] (Understanding Biased patient-provider
Interaction and Supporting Enhanced Discourse), is at the leading edge
of a wave of efforts to counter the negative effects of bias in
medicine. From creating new models of education and training, to
developing accurate tests to objectively measure pain, scientists are
working to provide health care workers—and institutions—with the
tools to diminish bias and provide equitable care. Although it might
be too early to know whether these interventions are successful and
long-lasting, some strategies appear promising.

“We’re just looking at one little slice” of how to eradicate
implicit bias in health care, says Andrea Hartzler, the UW biomedical
informatician who leads UnBIASED, which is funded by the National
Library of Medicine. “It’s going to take a tool chest of all kinds
of different interventions.” One key component, she adds, is to go
beyond the individual doctor or nurse and target structural issues
within their institutions that promote these biases.

EVERYONE HAS prejudices that affect how they perceive and behave with
others. And although many people might be aware of some—their
explicit or conscious biases—and intentionally try to compensate for
them, other hidden ones still lurk and can influence attitudes and
interactions.

These implicit biases are widespread among health care providers, as
Janice Sabin discovered in the late 2000s. In her research back then
—as a social welfare Ph.D. student at UW—Sabin had asked 95
doctors from the Department of Pediatrics at Seattle Children’s
Hospital to take a test that would determine whether they had a
“hidden” bias toward a certain race. “I was terrified,” Sabin,
now a biomedical informatics professor at UW, recalls. “This
wasn’t just asking them questions about bias and racism, this was
actually going into their mind.”

Sabin used the well-known Implicit Association Test (IAT),
which determines how strongly an individual associates a trait
[[link removed]]—such as race
or sexual orientation—with a subjective value, such as “good” or
“bad.” The quicker you match each concept to a subjective value,
the greater the association and the higher your score, which broadly
indicates a stronger implicit association between the trait and value.

Sabin found the doctors she tested—a few of them nonwhite—had a
clear, unconscious preference for white people
[[link removed]] over
Black people. It was one of the first studies showing health care
providers had unintentional racial prejudices. “It was kind of scary
because this was a concept completely foreign to [many] people at the
time,” Sabin says.

The IAT remains a standard tool for measuring implicit bias, although
some have criticized it because it has to be taken several times to
reveal a reliable result, as people’s scores could change every time
they take it. Even when people come out neutral on race, most studies
will reveal some kind of unconscious prejudice, such as an
unrecognized preference of certain sexual orientations or religions.
“We all have some kind of hidden bias,” Sabin says. Not only are
these biases present among health care providers, but research
suggests they are likely affecting diagnoses and treatment decisions,
and in turn contributing to health disparities affecting people of
color, women, and members of the LGBTQ+
[[link removed]] community
and other historically marginalized groups.

Thumy Phan

A 2020 study by Rachel Hardeman, a reproductive health equity
researcher at the University of Minnesota’s Center for Antiracism
Research for Health Equity, and colleagues showed Black newborns are
twice as likely to die in the care of a white physician than a Black
doctor [[link removed]], for
instance. Another study from 2022 found women and people of color
with chest pain wait longer
[[link removed]] to be
treated in the emergency room compared with white men.

Pain assessment by health care providers has become a fertile ground
for research into unconscious medical prejudices and a classic example
of the way bias undermines the care of minorities. Implicit bias often
shows up when there’s no objective test or measurement for a
symptom, and that’s the case for pain. “Pain is subjective and how
people overtly show signs of pain varies across different cultures,
across gender identities,” says Kristyn Smith, an emergency
physician at the University of Pennsylvania.

Previous studies have shown physicians tend to underestimate pain
experienced by women and people of color and discount their
complaints. In mock medical cases, reported in 2016 in
the _Proceedings of the National Academy of Sciences_, white medical
residents estimated Black patients felt less pain
[[link removed]] than white
ones, and as a result, made less effective treatment recommendations
for Black people. But this bias isn’t limited to doctors: A study
from 2021 showed lay people underestimate the pain in women patients
[[link removed](21)00035-3/fulltext] compared
with men patients, opting to treat women with psychotherapy and men
with painkillers.

“My personal experience is that it’s not something that is due to
people necessarily having [overt] prejudices or wanting to do a bad
job,” says Indiana University, Indianapolis, psychiatrist Alexander
Niculescu III. “It’s just that [when] lacking objective tools,
sometimes people can make assumptions that are [going] in the wrong
direction.”

Implicit biases can also affect health outcomes simply because
patients feel discriminated against and don’t come back to their
doctor. A 2017 systematic review of research on implicit bias in
health care providers
[[link removed]] examined
42 published studies conducted mainly in the United States but also in
nine other countries, and concluded there was “a significant
positive relationship between level of implicit bias and lower quality
of care.”

Many other inequities, such as poverty and redlining, prevent certain
groups from accessing good health care, Smith says. “If you include
the biases of your health care professionals, then that creates a
perfect storm for health care disparities to continue.”

SCIENTISTS HAVE long studied several kinds of interventions that
attempt to “erase” implicit bias, but few of them have shown
lasting effects. “There is a robust science around implicit bias,”
Hardeman says. But, “There is no gold standard for how to intervene
right now. It’s imprinted in our brains in ways that make it really
hard.”

Simple interventions can dampen biases, as measured by successive
IATs, but the changes are usually modest and don’t persist. In a
2001 experiment, for example, researchers showed images of admired
Black people—such as Denzel Washington or Colin Powell—versus
disliked white individuals—such as Jeffrey Dahmer and Howard
Stern—to study participants, and saw that this
exposure significantly weakened a pro-white preference for 24 hours
[[link removed]], but not for much
longer.

Simply asking health care providers to take the IAT without providing
context or tools can be counterproductive. A study in 2015 indicated
that when medical students are told about their unconscious bias
without direction on overcoming it, they tend to get anxious,
confused, and nervous
[[link removed]] interacting with
patients who belong to social groups different from their own.
That’s why even a quick training on skills to mitigate implicit bias
can go a long way, according to Hardeman.

There is a robust science around implicit bias. [But] there is no gold
standard for how to intervene right now.

Rachel Hardeman, University of Minnesota’s Center for Antiracism
Research for Health Equity

So, Hartzler and others are developing feedback tools that will help
clinicians confront and make sense of their biases. The team conducted
interviews with a small group of primary care doctors to get a sense
of the best way to provide feedback to providers on implicit bias. The
researchers also spoke with traditionally marginalized groups
[[link removed]], including
people of color and LGBTQ+ individuals, to learn about biased
behaviors that may be on display in patient-doctor interactions. The
UnBIASED team then recruited physicians and “community champions,”
including Chastine, to help design culturally sensitive experiments
that could reveal personal biases. “It’s really great to have all
these minds that want to be innovative about how we are addressing
bias,” Chastine says.

Brian Wood, an infectious disease clinician with UW and Harborview
Medical Center, is one such physician volunteer with UnBIASED. Wood,
who primarily sees people with an HIV diagnosis, says his physicians
group serves a diverse population who often feel stigmatized by
doctors. “I often hear from Black patients how they feel
discriminated against,” says Wood, who is white. “And my
transgender patients often express how fearful they are of seeking
care from any provider they don’t know,” because of painful past
encounters with the medical establishment, he says.

That made him eager to take part in UnBIASED’s first experiments,
which rely on cameras installed in exam rooms. The cameras in Wood’s
Seattle clinic captured interactions between him and his patients,
including close-ups of his and their facial features and body
language. “I found quite quickly that the patient and I both forgot
the cameras were there and just fell into our usual routine and
conversation,” he says.

The UnBIASED team then used a type of artificial intelligence (AI)
known as machine learning to analyze patterns in the recordings and
identify nonverbal cues that could indicate implicit bias. In one of
the clips Wood was later shown, he was talking with a patient while
leaning forward with his arms crossed on the desk, body language he
worries may have made him seem closed and unapproachable. “I
reflected on my own as to how that body language might be felt and
perceived by the patient,” he says. Wood, who hopes to improve his
demeanor, says he welcomed such feedback and is eager for more.

“Reflecting on possible negative moments during a visit was not
easy, but felt important and valuable,” Wood says.

Thumy Phan

The team is now working on translating the experiment’s results into
feedback like “digital nudges”—such as an icon that pops onto
the computer screen, a wearable device, or other mechanism telling
physicians to interrupt patients less or look them in the eye more
often. But the UnBIASED team still has challenges interpreting the
data in the recordings. For instance, nonverbal signals are nuanced,
Hartzler says. “It’s not always as simple as ‘more interruptions
means bad.’”

Others using computer software to research implicit bias in medicine
are also struggling to give physicians meaningful feedback. Nao
Hagiwara, a social and health psychologist at Virginia Commonwealth
University, and her team are analyzing dozens of nonverbal and verbal
communication behaviors
[[link removed]], such as
facial expressions and voice changes, in recordings of primary care
physicians’ interactions with people who have type 2 diabetes. Their
software hasn’t yet identified behaviors that could clearly be
linked to bias or had an adverse effect in the patient’s outcome.
One reason for this murkiness, Hagiwara suggests, is that multiple
different cues likely interact to influence patient outcomes whereas
studies so far tend to analyze the impact of only one behavior at a
time.

Smith is working on a different type of implicit bias intervention:
creating clinical simulations from her time working in a trauma center
that served a majority Black population. Such emergency departments,
where doctors and nurses are often overworked and in a high-stress
environment, are ripe for implicit bias to kick in easily, Smith says.
She recalls an instance when a Black man arrived at the hospital with
a gunshot wound. He died, and shortly after, a social worker came up
to her and said, “He was always here … he was just shot 2 weeks
ago. … I wonder what he did this time.”

Smith recalls that the social worker was normally a fierce advocate
for patients, but says that comment was steeped in bias because it
suggested the gunshot victim had done something wrong and deserved his
fate—an attitude that could affect care. But a gunshot victims’
actions should not affect their care, Smith stresses. “Patients who
get shot are victims first, and deserve to be treated with respect and
sensitivity.” (The social worker was very remorseful after Smith
told her that her comment was inappropriate.)

Using this experience, Smith is now developing a series of training
exercises
[[link removed]] for
residents, attending physicians, and nurses, among others, where
seasoned health care workers play out scripted clinical scenarios
depicting microaggressions stemming from implicit bias—everyday
putdowns, insults, or slights that minoritized populations
face—while newer members of the medical establishment watch on.
Health care workers witness microaggressions toward patients all the
time, Smith says, but nobody teaches providers how to address the
offenses. And so, after the simulations, Smith discusses the skits
with these new practitioners and provides them with strategies to take
action, such as documenting an inappropriate remark, when they see
microaggressions in their workspace.

Sabin has also developed a 40-minute educational online course for
medical school faculty across the country on how to manage bias. It
includes a brief history of racism in medicine as well as advice on
collecting data to identify inequities in care. Those who took the
course not only increased their recognition of bias, but this
awareness lasted for at least 1 year
[[link removed]],
Sabin and colleagues reported last year in a peer-reviewed
publication. The participants credited the contents of the course for
improving their teaching and their clinical practice. Sabin hopes the
training can help these physicians be more thoughtful and mindful
about avoiding stereotypes when filling patient charts, for example.

Thumy Phan

Niculescu and his team are addressing implicit bias from a different
angle. They are trying to eliminate a central subjectivity in medical
care by developing a blood test for biomarkers that reflect a
person’s level of pain. Objectively measuring pain “removes
stigma, because people might be underappreciating your degree of pain
or suffering,” he says. “And blood biomarkers show that there is
something biological going on. It’s not something that you’re just
making up.”

So far, the researchers have focused on identifying RNAs in blood that
show the activity of specific genes and could indicate the level of
someone’s pain. Their preliminary results suggest the expression of
the gene for a molecule called microfibril associated protein 3
(MFAP3) is lower when research participants are experiencing severe
pain [[link removed]], and low
activity in the gene is also a good predictor of future emergency room
visits. MFAP3 hadn’t previously been connected to pain and the group
suggests it may normally have a pain suppression function.

However, validating biomarkers takes extensive replication studies in
multiple, large populations. And even proven biomarker tests are
usually not cheap, which makes them unlikely to be a reality anytime
soon, especially for the marginalized populations that could benefit
from their rollout. But Niculescu remains optimistic. “The medicine
of the future hopefully will be equitable and not biased, and
everybody will have access to these things,” he predicts.

NONE OF THESE solutions on its own will eliminate implicit bias in
medicine, researchers say. “We’re not going to solve this
tomorrow,” says Charles Goldberg, an internal medicine physician at
UCSD, who is also involved with the UnBIASED team. “But tomorrow is
going to be better than today largely, and next month will be better
than this month. And in a year, we’re going to be moving along.”

Getting buy-in from whole health care systems could accelerate the
process. Recently, California, Michigan, Maryland, Minnesota, and
Washington state passed legislation mandating implicit bias training
for the medical professionals
[[link removed]] they
license. And since June 2022, Massachusetts physicians are required
to take implicit bias training
[[link removed]] to
get a new license or get recertified to practice.

Although researchers see this as a good step, they worry mandated
training will become a one-off box-checking exercise. Sustained
implicit bias training for physicians should instead be the norm, some
emphasize. Hospitals also need to monitor and collect data on health
care outcomes for different groups in order to monitor equity, Sabin
says. “You have to know where the disparities lie and then begin to
work backwards from that.”

It won’t be easy, Hardeman says, noting that, at least in the United
States, centuries of white supremacy and other forms of bigotry have
resulted in deep-rooted stereotypes and other implicit biases.
“Every single person should be thinking about doing this work,”
she says. “But if they’re doing it within a system that hasn’t
addressed its own biases and racism, then it’s not going to be fully
effective.”

_RODRIGO PÉREZ ORTEGA is a science journalist covering life sciences,
medicine, health, and academia._

_Look to the future with SCIENCE.
The discoveries and innovations that you read about in Science are
destined to change the world as we know it. Join AAAS to access
exclusive benefits._

_JOIN AAAS NOW [[link removed]]  Be an
advocate. Advance the world of science.
By joining AAAS, you're strengthening the voice of science. Your
membership means uniting STEM professionals and enthusiasts across the
globe to support new discovery, innovation, and science education._

* Science
[[link removed]]
* Medicine
[[link removed]]
* Health Care
[[link removed]]
* bias
[[link removed]]
* prejudice
[[link removed]]

*
[[link removed]]
*
[[link removed]]
*
*
[[link removed]]

 

 

 

INTERPRET THE WORLD AND CHANGE IT

 

 

Submit via web
[[link removed]]

Submit via email
Frequently asked questions
[[link removed]]

Manage subscription
[[link removed]]

Visit xxxxxx.org
[[link removed]]

Twitter [[link removed]]

Facebook [[link removed]]

 




[link removed]

To unsubscribe, click the following link:
[link removed]